XSEDE: the Extreme Science and Engineering Discovery Environment

XSEDE: The Extreme Science and Engineering Discovery Environment

Interim Project Report 8: Report Year 3, Reporting Period 3 November 1, 2018 – January 31, 2019

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XSEDE Senior Management Team (SMT) John Towns (NCSA) PI and Project Director Kelly Gaither (TACC) Co-PI and Community Engagement and Enrichment Director Philip Blood (PSC) Extended Collaborative Support Service Co-Director Bob Sinkovits (SDSC) Extended Collaborative Support Service Co-Director Greg Peterson (UT-NICS) XSEDE Operations Director David Hart (NCAR) Resource Allocations Service Director David Lifka (Cornell) XSEDE Cyberinfrastructure Integration Director Ron Payne (NCSA) Program Office Director Emre Brooks (UT-San User Advisory Committee Chair Antonio) Shawn Strande (SDSC) XD Service Providers Forum Chair Scott Wells (UT-NICS) Interim Senior Project Manager (ex officio) Lizanne Destefano Lead of External Evaluation (ex officio) Bob Chadduck Cognizant NSF Program Office (ex officio)

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Table of Contents XSEDE: The Extreme Science and Engineering Discovery Environment ...... i XSEDE Senior Management Team (SMT) ...... ii List of Tables...... vi Reading this Report ...... vii 1. Executive Summary ...... 1 Strategic Goals ...... 1 Summary & Project Highlights ...... 4 2. Science, Engineering, & Program Highlights ...... 6 A New Way to See Stress Using Supercomputers ...... 6 Supercomputer Simulations Reveal New Insight on Sea Fog Development...... 8 Stopping HIV in its Tracks ...... 9 Turning Animals Monogamous ...... 11 XSEDE Researcher Applies Supercomputing to Global Financial Markets ...... 13 Hawai’i H2O ...... 14 Undergraduate Students Apply Advanced Computing to Combat Violence ...... 16 XSEDE ECSS Symposium: Exploring New Levels of Complexity in ‘Hyper Glyphs’ Reveal New Insights in Visualized Data ...... 17 Q&A with Roberto Camacho Barranco, XSEDE Student Ambassador ...... 19 3. Discussion of Strategic Goals and Key Performance Indicators ...... 21 Deepen and Extend Use ...... 21 3.1.1. Deepening Use to Existing Communities ...... 21 3.1.2. Extending Use to New Communities ...... 22 3.1.3. Prepare the Current and Next Generation ...... 23 3.1.4. Raising Awareness ...... 24 Advance the Ecosystem ...... 24 3.2.1. Create an Open and Evolving e-Infrastructure ...... 24 3.2.2. Enhance the Array of Technical Expertise and Support Services ...... 25 Sustain the Ecosystem...... 25 3.3.1. Provide Reliable, Efficient, and Secure Infrastructure ...... 26 3.3.2. Provide Excellent User Support ...... 26 3.3.3. Effective and Productive Virtual Organization...... 28 3.3.4. Innovative Virtual Organization ...... 29 4. Community Engagement & Enrichment (WBS 2.1) ...... 30 CEE Director’s Office (WBS 2.1.1) ...... 33

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Workforce Development (WBS 2.1.2) ...... 33 User Engagement (WBS 2.1.3) ...... 34 Broadening Participation (WBS 2.1.4) ...... 35 User Interfaces & Online Information (WBS 2.1.5) ...... 35 Campus Engagement (WBS 2.1.6) ...... 36 5. Extended Collaborative Support Service (WBS 2.2) ...... 38 ECSS Director’s Office (WBS 2.2.1) ...... 40 Extended Support for Research Teams (WBS 2.2.2) ...... 40 Novel & Innovative Projects (WBS 2.2.3) ...... 41 Extended Support for Community Codes (WBS 2.2.4) ...... 41 Extended Support for Science Gateways (WBS 2.2.5) ...... 42 Extended Support for Training, Education, & Outreach (WBS 2.2.6) ...... 42 6. XSEDE Cyberinfrastructure Integration (WBS 2.3) ...... 43 XCI Director’s Office (WBS 2.3.1) ...... 45 Requirements Analysis & Capability Delivery (WBS 2.3.2) ...... 45 Cyberinfrastructure Resource Integration (WBS 2.3.3) ...... 46 7. XSEDE Operations (WBS 2.4) ...... 48 Operations Director’s Office (WBS 2.4.1) ...... 49 Cybersecurity (WBS 2.4.2) ...... 49 Data Transfer Services (WBS 2.4.3) ...... 49 XSEDE Operations Center (WBS 2.4.4) ...... 50 System Operations Support (WBS 2.4.5) ...... 50 8. Resource Allocation Service (WBS 2.5) ...... 51 RAS Director’s Office (WBS 2.5.1) ...... 52 XSEDE Allocations Process & Policies (WBS 2.5.2) ...... 52 Allocations, Accounting, & Account Management CI (WBS 2.5.3) ...... 53 9. Program Office (WBS 2.6) ...... 55 Project Office (WBS 2.6.1) ...... 57 External Relations (WBS 2.6.2) ...... 57 Project Management, Reporting, & Risk Management (WBS 2.6.3) ...... 58 Business Operations (WBS 2.6.4) ...... 58 Strategy, Planning, Policy, Evaluation & Organizational Improvement (WBS 2.6.5)...... 59 10. Financial Information ...... 60 11. Project Improvement Fund ...... 66 12. Appendices ...... 67

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Glossary and List of Acronyms ...... 67 Metrics ...... 71 12.2.1. SP Resource and Service Usage Metrics ...... 71 12.2.2. Other Metrics...... 81 Scientific Impact Metrics (SIM) and Publications Listing ...... 93 12.3.1. Summary Impact Metrics ...... 94 12.3.2. Historical Trend ...... 94 12.3.3. Publications Listing ...... 95 13. Collaborations ...... 145 14. Service Provider Forum Report ...... 150 15. UAC ...... 152 16. XMS Summary...... 154 Executive Summary ...... 154 17. XAB Executive Summaries ...... 155 Executive Summary of XSEDE Advisory Board Meeting, April 17-18, 2018 ...... 155 Executive Summary of XSEDE Advisory Board Meeting, June 20, 2018 ...... 157 Executive Summary of XSEDE Advisory Board Meeting, August 8, 2018 ...... 158 Executive Summary of XSEDE Advisory Board Meeting, October 10, 2018 ...... 159 Executive Summary of XSEDE Advisory Board Meeting, December 12, 2018...... 163

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List of Tables Table 1-1: Summary of Key Performance Indicators (KPIs) for XSEDE...... 3 Table 3-1: KPIs for the sub-goal of deepen use (existing communities)...... 21 Table 3-2: KPIs for the sub-goal of extend use (new communities)...... 22 Table 3-3: KPIs for the sub-goal of preparing the current and next generation...... 23 Table 3-4: KPIs for the sub-goal of raise awareness of the value of advanced digital research. .... 24 Table 3-5: KPIs for the sub-goal of create an open and evolving e-infrastructure...... 25 Table 3-6: KPIs for the sub-goal of enhance the array of technical expertise and support services...... 25 Table 3-7: KPIs for the sub-goal of provide reliable, efficient, and secure infrastructure...... 26 Table 3-8: KPIs for the sub-goal of provide excellent user support...... 26 Table 3-9: KPIs for the sub-goal of operate an effective and productive virtual organization...... 28 Table 3-10: New KPIs for the sub-goal of operate an innovative organization...... 29 Table 4-1: KPIs for Community Engagement & Enrichment...... 30 Table 5-1: KPIs for Extended Collaborative Support Service...... 38 Table 6-1: KPIs for XSEDE Cyberinfrastructure Integration (XCI)...... 44 Table 7-1: KPIs for Operations...... 48 Table 8-1: KPIs for Resource Allocation Service...... 51 Table 9-1: KPIs for Program Office...... 55 Table 10-1: Project Level Financial Summary...... 60 Table 10-2: Partner Institution Level Financial Summary...... 61 Table 12-1: Quarterly activity summary...... 72 Table 12-2: End of quarter XSEDE open user accounts by type, excluding XSEDE staff...... 74 Table 12-3: Active institutions in selected categories. Institutions may be in more than one category...... 75 Table 12-4: Project summary metrics...... 76

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Reading this Report This report is the result of an ongoing process of improving reporting on progress in delivering on our mission and realizing our goals thus communicating the value XSEDE bring towards enhancing the productivity of a growing community of scholars, researchers, and engineers. For a large, complex, highly-distributed project such as XSEDE, this is a considerable undertaking. This process has helped XSEDE improve as an organization and as a provider and broker of services to the compute- and data-enabled science and engineering research and education community. XSEDE reports on its activities and progress by using a metrics-based approach complemented by illustrations through our science and engineering highlights. Based on feedback from our review panels, advisory bodies, the NSF, and other stakeholders, we have defined KPIs (Key Performance Indicators) that measure progress toward our high-level strategic goals. The key concept is not that the metrics (KPIs) themselves have a direct causal effect on eventual outcomes, but rather that the metrics are chosen so that actions and decisions which move the metrics in the desired direction also move the organization in the direction of the desired outcomes and goals. KPIs at the project and Work Breakdown Structure Level 2 areas are intended to focus the attention of external stakeholders on what we believe to be the best (key) indicators of progress toward our long-term strategic goals. The Executive Summary (§1) is intended to effectively and concisely communicate the status of the project toward delivery of the mission and realization of the vision by reaching three strategic goals. Stoplight indicators (§1.1) are used to visually provide a quick understanding of our assessment of overall project progress with respect to the strategic goals in light of our KPIs. The Science, Engineering, & Program Highlights (§2) section provides a small selection of a continuing series of scientific and engineering research and education successes XSEDE has enabled. These successes are an ongoing testament to the importance of our services to the research community. The Discussion of Strategic Goals and Key Performance Indicators (§3) section provides the next level of detail in understanding project progress. It decomposes the strategic goals into sub- goals and discusses progress toward each of the sub-goals using KPIs that, where possible, represent measures of impact to the communities XSEDE supports. These first three sections take a project-wide view. A more detailed analysis of progress from the view of the areas responsible for supporting each of the sub-goals—and contributing toward the KPIs associated with those sub-goals—is provided by looking at each of the areas of the project and financial information in the remaining sections (§4, §5, §6, §7, §8, §9, §10, and §11). These sections also contain area highlights and Key Performance Indicators (KPIs) that are deemed important, along with links to corresponding sections of the XSEDE KPIs & Metrics wiki page. The metric tables on the wiki page contain definitions, descriptions, and collection methodology information about each metric in the tables. When a new metric is added, table cells from previous reporting periods will contain an asterisk (*) to designate that data for that metric was not being collected at that time. Prior to the RY2 Annual Report, we have reported on metrics at the Work Breakdown Structure Level 3. Based on feedback from reviewers and discussion with the Cognizant Program Officer, we will discontinue this to reduce duplication and improve readability of the document.

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Note that XSEDE Project Years (PY) run September-August and Report Years (RY) run May-April. The table below lists the schedule for Reporting Periods (RP) within each RY, including RY1 and RY5 which are slightly altered due to a shorter RY1 and a longer RY5.

RP4 (Included in RP1 RP2 RP3 Annual Report) Typical Schedule May 1- July 31 Aug. 1- Oct. 31 Nov. 1- Jan. 31 Feb. 1- Apr. 30 RY1 Period doesn’t exist Sept. 1- Oct. 31, Nov. 1, 2016- Jan. Feb. 1- Apr. 30, 2017 due to shortened 2016 (Abbreviated 31, 2017 (Included in Annual Sept. 2016-April first year. due to shortened Report) 2017 year) RY2 May 1- July 31, 2017 Aug. 1- Oct. 31, 2017 Nov. 1, 2017- Jan. Feb. 1- Apr. 30, 2018 31, 2018 (Included in Annual May 2017-April Report) 2018 RY3 May 1- July 31, 2018 Aug. 1- Oct. 31, 2018 Nov. 1, 2018- Jan. Feb. 1- Apr. 30, 2019 31, 2019 (Included in Annual May 2018-April Report) 2019 RY4 May 1- July 31, 2019 Aug. 1- Oct. 31, 2019 Nov. 1, 2019- Jan. Feb. 1- Apr. 30, 2020 31, 2020 (Included in Annual May 2019-April Report) 2020 RY5 May 1- July 31, 2020 Aug. 1- Oct. 31, 2020 Nov. 1, 2020- Jan. Feb. 1- Aug. 31, 31, 2021 2021 (Longer May 2020-Aug. period included in 2021 Final Report) It is anticipated that this report is read in electronic form (PDF) using Adobe Reader®. There is extensive cross-linking to facilitate referencing content across the document. In general, all text that has blue underlining (e.g., §2) is clickable. Clicking on the underlined text will take you to the referenced section. These are set up to facilitate moving back and forth between the high level discussions in §1 and §3, to more detailed discussions regarding specific project areas and financial information in §4, §5, §6, §7, §8, §9, §10, and §11. As noted, this represents an ongoing effort at improvement, and we welcome comments on how to improve any and all aspects of our reporting process.

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1. Executive Summary Computing across all fields of scholarship is becoming ubiquitous. Digital technologies underpin, accelerate, and enable new, even transformational, research in all domains. Researchers continue to integrate an increasingly diverse set of distributed resources and instruments directly into their research and educational pursuits. Access to an array of integrated and well- supported high-end digital services is critical for the advancement of knowledge. XSEDE (the Extreme Science and Engineering Discovery Environment) is a socio-technical platform that integrates and coordinates advanced digital services within the national ecosystem to support contemporary science. This ecosystem involves a highly distributed, yet integrated and coordinated, assemblage of software, supercomputers, visualization systems, storage systems, networks, portals and gateways, collections of data, instruments, and personnel with specific expertise. XSEDE supports the need for an advanced digital services ecosystem distributed beyond the scope of a single institution and provides a long-term platform to empower modern science and engineering research and education. As a significant contributor to this ecosystem, driven by the needs of the open research community, XSEDE substantially enhances the productivity of a growing community of scholars, researchers, and engineers. XSEDE federates with other high-end facilities and campus-based resources, serving as the foundation for a national e-science infrastructure with tremendous potential for enabling new advancements in research and education. Our vision is a world of digitally-enabled scholars, researchers, and engineers participating in multidisciplinary collaborations while seamlessly accessing computing resources and sharing data to tackle society’s grand challenges. Researchers use advanced digital resources and services every day to expand their understanding of our world. More pointedly, research now requires more than just supercomputers, and XSEDE represents a step toward a more comprehensive and cohesive set of advanced digital services through our mission: to substantially enhance the productivity of a growing community of scholars, researchers, and engineers through access to advanced digital services that support open research; and to coordinate and add significant value to the leading cyberinfrastructure resources funded by the NSF and other agencies. XSEDE has developed its strategic goals in a manner consistent with NSF’s strategic plan, Building the Future: Investing in Discovery and Innovation - NSF Strategic Plan for Fiscal Years (FY) 2018 - 20221, NSF’s strategies stated broadly in the Cyberinfrastructure Framework for 21st Century Science and Engineering2 vision document, and, the more specifically relevant, Advanced Computing Infrastructure: Vision and Strategic Plan3 document. Strategic Goals To support our mission and to guide the project’s activities toward the realization of our vision, three strategic goals are defined: Deepen and Extend Use: XSEDE will deepen the use—make more effective use—of the advanced digital services ecosystem by existing scholars, researchers, and engineers, and extend the use to new communities. We will contribute to preparation—workforce development—of the current and next generation of scholars, researchers, and engineers in the use of advanced digital services via training, education, and outreach; and we will raise the general awareness of the value of advanced digital services.

1 https://www.nsf.gov/pubs/2018/nsf18045/nsf18045.pdf 2 https://www.nsf.gov/cise/aci/cif21/CIF21Vision2012current.pdf 3 http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf12051

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Advance the Ecosystem: Exploiting its internal efforts and drawing on those of others, XSEDE will advance the broader ecosystem of advanced digital services by creating an open and evolving e-infrastructure, and by enhancing the array of technical expertise and support services offered. Sustain the Ecosystem: XSEDE will sustain the advanced digital services ecosystem by ensuring and maintaining a reliable, efficient, and secure infrastructure, and providing excellent user support services. XSEDE will further operate an effective, productive, and innovative virtual organization. The strategic goals of XSEDE cover a considerable scope. To ensure we are delivering on our mission and to assess progress toward our vision, we have identified key metrics to measure our progress toward meeting each sub-goal. These Key Performance Indicators (KPIs) are a high- level encapsulation of our project metrics that measure how well we are meeting each sub-goal. Planning is driven by our vision, mission, goals, and these metrics—which are in turn rooted in the needs and requirements of the communities we serve. The key concept is not that the KPIs themselves must have a direct causal effect on eventual outcomes, or measure eventual outcomes or long-term impacts, but rather that the KPIs are chosen so that actions and decisions which move the metrics in the desired direction also move the organization in the direction of the desired outcomes and goals. As discussed in our preceding report, the XSEDE Reporting Year 2 Annual Report and Project Year 8 Program Plan4, a significant review of our KPIs and metrics was conducted in the latter half of calendar year 2017. This resulted in considerable changes in metrics as we refined them. Table 1-1 reflects the revised summary of KPIs against which we will be reporting in this and subsequent reports. Table 1-1 below shows the project’s progress toward the three strategic goals and associated sub-goals. Status icons are used in the table as follows: A green status is defined as a strategic goal for which at least 90% of the targets for all KPIs are met. A yellow status is defined as a strategic goal within which at least 60% of the targets for all KPIs are met. A red status is a strategic goal with less than 60% of the KPI targets met.

A white status indicates there are currently no metrics tracked for this sub-goal or there is not complete data for any of the metrics tracked. Multiple indicators represent a strategic goal that has sub-goals for which there is incomplete data or that have metrics not currently tracked. In these cases, the second indicator is a qualitative assessment of the status provided in lieu of sufficient data or a formal metric being in place.

4 http://hdl.handle.net/2142/100097

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Table 1-1: Summary of Key Performance Indicators (KPIs) for XSEDE.

Strategic Sub-goals KPIs Goals

Deepen and Extend Use (§3.1)

● Number of sustained users of XSEDE resources and services via the portal ● Number of sustained underrepresented individuals using XSEDE Deepen use (existing resources and services via the portal communities) (§3.1.1) ● Percentage of sustained allocation users from non-traditional disciplines of XSEDE resources and service

● Number of new users of XSEDE resources and services via the portal ● Number of new underrepresented individuals using XSEDE resources and Extend use (new services via the portal communities) (§3.1.2) ● Percentage of new allocation users from non-traditional disciplines of XSEDE resources and services

Prepare the current and next generation ● Number of participant hours of live training delivered by XSEDE (§3.1.3)

Raise awareness of the ● Aggregate mean rating of user awareness of XSEDE resources and value of advanced services digital services (§3.1.4) ● Percent increase in social media impressions over time

Advance the Ecosystem (§3.2)

Create an open and evolving e- ● Total number of capabilities in production infrastructure (§3.2.1)

Enhance the array of ● Aggregate mean rating of user satisfaction with XSEDE technical support technical expertise and services support services (§0)

Sustain the Ecosystem (§3.3)

Provide reliable, efficient, and secure ● Mean composite availability of core services (%) infrastructure (§3.3.1)

● Mean time to ticket resolution (hours) Provide excellent user ● Aggregate mean rating of user satisfaction with allocations process and support (§3.3.2) support services ● Percentage of research requests successful (not rejected)

● Mean rating of importance of XSEDE resources and services to researcher Operate an effective productivity and productive virtual ● Percentage of users who indicate the use of XSEDE-managed and/or organization (§3.3.3) XSEDE-associated resources in the creation of their work product

Operate an innovative ● Percentage of Project Improvement Fund proposals resulting in virtual organization innovations in the XSEDE organization (§3.3.4) ● Mean rating of innovation within the organization by XSEDE staff

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Summary & Project Highlights The XSEDE project continues to provide significant value to the national research community by enabling high-impact scientific advances across a broad range of disciplines. In our continuing documentation of science success, we have selected a few key examples of efforts that have been enabled by XSEDE in conjunction with our Service Provider partners. These are highlighted in §2 of this report and span a range of domains including: new atomic level simulations showing asymmetric distributions on stressed materials that may help scientists design new materials; studying how and why sea fog forms; understanding a monkey protein that confers immunity to HIV; studying how evolution uses a universal formula for turning non-monogamous species into monogamous species; integrating advanced computing into a business curriculum; and a multi- institution collaboration aimed at helping the island paradise of Hawai’i ensure its water security. A continually updated collection of these successes are documented on the XSEDE website (see: https://www.xsede.org/science-successes). This reporting period includes a number of notable contributions and impacts to the community. Some highlights include: • In January, a new book titled, Culturally Responsive Strategies for Reforming STEM Higher Education: Turning the TIDES on Inequity, edited by the Association of American Colleges & Universities was published. Broadening Participation member, Linda Akli, made significant contributions to highlight the role of XSEDE training and education workshops on Morgan State University’s successful infusion of computation throughout their STEM curriculum and the development of minors in computational and data science. (§4) • CEE Education member, Aaron Weeden, was selected to contribute a beginner lesson on computational thinking to the Hour of Cyberinfrastructure project (NSF award #18- 29708). (§4) • The EMPOWER Program recruited a record number of students and mentors for the spring 2019 EMPOWER program, largely due to the XSEDE Campus Champions efforts to increase applicants. With their ten year anniversary coming to an end, the Campus Champions program grew to over 530 individuals from over 270 institutions during this reporting period. (§4) • The XSEDE Training team completed a redesign of the course catalog webpage resulting in improved ease of use. (§4) • XSEDE information services has been moved to Amazon Web Services (AWS), re- architecting them to enhance scalability, reliability, and performance. (§6) • ORCID integration is complete across the XSEDE User Portal, RDR (Resource Description Repository), XRAS (XSEDE Resource Allocation Service), and is available to all XRAS clients; this will ultimately allow allocations information to be available via their ORCID; XSEDE is now waiting for the project’s GRID identifier to be released. (§4 & §8) • The University of Illinois’ Illinois Business Consulting team’s evaluation of XRAS found that there is sufficient interest in XRAS as a service to continue pursuing this sustainability approach. (§8) • Alex Withers, Senior Security Engineer at NCSA, has joined XSEDE as the co-manager of the cybersecurity group and the XSEDE Security Officer (XSO). (§7) • The XAB has four new members as of January 2019. (§9) • XSEDE has completed an assessment of our ability to handle controlled unclassified information (CUI) per the NIST standard (NIST Special Publication 800-171A) with identification of unsatisfied controls and assessment of risks associated with each of them. (§7)

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Of particular note also in this reporting period, in anticipation of requests for collaborations in response to a number of infrastructure-oriented solicitations released by NSF in October and November of 2018, XSEDE released its “Collaborating with XSEDE” page (https://www.xsede.org/about/collaborating-with-xsede) in order to address potential conflict of interest issues with competing potential collaborators. As of the writing of this report, nine letters of collaboration have already been provided and more than 10 additional requests are either in process or anticipated. This demonstrates the value XSEDE brings not only directly in support of science, but also in collaborations that enable science.

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2. Science, Engineering, & Program Highlights This section provides a select set of science and engineering highlights as well as program highlights from the community of researchers with whom we collaborate. These are drawn from the most recent reporting period (RP3 of RY3). A complete collection of highlights can be found at: https://www.xsede.org/science-successes. A New Way to See Stress Using Supercomputers Stressed materials show asymmetric distributions in simulations on Comet and Jetstream supercomputers through XSEDE allocations Stress is the force per unit area on an object. Scientists handle stress mathematically by assuming it to have symmetry. That means the components of stress are identical if you transform the stressed object with something like a turn or a flip. Supercomputer simulations show that at the atomic level, material stress doesn't behave symmetrically (see Figure 1). These findings could help scientists design new materials, such as glass or metal, that do not ice up. In a study published in September of 2018 in the Proceedings of the Royal Society A, co-author Liming Xiong from Iowa State University summarized the main findings: “The commonly accepted symmetric property of a stress tensor in classical continuum mechanics is based on certain assumptions, and they will not be valid when a material is resolved at an atomistic resolution.” Xiong continued that “The widely-used atomic Virial stress or Hardy stress formulae significantly underestimate the stress near a stress concentrator such as a dislocation core, a crack tip, or an interface, in a material under deformation.” Xiong and colleagues treated stress in a different way than classical continuum mechanics, which assumes that a material is infinitely divisible such that the moment of momentum vanishes for the material point as its volume approaches zero. Instead, they used the definition of stress, provided by mathematician A.L. Cauchy, as the force per unit area acting on three rectangular planes. With that, they conducted molecular dynamics simulations to measure the atomic-scale stress tensor of materials with inhomogeneities caused by dislocations, phase boundaries, and holes. The computational challenges swell up to the limits of what's currently computable when one deals with atomic forces interacting inside a tiny fraction of the space of a raindrop. “The degree of freedom that needs to be calculated will be huge, because even a micron-sized sample will contain billions of atoms. Billions of atomic pairs will require a huge amount of computation resource,” Xiong said. Through an XSEDE allocation, Xiong was awarded access to the Comet system at SDSC as well as the Jetstream Figure 1: Supercomputer simulations show that at the atomic system, a cloud environment level, material stress doesn't behave symmetrically. Molecular supported by Indiana University, the model of a crystal containing a dissociated dislocation, atoms are encoded with the atomic shear strain. Below, snapshots of University of Arizona, and TACC. simulation results showing the relative positions of atoms in the “What impressed our research group rectangular prism elements; each element has dimensions most about Jetstream,” Xiong said, 2.556 Å by 2.087 Å by 2.213 Å and has one atom at the center. Credit: Liming Xiong.

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“was the cloud monitoring and on-demand intelligence resource allocation. During the debugging stage of the code, we need to monitor how the code is performing during the calculation. If the code is not fully developed, if it's not benchmarked yet, we don't know which part is having a problem. The cloud monitoring can tell us how the code is performing while it runs. This is very unique,” said Xiong. The simulation work helps scientists bridge the gap between the micro and the macro scales of reality, in a methodology called multiscale modeling. "Multiscale is trying to bridge the atomistic continuum. In order to develop a methodology for multiscale modeling, we need to have consistent definitions for each quantity at each level…This is very important for the establishment of a self-consistent concurrent atomistic-continuum computational tool. With that tool, we can predict the material performance, the qualities and the behaviors from the bottom up. By just considering the material as a collection of atoms, we can predict its behaviors. Stress is just a stepping stone. With that, we have the quantities to bridge the continuum," Xiong said (see Figure 2). Xiong sees great value in the computational side of science.

“Supercomputing is a really powerful way Figure 2: Top: 2D visualization of the shear stress to compute. Nowadays, people want to components (credit: Liming Xiong) speed up the development of new Bottom: Shear stress distribution of a single crystal under a shear loading; (a,b) σ-xy,M and σ-yx,M in case A, (c) materials. We want to fabricate and atomic virial σ-xy,V in case A, (d) comparison of the understand the material behavior before distribution of σ-xy,M , σ-yx,M and σ-xy,V in the x direction putting it into mass production. That will in case A and (e) case B. require a predictive simulation tool. That predictive simulation tool really considers materials as a collection of atoms. The degree of freedom associated with atoms will be huge. Even a micron-sized sample will contain billions of atoms. Only a supercomputer can help,” Xiong said. The study, “Asymmetry of the atomic-level stress tensor in homogeneous and inhomogeneous materials,” was published September 5, 2018 in the Proceedings of the Royal Society A. The authors are Ji Rigelesaiyin and Liming Xiong of the Department of Aerospace Engineering, Iowa State University; Adrian Diaz, Weixuan Li, Youping Chen of the Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville. Study funding was provided by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Division of Materials Sciences

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and Engineering under Award no. DE-SC0006539. The MD simulations were enabled by XSEDE allocation TG-MSS170003. Supercomputer Simulations Reveal New Insight on Sea Fog Development Researchers use ~2 million core hours via XSEDE allocations to study how and why sea fog forms A recently published study by an international team of researchers has shed new light on how and why a particular type of sea fog forms, using XSEDE-allocated high-performance computing resources to create detailed simulations that provide more accurate predictions of its occurrence and patterns to help reduce the number of maritime mishaps. The study, published in the January 1, 2019 issue of Atmospheric Research, focused on a significant sea fog event that stretched approximately 400 miles across the Yellow Sea and its surrounding land. Images captured by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite allowed the research team to better explain the sensitivity of sea fog simulations to vertical resolution. The researchers have been using the Comet supercomputer based at SDSC. To-date, the team has used about two million core hours. “We have been getting used to the system, and all of our modeling systems have now been installed on Comet,” said Xiao-Ming Hu, a researcher with the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma and principal investigator for the project. “The system’s storage capacity enabled us to save useful model outputs for analysis until the research was eventually published, which was critical. All of this really saved our team a great deal of time and money.” NASA data affirms that spring fogs can occur for 50-80 days per year across the Yellow Sea. The fogs occur within the marine atmospheric boundary layer and are responsible for as many as half of the shipping accidents at the Qingdao port, according to the Chinese government (see Figure 3). The primary type of fog covering the Yellow Sea is called advection fog, which usually forms when warm, moist air moves into areas with a cold surface. The recently published study was conducted by Ocean University of China researchers Yue Yang and Shanhong Gao, in addition to Hu and Yongming Wang from the University of Oklahoma. The team used the Weather Research and Figure 3: This image near Qingdao, from the CALIPSO (Cloud- Forecasting (WRF) model to Aerosol Lidar and Infrared Pathfinder Satellite Observations) simulate the selected sea fog and satellite, shows the vertical cross-section of backscattering coefficients (indication of water droplets, i.e., sea fog) along the blue examine sensitivity of the WRF line AB. Courtesy of the Center for Analysis and Prediction of Storms simulations to different vertical (CAPS), University of Oklahoma.

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resolutions in order to provide insight regarding how the fog forms, as well as develop optimal model configurations to better predict sea fog. “Inaccurate predictions of sea fog over the Yellow Sea pose a severe risk or damage to many human activities such as aviation, shipping, and the general business economy,” explained Hu. “Accurate predictions of sea fogs involve a few parameterizations for the atmospheric boundary layer and microphysics processes, all of which requires appropriate resolutions. Finding the optimal configurations among resolutions and Figure 4: Evaluation of surface temperatures simulated with parameterization schemes is not only different vertical resolutions (from left to right) using station beneficial for predicting sea fogs as data over eastern Asia archived by NOAA. This helped demonstrated in this study, but also researchers identify the optimal vertical resolution for accurately simulating sea fog. Courtesy of the Center for helps researchers to better understand Analysis and Prediction of Storms (CAPS), University of and predict other meteorological Oklahoma. events.” The optimal model configurations by this study were adopted by the sea fog forecasting system from the Ocean University of China about one year ago, based on draft manuscripts of this research study. The Ocean University of China and University of Oklahoma researchers continue their collaboration to improve meteorological forecasting models in order to reduce the damage of meteorological disasters around the world, with support from supercomputers such as Comet (see Figure 4). “These studies have large-scale computing requirements that can only be satisfied by large supercomputers,” said Hu. “Without access to high-performance computing, CAPS would not be nearly as productive.” The study, “Sensitivity of WRF Simulations with the YSU PBL Scheme to the Lowest Model Level Height for a Sea Fog Event over the Yellow Sea,” was published in the January 1, 2019 issue of Atmospheric Research. Researchers include Yue Yang and Shanhong Gao, with the Ocean University of China; and Xiao-Ming Hu and Yongming, with the University of Oklahoma. This work was supported by XSEDE allocation grant TG-ATM160014, as well as the National Key Research and Development Program of China 2017YFC1404200 and 2017YFC1404100, and the National Natural Science Foundation of China (41276009). The first author received support from China Scholarship Council and the second author was partially supported by the NOAA VORTEX-SE Program through grant NA17OAR4590188. Stopping HIV in its Tracks XSEDE helps scientists understand monkey protein that confers immunity to HIV Scientists have scored a number of victories against HIV, the virus that causes AIDS. But these victories are incomplete. We can hold the virus in check, but not cure it. We can reduce the chances someone will be infected, but do not have a surefire way to prevent infection. Scientists from the University of Delaware and the University of Pittsburgh are using the XSEDE-allocated

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resource Bridges at PSC to investigate how one protein prevents HIV from infecting monkeys. Understanding how it does that, and why the human version of that protein doesn't, promises a completely new avenue for stopping HIV in its tracks. Juan Perilla of the University of Delaware, along with colleagues there and at the University of Pittsburgh, are interested in a protein called TRIM5α ("trim five alpha") as a

possible new target for therapy. In Figure 5: The HIV capsid's oblong shape (left) is made possible old-world and rhesus monkeys, by the CA protein (right) sometimes assembling in five- TRIM5α provides a hard stop to the membered pentamers (center, top), and sometimes in six- membered hexamers (center, bottom). The monkey TRIM5α HIV's ability to infect cells. Its ability protein disrupts the capsid by making the pentamers more rigid to destabilize HIV's capsid, the than they need to be to function. protective shell around the virus's genetic material, is what makes HIV unable to infect monkeys (see Figure 5). On the other hand, humans do have a version of TRIM5α—but for reasons we do not yet understand it cannot stop the virus like the monkey version does. Perilla and his collaborators would like to find out how monkey TRIM5α works, and possibly how the human version can be helped to do the same trick. This knowledge could provide a new way of attacking the virus that might stop HIV much more fully than the current generation of drugs. The scientists studied the problem in two ways. Angela Gronenborn of Pitt and Tatyana Polenova of Delaware used a lab technique called nuclear magnetic resonance (NMR) to study which parts of the virus' capsid protein, called CA, are affected when TRIM5α is present. NMR can tell what parts of the protein are affected, but not how they are affected. So, alongside the lab work, Perilla and his graduate students studied the system using simulations on PSC's Bridges supercomputer. The simulation was truly massive. Studying the interaction between a single copy each of the CA and TRIM5α proteins would be a significant computational problem. But Perilla's group took it much farther than that. They created a virtual version of the entire viral capsid, containing more than 1,000 copies of CA—a total of roughly four million atoms. In turn, the scientists embedded their simulated proteins in a box of simulated water molecules. The result was a system containing more than 64 million atoms. The researchers simulated the molecules' interactions, watching how TRIM5α affects the capsid. Analyzing the many interactions in the simulation required repeating the simulation under different conditions, which in turn required massive computer memory (RAM). The XSEDE- allocated Bridges' “large memory” nodes were perfect for the job. They contain 3 terabytes of RAM, which is 96 times the RAM in a high-end laptop. The simulated results obtained from Bridges agreed perfectly with the NMR lab results. That gave the scientists confidence that the simulations were accurately capturing the system. The simulation also painted an intriguing picture. Just as a soccer ball needs six-sided and five-sided panels to be round, the HIV capsid needs CA proteins grouped in five-sided “pentamers” and six- sided “hexamers” to achieve its normal, oblong shape. The effect of TRIM5α on the CA proteins was different depending on which of the two shapes they were forming, though (see Figure 5).

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Normally the CA proteins, even when they are linked together to form the capsid shell, wriggle and writhe. But in the presence of TRIM5α, the CA proteins in the pentamers were unusually stiff, moving far less than they normally do. This disruption of the pentamers makes the capsid unstable. In turn, that makes the virus unable to deliver its genetic material properly when it enters a host cell. The infection is stopped in its tracks. The article “Dynamic Regulation of HIV-1 Capsid Interaction with the Restriction Factor TRIM5α” was published Oct. 17, 2018 in the Proceedings of the National Academy of Sciences and was supported by awards from the National Institutes of Health as well as XSEDE allocation MCB170096. Editor's Note: This is the second major finding from the Perilla Lab in 2018 using XSEDE-allocated resources, each one targeting a completely different system in HIV. Read more about XSEDE's role in their efforts to model inositol phosphate interactions with HIV-1 structural proteins here. Turning Animals Monogamous XSEDE resource helps scientists understand how evolution uses a universal formula for turning non-monogamous species into monogamous species Why are some animals committed to their mates while others are not? According to a new study led by researchers at The University of Texas at Austin that looked at 10 species of vertebrates, evolution used a kind of universal formula for turning non- monogamous species into monogamous species — turning up the activity of some genes and turning down others in the brain. “Our study spans 450 million years of evolution, which is how long ago all these species shared a common ancestor,” said Rebecca Young, research associate in UT Austin's Department of Integrative Biology and first author of the study published in January 2019 in the journal Proceedings of the National Academy of Sciences. The authors define monogamy in animals as forming a pair bond with one mate for at least one mating season, sharing at least some of the work of raising offspring and defending young together from predators and other hazards. Researchers still consider animals monogamous if they occasionally mate with another. The researchers studied five pairs of closely related species – four mammals, two birds, two frogs and two fish — each with one monogamous and one non-monogamous member (see Figure 6). These Figure 6: In many non-monogamous species, females provide all or most five pairs represent five times of the offspring care. In monogamous species, parental care is often shared. In these frogs, parental care includes transporting tadpoles one in the evolution of vertebrates by one after hatching to small pools of water. In the non-monogamous that monogamy independently strawberry poison frog (Oophaga pumilio, left) moms perform this task; arose, such as when the non- however, in the monogamous mimic poison frog (Ranitomeya imitator, right) this is dad's job. Credit: Yusan Yan and James Tumulty.

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monogamous meadow voles and their close relatives the monogamous prairie voles diverged into two separate species. Researchers examined gene activity across the genomes of the 10 species, using RNA- Figure 7: At least five times during the past 450 million years, evolution used a sequencing technology kind of universal formula for turning animals monogamous — turning up the and tissue samples from activity of some genes (red) and turning down others (blue) in the brain. three individuals of each Researchers studied five pairs of closely related species – four mammals, two species. The scientists birds, two frogs and two fish — each with one monogamous and one non- detected gene-activity monogamous member. They found 24 genes with similar expression patterns in monogamous males. Illustration credit: The University of Texas at Austin. patterns across species using bioinformatics software and XSEDE-allocated time on TACC’s Wrangler data-intensive supercomputer. Using a software package, OrthoMCL, the team was able to arrange genes from distantly related species — such as a fish and a mammal — into groups based on sequence similarities. This allowed them to identify the common evolutionary formula that led to pair bonds and co-parenting in the five species that behave monogamously (see Figure 7). The researchers compared gene expression in male brains of all 10 species to determine what changes occurred in each of the evolutionary transitions linked to the closely related animals. Despite the complexity of monogamy as a behavior, they found that the same changes in gene expression occurred each time. The finding suggests a level of order in how complex social behaviors come about through the way that genes are expressed in the brain. According to Young, with traditional online databases she was only able to identify about 350 comparable genes across these 10 species. However, when she ran OrthoMCL on Wrangler, she identified almost 2,000 genes that are comparable across all of the species. “This is an enormous improvement from what is available,” Young said. “When you add this up across 10 species, you have an enormous amount of data. We're starting at a minimum of 80,000 genes that we're going to compare in all pairwise combinations for over three billion comparisons to perform and organize in total. The staff at TACC and the Wrangler supercomputer helped make this science possible.” The study, “Conserved transcriptomic profiles underpin monogamy across vertebrates,” was published January 22, 2019, in the journal Proceedings of the National Academy of Sciences. The authors are Rebecca Young, Hans Hofmann and Steven Phelps from The University of Texas at Austin. Authors at other institutions are Michael Ferkin (University of Memphis), Nina Ockendon-Powell (University of Bristol), Veronica Orr (University of California, Davis), Á kos Pogá ny (Eö tvö s Lorá nd University), Corinne Richards-Zawacki (University of Pittsburgh), Kyle Summers (East Carolina University), Tamá s Szé kely (University of Bath), Brian Trainor (University of California, Davis), Araxi Urrutia (University of Bath and Universidad Nacional Autónoma de México), Gergely Zachar (Semmelweis University) and Lauren O'Connell, (Stanford University). This work was supported by the Alfred P. Sloan Foundation (BR-4900); NSF Grants IOS-1354942, IOS-1501704, and IOS-1601734 (to H.A.H.); NIH Grant R01 MH85069-S2 (to B.C.T.); and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences (to Á.P.). Supercomputer time was awarded through XSEDE allocation BIO150029.

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XSEDE Researcher Applies Supercomputing to Global Financial Markets Mao Ye, a professor of Finance at the University of Illinois at Urbana-Champaign, is using XSEDE resources to integrate advanced computing into a business curriculum Until recently, the thought of using supercomputers to study global financial markets was relatively unheard of. Today, Mao Ye, a Finance researcher from the University of Illinois at Urbana-Champaign is seeking to change that, and perhaps even inform regulatory decision- making in the future, all with the help of XSEDE's computational and consulting expertise (see Figure 8). “I'm doing research on market microstructure, which requires massive amounts of data, and, as a consequence, analyzing even just two hours worth of data takes a very long time,” explained Ye. To examine how modern computing could be used to analyze these huge collections of data, Ye required outside expertise to aid in his understanding of high-performance computing, and how to navigate the administrative channels to make this research a reality. “With the unique challenge of data this size, some of my colleagues recommended that I contact TeraGrid [XSEDE's predecessor, which ended in 2011]. Through the application process, I bumped into John Towns, who helped me find computing resources and helped me to analyze the data.” Thanks to XSEDE, approaching the use of advanced computing systems to dive through these massive sets of data wasn't nearly as daunting as it could be; it was exciting. With guidance from XSEDE's ECSS team, Ye's novel research thoughts were transformed from an idea to an innovative use of supercomputing. “The work that Mao is conducting is truly impressive and of high impact in financial economics,” said Towns. “His work is opening new research opportunities in the discipline by bringing the application advanced computing resources and services to this community to tackle these immense challenges that fundamentally affect the markets.” After nearly a decade, Ye is still working with XSEDE to utilize computational resources, relying on ECSS expertise to do this efficiently, and establishing a series of workshops meant to educate interested parties. Over time, Towns and Ye have not been the only ones who saw value in this unique financial research. In September 2018, Ye received a $422,288 NSF grant for his project BIGDATA: IA: Collaborative Research: Understanding the Financial Market Ecosystem, to dive deeper into the intersection of Computer Science and Figure 8: Mao Ye, a Finance researcher from the University of Illinois Economics by interpreting and at Urbana-Champaign, uses supercomputers to study global financial markets. implementing big data techniques

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to understand financial data. Through this award, Ye will host a series of workshops in collaboration with the National Bureau of Economic Research (NBER) and XSEDE, with the goal of jump-starting a new community at the intersection of big data, finance, and public policy. While Ye's research has created a significant starting point for using high-performance computing to analyze market data, there is still a substantial amount to be discovered about this unique and emerging relationship. In time, we may see research in this confluence of disciplines inform financial regulations. Through access to XSEDE-allocated computational resources (and the expertise to know how to utilize them), Ye and others are offered the rare opportunity to peer into an industry whose regulations are in their infancy, and in turn, may inform how to regulate machine-based trading in the future. “What does the next generation of regulation look like? Should we update existing regulations or should we destroy the old regulations for humans? That's the first order of importance to the fairness and transparency of financial markets,” concludes Ye. “XSEDE will give me the computing power to fulfill my dream of pursuing this line of research.” BIGDATA: IA is supported by award no. 1838183 via the National Science Foundation, with supercomputer time and ECSS consultation provided through XSEDE Allocation award SES120001. Hawai’i H2O XSEDE Campus Champion introduces XSEDE resources to multi-institution collaboration aimed at helping the island paradise ensure its water security An estimated 1.1 billion people lack access to clean water worldwide. As the water systems that keep our taps running and our crops fed continue to be strained by a growing population and increasing pollution, the problem is expected to get worse. This threat is not lost on Hawai'i, a cluster of islands that faces a unique set of challenges due to its combination of isolation and volcanic geophysics. “Hawai'i typically has only about three days' worth of resources if airplanes and boats stop coming. You're far out if you need to get fresh water,” says Sean Cleveland, XSEDE Campus Champion and lead software architect at the University of Hawai'i's (UH) ‘Ike Wai gateway. ‘Ike Wai (meaning "knowledge" +"water") is a platform dedicated to ensuring Hawai'i's water security through research, education, and community engagement (see Figure 9). “The idea is to aggregate water data resources,” Cleveland says. “We Figure 9: Visualizing resources. The ‘Ike Wai gateway gives want to gather more information scientists access to sophisticated computational tools for data about watersheds and other water management, analysis, and visualization. Courtesy University of Hawai'i.

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data in Hawai'i into one place so that scientists can have access to these software tools and computational resources.” Although the project has been in the works for about three years, the gateway went online just a few months ago. Right now, most of the users are the researchers who make up the ‘Ike Wai program. While some are looking at the geophysics of Hawai'i relating to water, others are performing microbial testing on the state's various wells and watersheds. “They're trying to see if they can figure out some interconnectivity between wells in different places based on the fingerprint of their microbial communities,” Cleveland explains. “If there are organisms in this water and also in that water, then they may be interconnected in some way.” The hope is that, like the watersheds of Hawai'i, the gateway will serve as an interconnected resource for the community at large, scientists, and public officials who make the decisions about water policies in the state. To bring this gateway to fruition, Cleveland turned to XSEDE. ‘Ike Wai uses XSEDE-allocated resources, including Jetstream, a cloud environment designed to provide interactive computing and data analysis to researchers. They are also looking into using the Stampede2 supercomputer at TACC to supplement the capacity of UH's own high-performance computer cluster. “Thus far, we have primarily used Jetstream for this project to stand up educational/workshop environments to introduce Jupyter and a containerized compute environment for data science to some of our researchers,” Cleveland says. “Additionally, I have been working with some of our researchers creating Jetstream VMs for the development of rainfall monthly map software and workflows, and doing microbial sequence analysis – these VMs supplement the HPC capabilities of our local central cluster.” Because the system is still new, Cleveland says one of the biggest challenges in developing the gateway is the initial lack of data. They have combatted this by, at first, importing information from other repositories. They are also exploring methods used by agencies like the NSF and NIH that require researchers to deposit their data when using the gateway. But the more data they collect, the greater the responsibility to keep it secure. “Any device that's connected to the internet is going to have cybersecurity issues associated with it,” says Cleveland. “If it gets infiltrated, it can become a vector for infecting or infiltrating other infrastructure the network is on.” Without appropriate security measures, hackers could use the tool as a literal gateway into UH's system at large—which houses valuable student and other data. This is why they've partnered with Trusted CI to conduct a cybersecurity assessment. Cleveland and his team are also working with the Science Gateways Community Institute to ensure the ‘Ike Wai gateway is as accessible and easy to use as possible for researchers, policy makers, and citizens. This includes a data visualization tool they've been developing with the United States Geological Survey (USGS), which provides water managers with scenario tests that can inform their decisions on how to use available land for things like farming. “Water is really important to a lot of folks, but we've discovered that some of our water infrastructures are not as good as we always thought,” says Cleveland. “We're providing the tools so that we can better plan and better utilize our water resources. Clean drinking water and access to water is going to be a big thing going forward.” The 'Ike Wai project is funded by the NSF EPSCoR RII Track 1 Program - Grant NSF OIA #1557349. XSEDE resources allocated through grants TG-CCR140024, TG-ASC180040, TG- OCE150019.

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Undergraduate Students Apply Advanced Computing to Combat Violence XSEDE-sponsored Computing4Change competition brings diverse students together for transformative learning experience In November, two dozen students from across the U.S. and around the world teamed up to compete for a $500 team prize and develop solutions to the issue of violence as part of the Computing4Change event, now in its third year, which empowers students to create change through advanced computing and is sponsored in part by XSEDE (see Figure 10). “The Computing4Change competition is a 360 degree approach for exposing students to the relevancy and power of data and computational science to their disciplines and in driving social change,” said Linda Akli, co-founder of the program and assistant director of Training, Education & Outreach at SURA. “In addition to programming, data analytics, and visualization skills, the students learn how to use the results of their data exploration to inform policy and funding decisions by local, state, and federal leaders.” Over 48 hours, the students worked intensely to discuss how violence impacted their lives and communities, analyze available data on the topic, and develop computer-enabled solutions. They presented these solutions to an audience of supercomputing professionals at SC18. Alexandra Hanson, a junior studying computer science at Clackamas Community College, noted the rapid and extreme rise in anti-Semitic violent incidents from 2015 to 2018. She wanted to know: Did misinformation online play a role in inspiring individuals to commit acts of violence? She cited examples like the Pizzagate conspiracy theory and the growth in hate group forums, which communicate and reinforce racist ideas. Hanson suggested several possible solutions to combat misinformation; for instance, using artificial intelligence (AI) to determine the credibility of information online or mounting online awareness campaigns to help people better determine credibility themselves. “The data is shocking,” she concluded. “But there's still reason for hope.” Others discussed forms of violence that had struck close to home such as bullying, domestic violence and suicide. Pai-Chu Chin, a Chinese- born student studying at Simmons College, presented several possible technological solutions to violence, including GPS receivers in firearms that could detect guns in prohibited areas and disable them. The students met in person for the first time at the SC18 conference, but their experience began six months earlier when they were selected from a pool of Figure 10: The 2018 cohort of Computing4Change.

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hundreds of applicants to attend the competition. The 16 participants were citizens of five countries. Nearly two-thirds identified as female, 12 percent had a disability, and 50 percent had never attended a professional conference. Among the awardees, 31 percent were Hispanic/Latino, 12 percent were Black/African American, 19 percent were Asian/Asian-American and 25 percent were White. Four additional students joined the program from Chaminade University in Hawai'i as part of the National Science Foundation-funded INCLUDES program. Four additional students were participants at previous Computing4Change competitions and served as mentors to the first-time students. For the first time since Computing4Change began, students were asked to participate in six online webinars in the months leading up to the competition. These introduced technical topics like data analytics and visualization, as well as more general topics like public speaking, team science, and critical thinking. Once at SC, students were assigned to one of five groups and given a theme. They then worked for two-and-a-half days to develop their hypotheses, gather evidence, visualize their findings, and prepare 30-minute group presentations. “I was so impressed to see these young people's metamorphosis from tentatively examining issues to confidently presenting positions using supporting data,” said Sue Fratkin, a public policy analyst and guest instructor at the event. “The mentors pushed us to weave our stories in and have the data to back up our conclusions,” said Clara-Nathele Trainer, a junior studying Forensic Science at Chaminade University of Honolulu. The presentations ended emotionally, with the recitation of Ho'oponopono — a practice of reconciliation and forgiveness — by the native Hawaiian students. It was easy to be moved by the sincerity of the students and the transformative impact of the experience they had undergone. Nancy Wilkins-Diehr, a leading scholar at SDSC and an audience member at the final presentation, said that this competition was among the most powerful experiences she had witnessed in 20 years of attending SC conferences. “This will stick with me for the rest of my life,” Wilkins-Diehr said. “I feel so honored to be in this small room to be part of something so amazing and I wish the other 10,000 people at the conference could hear these compelling stories, backed up by data, and with feeling that we don't see in so many presentations.” “We're all different people — really different, tackling the same issue together,” said Nainoa Norman Ing, a junior studying biochemistry at Chaminade University. “That's powerful to me — coming together and working across different levels to solve problems.” XSEDE ECSS Symposium: Exploring New Levels of Complexity in ‘Hyper Glyphs’ Reveal New Insights in Visualized Data While the use of glyphs in scientific research is well documented, their untested limits as to how much information can be contained in a single glyph – or a ‘hyper glyph' – has the potential to lead to new knowledge and insights through data visualization, according to a presentation at XSEDE's first ECSS Symposium of 2019. “There's some wild stuff I'll be showing you,” said Jeff Sale, a visualization consultant for XSEDE's ECSS group and a research programmer/analyst with SDSC. “We're hoping to reach folks who are looking deep into their data sets and don't know where to begin when it comes to learning how to visualize the results of such data.”

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Figure 11: These two hyper glyph towers, based on visualizations of the Lahman Baseball Database, show the hits and home runs of Babe Ruth (left) and Barry Bonds (right) throughout their careers, from the bottom (earliest) to top (latest). Note how the yellow cubes in the Ruth visualization are more consistent throughout his career with an expected tapering toward the end, while the Bonds visualization shows a late-career surge that revealed his alleged use of performance-enhancing steroids. Images courtesy of Jeff Sale/SDSC.

Sale's presentation5 covers the long evolution of the glyph, which is widely known as a typographic terminology used to describe visual symbols or markers. In the world of scientific research, even a wind barb – a symbol which represents a certain speed and direction of wind – is considered a glyph. “A growing percentage of the ‘big data’ torrent consists of semi-structured, unstructured, and non-traditional data, presenting a challenge for conventional visualization methods,” according to Sale. “Hyper glyphs add a new dimension to how researchers can not only find new insights in their data, but present their findings in a way that resonates with those who are inclined to better understand concepts, results, and trends if they're presented in a visual format.” The primary thrust of Sale's presentation focused on a video demonstration of ANTz, a free, open-source tool that provides a menu of various hyper glyph geometries and extensions so researchers can better identify trends in their data, by visually clustering similar glyphs or noting anomalies in glyph structures, for example. ANTz is designed for interactive exploratory visual analytics by anyone working with large amounts of non-standard or unconventional data, and can be used in a great diversity of research domains, from analyzing pedestrian and bike traffic patterns and density in urban areas, to animated research such as seismic wave propagation or tracking wind patterns. From Hits and Home Runs to Bits and Bots Sale highlighted two hyper glyph demonstrations in particular. The first was a visualization of a statistical analysis of the hits and home runs of baseball players, called the Lahman Baseball Database, used in part to analyze the ebbs and flows in their entire batting careers. "Among other things, this data quickly showed that when fields became smaller, the number of home runs became greater," said Sale. The visualization also verified a late surge in Barry Bonds' entire career, leading Major League officials to suspect Bonds of using steroids to boost his performance (see Figure 11). XSEDE's ECSS Symposium allows the 70+ ECSS staff members as well as any XSEDE users to exchange information about successful techniques and new technologies used to address challenging science problems. Two 30-minute, technically-focused talks are presented the third

5 XSEDE, “ECSS Symposium January 2019,” YouTube, 16-Jan-2019. [Online]. Available: https://www.youtube.com/watch?v=gAtSCHqdZNk&feature=youtu.be&t=1763. [Accessed: 14-Feb-2019]

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Tuesday of each month, followed by a brief question and answer period. The Symposium is open to everyone. “The Symposium is an excellent opportunity to learn more about new areas of computational science and the positive impact that ECSS staff have on important research problems,” said Robert Sinkovits, XSEDE's co-director of ECSS. “ECSS is available to all XSEDE PIs, regardless of field of science or size of the allocation. Support in a wide range of areas, including visualization, can be requested either when an allocations proposal is submitted or later as a supplemental request.” Q&A with Roberto Camacho Barranco, XSEDE Student Ambassador XSEDE Broadening Participation student volunteer finds path to dream job at Google through experience with XSEDE mentorship and Computing4Change programs (see Figure 12) What is your research background? I'm currently a Ph.D. candidate in Computer Science at The University of Texas at El Paso (UTEP). My doctoral thesis focuses on data analytics and data mining. Specifically, I'm working on automatic word modeling to study the semantic evolution of words in large temporal text corpora. The primary goal of my research is to identify potential connections between entities that are hidden in the data. How and when did you get involved with XSEDE? My first contact with XSEDE was through the XSEDE conference (now PEARC) in 2016. Dr. Pat Teller was my mentor and advisor throughout my master's thesis and the initial part of my Ph.D. studies. She encouraged me to submit a paper for publication at the conference. Fortunately, the paper was accepted, and I applied to be a volunteer at the conference. As a volunteer, I met Dr. Kelly Gaither and Rosalia Gomez, who both encouraged me to apply for the first cohort of the Advanced Computing for Social Change Institute (ACSCI) challenge at SC16. After participating in this challenge, I was invited to be a mentor for the next iteration of the program at SC17 and was fortunate enough to return as a mentor for the newly-minted ACM SIGHPC Computing4Change competition. Recently, I was invited to join the team of instructors for the XSEDE Advanced Scientific Computing Python seminars that will be part of the NASA STEM-DIRECT program at California State University, Los Angeles. Figure 12: Roberto Camacho Barranco, a past participant and What kinds of projects and mentor with the Computing4Change program, recently accepted a students have you worked with? position with Google.

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One of the things I appreciate after being part of the ACSCI challenge and Computing4Change program is the diversity of backgrounds of the participants. Having such diversity encourages everyone to listen to different perspectives and to learn from the experience and knowledge of the other students. The goal of the challenge is to use computing, visualization, and data analysis as tools to raise awareness and suggest potential solutions to current social issues. The topics of the challenges that I've been a part of include the "Black Lives Matter" movement, immigration, and violence. What kind of contribution do you usually make, and what kind of benefit does that have for students? I have experience working as a data scientist and as a programmer, so I try to provide as much technical support as possible. However, my goal is not only to provide technical assistance but to guide the students based on my previous experience in these challenges and life. I help them focus on the problem at hand and structure their thought process. I usually try to figure out potential holes in their arguments and work with them on how to make sure they are well- prepared for their presentations. Ultimately, I hope that my mentees can transfer what they learn during the challenge into their own educational and professional life, always using critical thinking and taking into account the potential consequences of their work. What project did you work on that you felt was particularly meaningful or successful? I think the most meaningful challenge that I participated in was the Computing4Change event at SC17. This event was important to me because it was the first time I was a mentor. Furthermore, the topic of that cohort was immigration, which hit very close to home because I live in a border city and my family recently immigrated. I was thrilled to be able to share some of my experiences and raise awareness of issues that we, as a borderland community, face every day. What lifelong skills do you hope students learn from these experiences? I hope that the students learn the importance of data and the enormous responsibility we have when we use it to support a claim. I try to teach my mentees to identify the reliability of the data based on their source. Another critical point that I always stress is to be aware that many datasets are inherently, and most of the times inadvertently biased, so it is essential to identify these biases and clarify the scope of the claims. I also hope that students have an increased awareness of the current social issues and a higher empathy for people that are affected by these issues. Being aware of the problem is the first step towards solving it. What will you do after receiving your Ph.D.? I accepted a Software Engineering position at the Google campus in Boulder for which I am incredibly excited! It was always my dream to work at Google, but to be completely honest I always considered it just that, a dream. That changed when I talked with a Google representative that visited my school and convinced me to apply for an internship. I was an intern there for the past two summers, and I can't wait to start working there this year.

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3. Discussion of Strategic Goals and Key Performance Indicators The strategic goals of XSEDE (§1.1) cover a considerable scope. Additionally, the specific activities within XSEDE’s scope are often very detailed; therefore, to ensure that this significant and detailed scope will ultimately deliver our mission and realize our vision, we decompose the three strategic goals into components or sub-goals to be considered individually. In determining the best measures of progress toward each of the sub-goals, KPIs that correlate to impact on the scientific community are used. These often pair measurements of outcome with an assessment of quality or impact to provide both a sense of scope and significance of the supporting activities. Deepen and Extend Use XSEDE will 1) deepen the use—make more effective use—of the advanced digital services ecosystem by existing scholars, researchers, and engineers and 2) extend the use to new communities. We will 3) contribute to preparation—workforce development—of scholars, researchers, and engineers in the use of advanced digital technologies via training, education, and outreach; and we will 4) raise the general awareness of the value of advanced digital research services. 3.1.1. Deepening Use to Existing Communities XSEDE engages in a range of activities that serve to deepen use including identifying new technologies and new service providers, evolving the e-infrastructure, and enhancing the research prowess of current and future researchers. However, the ongoing use of resources and services available via XSEDE is the key indicator of this deepening use. As a result, the project has chosen three KPIs (Table 3-1) that together measure the ongoing engagement with the community with an emphasis on exposing the diversity of those consuming these services: 1) number of sustained users of XSEDE resources and services via the portal, 2) number of sustained underrepresented individuals using XSEDE resources and services via the portal, and 3) percentage of sustained allocation users from non-traditional disciplines of XSEDE resources and services.

Table 3-1: KPIs for the sub-goal of deepen use (existing communities).

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of CEE (§4) RY5 sustained users of RY4 XSEDE resources and 3,500/ RY3 4,196 4,089 3,099 services via qtr the portal 3,000/ RY2 3,962 3,754 2,488 3,020 4,527 qtr >5,000/ RY1 * 4,755 4,446 4,924 6,186 qtr Number of CEE (§4) RY5 sustained underreprese RY4 nted individuals 1,500/ RY3 529 5091 343 using XSEDE yr resources and 1,500/ RY2 490 408 296 402 1,640 yr

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Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year services via >1,000/ RY1 * 322 238 535 1,095 the portal yr Percentage of ECSS (§5) sustained RY5 allocation users from RY4 non- traditional 20/ RY3 22.1 20.7 25.8 disciplines of qtr XSEDE RY2 21.5 20.4 21.1 21.0 21.0 resources and services

RY1 * 18.2 19.9 18.6 18.9

1 Value updated as it was previously entered incorrectly. The number of sustained users this quarter fell just under the quarterly target. However, if you average across all previous quarters this year, the average is ~3700 which is well above our quarterly metric. The number of sustained underrepresented users is on track to meet the annual target. 3.1.2. Extending Use to New Communities New communities are defined as fields of science, industry, and underrepresented communities that represent less than one percent of XSEDE Resource Allocation Committee (XRAC) allocations. The Novel & Innovative Projects (NIP) team and the Broadening Participation team both work to bring advanced digital services to new communities. XSEDE measures both the number of new users and the number of new users on research projects from underrepresented communities and non-traditional disciplines of XSEDE resources and services as the indicators of progress (Table 3-2).

Table 3-2: KPIs for the sub-goal of extend use (new communities).

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of new CEE (§4) RY5 users of XSEDE resources and RY4 services via the portal 3,000/ RY3 1,9051 2,743 2,489 qtr 2,000/ RY2 2,305 2,813 2,346 2,917 10,381 qtr >1,000/ RY1 * 1,973 1,849 2,359 6,181 qtr Number of new CEE (§4) RY5 underrepresented individuals using RY4 XSEDE resources and services via RY3 200/qtr 134 155 129 the portal RY2 150/qtr 251 175 222 234 950

RY1 100/qtr * 150 135 240 525

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Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Percentage of new ECSS (§5) RY5 allocation users from non- RY4 traditional disciplines of RY3 30/yr 33.1 26.0 38.7 XSEDE resources and services RY2 24.8 26.0 26.6 33.9 27.8

RY1 * 21.8 24.9 31.0 25.7

1 Value updated as it was previously entered incorrectly. The target for new users of XSEDE resources and services via the portal was not met this reporting period. We will review the metric at the end of RY3 to re-evaluate having increased the target to 3,000/quarter at the start of this Reporting Year. We will also re-evaluate the metric for number of new underrepresented individuals using XSEDE resources and services via the portal as well, given that we did not meet the target again this reporting period. Additionally, given the trend in RY3, we will analyze factors that may be affecting this and put in place measures to remedy them. 3.1.3. Prepare the Current and Next Generation Part of XSEDE’s mission is to provide a broad community of existing and future researchers with access and training to use advanced digital services via the sub-goal of preparing the current and next generation of computationally-savvy researchers. While many activities support this sub- goal, such as the various Champion (§4.6), Student Engagement (§4.4), and Education (§4.2) programs, the training offered through Community Engagement & Enrichment (CEE) impacts the most people directly. Therefore, the key indicator (Table 3-3) of performance toward this goal, which is reflective of industry standards, is the number of participant hours of live training delivered by XSEDE.

Table 3-3: KPIs for the sub-goal of preparing the current and next generation.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of CEE (§4) RY5 participant hours of live RY4 training delivered by 40,000/ RY3 14,140 8,274 7,259 XSEDE yr RY2 * * * * *

RY1 * * * * *

The number of participant hours of live training delivered by XSEDE has been lower in the past two reporting periods than it was in reporting period one of this year. The Training team works to offer training through sites that reach as many participants as possible, but the last two reporting periods encompass holidays and university breaks that could explain the lower number of participant training hours. Past experience indicates that there are typically more participants in the spring and summer. Therefore, CEE expects to achieve the RY3 target.

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3.1.4. Raising Awareness While many activities led by teams throughout the XSEDE organization, such as our Workforce Development (§4.2), User Engagement (§4.3), Broadening Participation (§4.4), and Campus Engagement (§4.6) contribute to the ability to raise the general awareness of the value of advanced digital research services, the project has chosen to focus on measures in two areas (Table 3-4): user input and social media. Desirable trends in these key outcomes can be correlated to success for this sub-goal.

Table 3-4: KPIs for the sub-goal of raise awareness of the value of advanced digital research.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Aggregate mean PgO (§9) RY5 rating of user awareness of RY4 XSEDE resources and services RY3 3.5 of 5/yr 3.7 - -

RY2 3.6 - - - 3.6

RY1 * - - - NA Percent increase PgO (§9) RY5 in social media impressions over RY4 time RY3 15/yr 62 14 58

RY2 NA -14 -20 -2 93 9

RY1 NA * NA 147 -37 NA - Data reported annually Social media impressions continues to trend in a positive direction. An increase in impressions for RP3 ensures the annual target will be met for RY3. External Relations will review the RY3 social media activities and results to adjust the RY4 target appropriately. Advance the Ecosystem Exploiting its internal efforts and drawing on those of others, XSEDE will advance the broader ecosystem of advanced digital services by 1) creating an open and evolving e-infrastructure, and by 2) enhancing the array of technical expertise and support services offered. 3.2.1. Create an Open and Evolving e-Infrastructure There are a variety of factors that affect the evolution of the e-infrastructure. These range from external factors, such as the number of XSEDE Federation members and the variety of services they provide, to internal factors, like Operations (§7) of critical infrastructure and services and the evaluation and integration of new capabilities. While we actively seek new Federation members and Service Providers, as well as partnerships with national and international cyberinfrastructure projects, we view our role as connectors of these elements to have the most impact. Thus, XSEDE focuses on the number of new capabilities in production as an indicator of performance with respect to this sub-goal (Table 3-5).

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Table 3-5: KPIs for the sub-goal of create an open and evolving e-infrastructure.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Total number XCI (§6) RY5 of capabilities in production RY4

RY3 81 76 851 87

RY2 * 72 74 74 75 75

RY1 * * 63 63 69 69

1 Value updated as it was previously miscalculated. XCI surpassed the target of 81 capabilities supported in production for the year and reached 87 this reporting period by completing two new capabilities: enhancing the ability to find specific applications or services and documenting the capability to view active user account information. We anticipate a slowdown in new capabilities for the remainder of the project year, though we expect to end the year significantly surpassing our RY3 goal. 3.2.2. Enhance the Array of Technical Expertise and Support Services To enhance the technical expertise of our staff to offer an evolving set of support services, we will continue many activities including workshops, symposia, and training events hosted by Extended Collaborative Support Services (ECSS) and Service Providers (§5.6). The KPI for this is feedback provided from the XSEDE user-base through the annual user survey (Table 3-6).

Table 3-6: KPIs for the sub-goal of enhance the array of technical expertise and support services.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Aggregate PgO (§9) RY5 mean rating of user RY4 satisfaction with XSEDE 3.5 of RY3 3.6 - - technical 5/yr support services RY2 N/A 3.4 - - - 3.4 RY1 N/A * * * * *

- Data reported annually. The KPI for this goal is an annual KPI and was collected in RP1 for RY3. The target was exceeded for RY3. The Evaluation and leadership teams will review the KPI during the March Quarterly Meeting to determine if the target should be increased for RY4. Sustain the Ecosystem XSEDE will sustain the advanced digital services ecosystem by 1) ensuring and maintaining a reliable, efficient, and secure infrastructure, and 2) providing excellent user support services. Furthermore, XSEDE will operate an 3) effective, 4) productive, and 5) innovative virtual organization.

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3.3.1. Provide Reliable, Efficient, and Secure Infrastructure Many activities support the provisioning and support of reliable, efficient and secure infrastructure—such as User Interfaces & Online Information (§4.5), Security (§7.2), Data Transfer Services (§7.3), Systems Operations and Support (§7.5), support for Allocations (§8.2), and Allocations, Accounting & Account Management (§8.3)—but perhaps the truest measure of an infrastructure’s reliability is its robustness as reflected by sustained availability. Thus, the KPI for this sub-goal is the mean composite availability of core services, shown as a percentage (Table 3-7), measured as a geometric mean. This is a composite measure of the availability of critical enterprise services and the XRAS allocations request management service.

Table 3-7: KPIs for the sub-goal of provide reliable, efficient, and secure infrastructure.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Mean XSEDE RY5 composite Operations (§7) availability of RY4 core services (geometric RY3 99.9/qtr 99.9 99.9 99.9 mean of critical services and XRAS) RY2 99.9/qtr 99.8 99.9 99.9 99.9 99.9 RY1 99.0/qtr * 99.9 99.9 99.9 99.9

Operations once again met the composite availability target of 99.9 percent. Sustaining high availability of core, user-facing services is reflective of meeting end-user expectations. 3.3.2. Provide Excellent User Support Although nearly every group in the organization has some support function, we have chosen to focus on metrics with respect to two primary support interfaces to the community: the XSEDE Operations Center (XOC) and the Resource Allocation Services (RAS) team. The XOC is the frontline centralized support group that either resolves or escalates tickets to the appropriate resolution center depending on the request. RAS is responsible for the allocations process and the allocation request system. These two support interfaces are the focus for gauging the progress towards achieving the sub-goal of providing excellent user support, specifically: 1) the mean time to resolution on support tickets that are resolved by the XOC or routed to, and resolved by, other XSEDE areas, 2) the aggregate mean rating of user satisfaction with allocations process and support services measured via a quarterly survey of users who have interacted with the allocations request system and the allocations process more generally, and 3) the percentage of research requests successful (not rejected) determined following the quarterly allocations session (Table 3-8).

Table 3-8: KPIs for the sub-goal of provide excellent user support.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Mean time to XSEDE RY5 ticket resolution Operations (hours) (§7) RY4

RY3 < 16 / qtr1 12.3 18.5 23.2

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Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year

RY2 < 24 / qtr 26.0 20.1 22.8 15.0 21.0

RY1 < 24 / qtr * 24.0 28.2 23.1 25.1 Aggregate mean RAS (§8) RY5 rating of user satisfaction with RY4 allocations process and RY3 4 of 5/ yr 4.1 4.2 4.1 support services RY2 4 of 5/ yr 4.1 4.0 4.1 3.9 4.0

RY1 4 of 5/ yr * 4.0 4.0 4.0 4.0 Percentage of RAS (§8) RY5 research requests successful (not RY4 rejected) RY3 85.0 / qtr 65.0 70.0 72.0

RY2 85.0 / qtr 70.0 69.0 72.0 68.0 69.8

RY1 85.0 / qtr * 76.0 75.0 74.0 75.0

1 Target lowered to <16 hours effective RP3. This reporting period, the mean time to ticket resolution (MTTR) results were higher than expected and did not meet the target. Since the beginning of the project, the MTTR target has remained less than 24 hours. Beginning this reporting period, Operations lowered the target to less than 16 hours due to improvements in ticket tracking and resolution that have consistently resulted in much lower resolution times. Because the instrumentation of this KPI includes user (non-staff) tickets resolved by the XOC and the WBS queues, a considerable disparity in the number of tickets resolved by either the XOC or WBS queues in a given period can have a significant impact on MTTR. This period, the number of tickets resolved by the XOC decreased by 27% over last period, while the MTTR for WBS tickets was higher than normal. Also, one WBS had a small number of tickets incorrectly marked as external that added three hours to the total MTTR. Combined, these issues created an overall MTTR above the target. Operations will continue to work with all WBSs to properly disposition tickets in order to continue what had been a trend toward shorter resolution times. RAS continues to deliver high user satisfaction for both the process and the supporting software services. For the successful research requests KPI, there was a slight improvement during this reporting period even though it remains below the target. The addition of a required field to the XRAS Submit for XSEDE (requiring users to identify their access to other resources) is intended to reduce rejections for this technicality and is likely part of the improvement this reporting period. It is noted here, however, that reducing the rejection rates will result in greater post- review cuts to recommended allocations in order to fit within the envelope of available resources. In particular, Research requests from new PIs (62%) and those without Supporting Grants (57%) brought down the overall average. The team continues to look at ways to improve this success rate.

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3.3.3. Effective and Productive Virtual Organization During the first five years of XSEDE, in conjunction with developing a methodology for driving and assessing performance excellence, XSEDE adopted the Baldrige Criteria6 and has assessed and applied criteria from all seven criteria by that methodology. These include annual reviews of the vision, mission, strategic goals, project-wide processes and standards (KPIs); user and staff surveys (§4.3, §9.5); stakeholder communications (§9.1); advisory boards (§9.1); community engagement (§4); workforce development (§4.2); and the analysis of organizational data that leads to organizational learning, strategic improvement, and innovation. With this foundation, it is now appropriate to look to the XSEDE users to give us an indication of our effectiveness by rating the importance of the resources and services provided by XSEDE (Table 3-9).

Table 3-9: KPIs for the sub-goal of operate an effective and productive virtual organization.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Mean rating of PgO (§9) RY5 importance of XSEDE resources RY4 and services to researcher RY3 4.2 of 5/yr 4.4 - - productivity RY2 4.42 - - - 4.4

RY1 4.32 - - - 4.3 Percentage of PgO (§9) RY5 users who indicate the use of XSEDE- RY4 managed and/or XSEDE-associated resources in the RY3 79 79 - - creation of their work product1 RY2 * * * * * * RY1 * * * * * *

1 New KPI added in RY3 RP2. 2 These historical numbers are based on other survey data that was vaguely related to this KPI. We created a new survey item in RY3 to address it directly. - Data reported annually. The “Mean rating of importance of XSEDE resources and services to researcher productivity” KPI for this goal is an annual KPI and was collected in RP1 for RY3. The target was exceeded for RY3. The Evaluation and leadership teams will review the KPI during the March Quarterly Meeting to determine if the target should be increased for RY4. “Percentage of users who indicate the use of XSEDE-managed and/or XSEDE-associated resources in the creation of their work product” is a new KPI based on the XSEDE User Survey. As we only have one data point for this metric, we used it as our target for RY3. The Evaluation and leadership teams will review the KPI during the March Quarterly Meeting to determine the appropriate target for RY4.

6 https://www.nist.gov/baldrige/

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3.3.4. Innovative Virtual Organization Measuring innovation for an organization like XSEDE (or for organizations in general) is difficult and represents an area of open research. After much thought and discussion both internally and with external stakeholders and advisors, we have identified two indicators that correlate to innovation within the project: 1) percentage of Project Improvement Fund proposals resulting in innovations in the XSEDE organization and 2) mean rating of innovation within the organization by XSEDE staff (Table 3-10). The first indicator is a measurement of our ability to fund smaller innovative improvements within the project; the second measures how staff rate the level of innovation within the project. These KPIs will continue to be the subject of an open conversation within the organization and with stakeholders and advisors as XSEDE assesses these measurements and how to best quantify innovation.

Table 3-10: New KPIs for the sub-goal of operate an innovative organization.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Percentage of PgO (§9) RY5 Project Improvement RY4 Fund proposals resulting in innovations in the RY3 60/yr - - - XSEDE organization RY2 * * * * *

RY1 * * * * * Mean rating of PgO (§9) RY5 innovation within the organization RY4 by XSEDE staff 3.5 of 5/ RY3 - 4.0 - yr RY2 * * * * *

RY1 * * * * *

- Data reported annually. The Project Improvement Funds proposals resulting in innovations will be determined during RP4. The “Mean rating of innovation within the organization by XSEDE staff” KPI for this goal is an annual KPI and was collected in RP2 for RY3. The target was exceeded for RY3. The Evaluation and leadership teams will review the KPI during the March Quarterly Meeting to determine if the target should be increased for RY4.

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4. Community Engagement & Enrichment (WBS 2.1) Community Engagement & Enrichment (CEE) sits at the front lines of XSEDE and is tasked with balancing support for a large and diverse portfolio of existing users and the broader population of potential users and future leaders in cyberinfrastructure. While maintaining high quality support for existing users engaged in science at all levels, CEE is concerned with training and educating future generations and trying to creatively address what has been widely accepted as a leaky pipeline of potential users, leaders, practitioners, and researchers. At the core of Community Engagement & Enrichment (CEE) is the researcher, broadly defined to include anyone who uses or may potentially use the array of resources and services offered by XSEDE. The CEE team is dedicated to actively engaging a broad and diverse cross-section of the open science community, bringing together those interested in using, integrating with, enabling, and enhancing the national cyberinfrastructure. Vital to the CEE mission is the persistent relationship with existing and future users, including allocated users, training participants, XSEDE collaborators, and campus personnel. CEE will unify public offerings to provide a more consistent, clear, and concise message about XSEDE resources and services, and bring together those aspects of XSEDE that have as their mission teaching, informing, and engaging those interested in advanced cyberinfrastructure. The five components of CEE are Workforce Development (§4.2), which includes Training, Education and Student Preparation, User Engagement (§4.3), Broadening Participation (§4.4), User Interfaces & Online Information (§4.5), and Campus Engagement (§4.6). These five teams ensure routine collection and reporting of XSEDE’s actions to address user requirements. They provide a consistent suite of web-based information and documentation and engage with a broad range of campus personnel to ensure that XSEDE’s resources and services complement those offered by campuses. Additionally, CEE teams expand workforce development efforts to enable many more researchers, faculty, staff, and students to make effective use of local, regional, and national advanced digital resources. CEE expands efforts to broaden the diversity of the community utilizing advanced digital resources. The CEE team tightly coordinates with the rest of XSEDE, particularly Extended Collaborative Support Service (ECSS) (§5), Resource Allocation Services (§8), XSEDE Cyberinfrastructure Integration (§6), and External Relations (§9.2). CEE is focused on personal interactions, ensuring that existing users, potential users, and the general public have sufficient access to materials and have a positive and effective experience with XSEDE public offerings and frontline user support. As such, the CEE Key Performance Indicators are designed to broadly assess this performance. CEE focuses on metrics that quantify how many users in aggregate are benefiting from XSEDE resources and services. Additionally, CEE focuses on how well the user base is sustained over time and how well training offerings evolve with changing user community needs. Key Performance Indicators for CEE are listed in the table below. Additional information about these KPIs can be found on the XSEDE KPIs & Metrics wiki page. For other metrics with respect to this WBS, see Appendix §12.2.2.1.

Table 4-1: KPIs for Community Engagement & Enrichment. Repor Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total t Year Supported Number of Deepen/ students RY5 Extend —

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Repor Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total t Year Supported benefiting Deepen/ from XSEDE RY4 Extend — resources and 1,250/ Deepen use RY3 1,613 1,666 1,145 services qtr to existing through communities training, RY2 950/qtr 1,722 1,478 1,170 1,522 3,720 (§3.1.1) XSEDE projects, or conference RY1 50/qtr * 997 815 2,679 4,824 attendance Percentage of Deepen/ under- RY5 Extend — represented Deepen use students to existing RY4 benefiting communities from XSEDE (§3.1.1) resources and RY3 50/ qtr 28 26 27 services through training, RY2 50 / qtr 28 27 30 28 30 XSEDE projects, or conference RY1 50 / qtr * 34 33 19 28 attendance Aggregate Deepen/ RY5 mean rating of Extend — training Extend use RY4 impact for to new attendees 4.4 of 5 communities RY3 4.5 4.4 4.5 registered /qtr (§3.1.2) through the 4 of 5 RY2 4.3 4.3 4.6 4.5 4.4 portal /qtr 4 of 5 RY1 * 4.5 4.4 4.3 4.4 /qtr Number of Deepen/ RY5 institutions Extend — with a Deepen use RY4 Champion to existing communities RY3 250 259 266 277 (§3.1.1) RY2 240 218 238 239 246 246

RY1 225 * 224 231 234 234 Percentage of Sustain — RY5 user Provide requirements excellent RY4 addressed user support within 30 days 100/ 89 90 100 (§3.3.2) RY3 qtr (40/45) (47/52) (36/36) 100/ 78 102 86 93 91 RY2 qtr (36/44) (47/46) (32/37) (41/44) (156/157) 100/ 50 89 75 74 RY1 * qtr (16/32) (40/45) (40/53) (96/130) While the goal for the number of students benefiting from XSEDE resources and services through XSEDE training, XSEDE project, or conference attendance was not reached this

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reporting period, CEE expects to exceed the target for RP4. New collaborations with Minority Serving institutions this reporting period have resulted in scheduled student trainings in the next reporting period. The percentage of underrepresented students served by XSEDE continues to be flat at ~26% and significantly below the 50% target. Advanced Computing for Social Change (ACSC), Compute4Change (C4C), and the training collaboration at California State University at Los Angeles (CSULA) DIRECT-STEM will contribute to future increases in underrepresented minority students participating on allocated research projects and internships such as EMPOWER. Additionally, faculty at Minority Serving Institutions participating in workshops are beginning to include computational and data analytics into their courses with the support of the XSEDE Education program. This will increase the number of students being trained in the skills necessary to participate in research projects using advanced computing resources. The remaining KPIs were met this reporting period. CEE Highlights The User Interfaces and Online Information team worked closely with the XRAS team to develop and deploy the ORCID ID integration in the XSEDE User Portal profile service. Users now have the ability to link their ORCID ID to their XSEDE User Profile. This is the first step in enabling access to ORCID information to XSEDE, and a first step to creating the potential for further integration of ORCID and XSEDE (i.e., creating publications and adding allocation information). In January, the book Culturally Responsive Strategies for Reforming STEM Higher Education: Turning the TIDES on Inequity, edited by the Association of American Colleges & Universities was published. Chapter 14, “Interventions Addressing Recruitment and Retention of Underrepresented Minority Groups in Undergraduate STEM Disciplines”7 authored by Morgan State University faculty and Linda Akli highlights the role of XSEDE training and education workshops on Morgan State University’s successful infusion of computation throughout their STEM curriculum and the development of minors in computational and data science. The Campus Champions program continues to grow at a steady rate (individual members: 539 and institutional members: 277), and Champions continue to actively engage and collaborate not only on our email list, Slack, and the AskCI platforms, but also through our regularly scheduled calls and our new “ad hoc call” offerings. Efforts around the Grant Proposal Writing Apprenticeship and the Paper Writing Apprenticeship that arose from the Virtual Residency Workshops are on-going. Many Champions attended SC18 and contributed to the larger advanced CI community, but also found time in their busy schedules to meet as a community. Champions climate survey results were finalized and shared with the community at our All Champions call. Also, as 2018 came to a close, Champions finished by celebrating our 10-year anniversary. The EMPOWER Program, with help from the XSEDE External Relations team and the XSEDE Broadening Participation team, recruited students and mentors for the spring 2019 EMPOWER program. Applications were received from 45 students, and 24 high-quality candidates were selected to participate from 17 unique institutions. This is the largest number of applicants and selected participants to date for the program. Of particular note, the largest contribution to the

7 Darden, Cleo Hughes, Roni M. Ellington, Jigish Zaveri, Sanjay Bapna, Linda Akli, Stella Hargett, Prabir Bhattacharya, Ali Emdad, and Asamoah Nkwanta. n.d. "Interventions Addressing Recruitment and Retention of Underrepresented Minority Groups in Undergraduate STEM Disciplines." Chap. 14 in Culturally Responsive Strategies for Reforming STEM Higher Education, 229-247. doi:10.1108/978-1- 78743-405-920191014.

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increase in applicants to the EMPOWER program comes from the XSEDE Campus Champions. This is largely due to the role they play on campuses and their connections with researchers and students. Further, Campus Champions provide reviewers for the EMPOWER application process. Finally, previous program participant Jean Santiago (student from University of Puerto Rico- Mayagüez) presented a visualization from his project at the Gallery of Fluid Motion at the Annual Meeting of the American Physical Society Division of Fluid Dynamics, Atlanta, GA. The XSEDE Training team completed a redesign of the course catalog page, adding a new filtering functionality and providing an interface that allows users to find training materials with more ease. In addition to the Training team’s efforts, Education staff member Aaron Weeden was selected to contribute a beginner lesson on computational thinking to the “Hour of Cyberinfrastructure” project (NSF award #18-29708). The project seeks to create a clear curriculum model for educators integrating cyberinfrastructure skill building into a domain- specific curriculum, with a clear learning goal for students: try cyberinfrastructure for one hour. CEE Director’s Office (WBS 2.1.1) The CEE Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the CEE area. This oversight includes providing direction to the L3 management team, coordination of, and participation in, CEE planning activities and reports through the area’s Project Manager, and monitoring compliance with budgets, and retarget effort if necessary. The Director’s Office also attends and supports the preparation of project level reviews and activities. The CEE Director’s Office will continue to manage and set the direction for CEE activities and responsibilities. They will contribute to and attend bi-weekly Senior Management Team calls, contribute to the project level plan, schedule, and budget, contribute to XSEDE quarterly, annual, and other reports as required by the NSF and attend XSEDE quarterly and annual meetings. Lastly, the Director’s Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. Workforce Development (WBS 2.1.2) The Workforce Development mission is to provide a continuum of learning resources and services designed to address the needs and requirements of researchers, educators, developers, integrators, and students utilizing advanced digital resources. This includes providing professional development for XSEDE team members. Workforce Development fulfills its mission through an integrated suite of training, education, and student preparation activities to address formal and informal learning about advanced digital resources. Workforce Development provides business and industry with access to XSEDE’s workforce development efforts including training services and student internships that have historically proven beneficial to industry. Workforce Development is comprised of three areas: Training, Education, and Student Preparation. The Training team develops and delivers training programs to enhance the skills of the national open science community and ensure productive use of XSEDE’s cyberinfrastructure. The Education team works closely with Training and Student Preparation to support faculty in all fields of study with their incorporation of advanced digital technology capabilities within the undergraduate and graduate curriculum. The Student Preparation program actively recruits students to use the aforementioned training and education offerings to enable the use of XSEDE resources by undergraduate and graduate students to motivate and prepare them to pursue advanced studies and careers to advance discovery and scholarly studies.

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Based on the asynchronous training attendee data, the numbers for both the unique and total asynchronous attendees remains low. The data on these other metrics is reported based on XSEDE User Portal logins. To address not meeting the targets for these metrics, the XSEDE Training team continues efforts to complete conversion of the Cornell Virtual Workshop and the CI-Tutor online training to utilize XSEDE User Portal logins. The number of students benefiting from XSEDE resources falls just shy of the target for this reporting period. XSEDE Student Programs has increased recruitment efforts through collaboration with the Broadening Participation and External Relations teams. These efforts have increased the number of applicants for student programs, like XSEDE EMPOWER. Whereas the number of downloaded curricular materials from the public repository exceeded the target in the first two reporting periods of the year, the number of materials contributed to the repository is trending to fall short of the target. Additionally, no computational science modules have been added to the curricula, as these modules were contributed by faculty attendees of curriculum workshops that are no longer offered. The Education team seeks alternative sources of curriculum contributions, such as Campus Champions, to address meeting this target. Further, the Education team continues to support faculty and institutions in the development of formal degree, minor, or certificate programs and this reporting period saw one program added to the curricula at Morgan State University. For other metrics with respect to this WBS, see Appendix §12.2.2.1.1. User Engagement (WBS 2.1.3) The mission of the User Engagement (UE) team is to capture community needs, requirements, and recommendations for improvements to XSEDE’s resources and services, and to report to the national community how their feedback is being addressed. XSEDE places an emphasis on maintaining consistent user contact, traceability in tracking user issues, and closing the feedback loop. UE continues to connect with all active PIs every reporting period to ensure their projects are progressing and any issues their teams may be encountering are identified and addressed. In the current reporting period, inquiries were sent to 2,058 unique PIs: 146 responses were received, 36 issues were identified, and all 36 (100%) of these issues were addressed within 30 days. The goal is to completely address all issues within the reporting period, but UE relies on SPs and other areas within XSEDE to engage most issues. This KPI metric value for the current reporting period is consistent with the value reported during the reporting periods for the first two years of the project; however, the KPI has been modified as of the current reporting period to further improve service and address previous issues with reporting in issues identified late in the reporting period; the target now expects that issues be addressed within 30 days. Since IPR5, UE has assisted the Novel and Innovative Projects (NIP) lead (Sergiu Sanielevici) by sending contact emails to NIP project PIs. UE’s involvement in the cross-area effort to reduce the number of stale user accounts included reminders to renewal PIs to review the list of users with access to their projects; this task has also become a regular reporting period UE activity. UE continues to seek opportunities to assist the Campus Champions team with tasks related to user and champion engagement. For other metrics with respect to this WBS, see Appendix §12.2.2.1.2.

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Broadening Participation (WBS 2.1.4) Broadening Participation’s mission is to engage underrepresented minority researchers from domains that are not traditional users of HPC and from Minority Serving Institutions. This target audience ranges from potential users with no computational experience to computationally savvy researchers, educators, Champions, and administrators who will promote change at their institutions for increased use of advanced digital services for research and teaching. Broadening Participation will continue the most effective recruitment activities— conference exhibiting, campus visits, and regional workshops—while increasing national impact through new partnerships and the utilization of lower cost awareness strategies to continue the growth in new users from underrepresented communities. The Diversity Forum and the Minority Research Community listservs and community calls focus on user persistence in their use of XSEDE services and their deepening engagement through participation in committees such as the User Advisory Committee (UAC) and XSEDE Resource Allocations Committee (XRAC), and participation in Champions, Campus Bridging, and other programs. Persistent institutional engagement is enabled by curriculum reform and larger numbers of researchers adopting the use of advanced digital resources as a standard research method. Based on the sustained users for the first three reporting periods of PY8, the annual target for sustained underrepresented users KPI will be met. The target for recruiting new underrepresented users was not met this reporting period which included the academic winter break and no large scale broadening participation events. New underrepresented users should increase this spring as several workshops and additional conference exhibiting opportunities and presentations are planned, including exhibiting at the Emerging Researchers National Conference in February. The number of underrepresented students served by XSEDE continues to be flat at ~26% and significantly below the 50% target. Advanced Computing for Social Change (ACSC), Compute4Change (C4C), and the training collaboration at California State University at Los Angeles (CSULA) DIRECT-STEM will contribute to future increases in underrepresented minority students participating on allocated research projects and internships such as EMPOWER. The XSEDE-led training sessions for the CSULA DIRECT-STEM program will be held in February and March with an expected cohort of 75 undergraduates and graduates. Additionally, faculty at Minority Serving Institutions participating in workshops are beginning to include computational and data analytics into their courses with the support of the XSEDE Education program. This will increase the number of students being trained in the skills necessary to participate on research projects using advanced computing resources. For other metrics with respect to this WBS, see Appendix §12.2.2.1.3. User Interfaces & Online Information (WBS 2.1.5) User Interfaces & Online Information (UII) is committed to enabling the discovery, understanding, and effective utilization of XSEDE’s powerful capabilities and services. Through UII’s ongoing effort to improve and engage a variety of audiences via the XSEDE website and User Portal, UII has an immediate impact on a variety of stakeholders including the general public, potential and current users, educators, services providers, campus affiliates, and funding agencies. These stakeholders will gain valuable information about XSEDE through an information-rich website, the XSEDE User Portal, and a uniform set of user documentation. The UII team has continued to work closely with other areas of XSEDE including the XRAS team to deploy ORCID ID integration in the XSEDE User Profile. The metrics for this reporting period

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are lower than the expected targets. This pattern is common when the reporting period overlaps with winter break and holidays. There is usually a reduced usage in both the website and User Portal, but this bounces back after the new year. For other metrics with respect to this WBS, see Appendix §12.2.2.1.4. Campus Engagement (WBS 2.1.6) The Campus Engagement program promotes and facilitates the effective participation of a diverse national community of campuses in the application of advanced digital resources and services to accelerate discovery, enhance education, and foster scholarly achievement. Campus Engagement, via the Campus Champions, works directly with institutions across the U.S. both to facilitate computing and data-intensive research and education, nationally and with collaborators worldwide, and to expand the scale, scope, ambition and impact of these endeavors. This is done by increasing scalable, sustainable institutional uptake of advanced digital services from providers at all levels (workgroup, institutional, regional, national, and international), fostering a broader, deeper, more agile, more sustainable and more diverse nationwide cyberinfrastructure ecosystem across all levels, and cultivating inter-institutional interchange of resources, expertise, and support. Campus Engagement also aims to assist with the establishment and expansion of consortia (e.g., intra-state, regional, domain-specific) that collaborate to better serve the needs of their advanced computing stakeholders. The Campus Champions program continues to grow at a steady rate (individual members: 539 and institutional members: 277) and Champions continue to actively engage and collaborate not only on our email list, Slack, and the AskCI platforms, but also through our regularly scheduled calls and our new “ad hoc call” offerings. Efforts around the Grant Proposal Writing Apprenticeship and the Paper Writing Apprenticeship that arose from the Virtual Residency Workshops are on-going. Many Champions attended SC18 and contributed to the larger advanced CI community, but also found time in their busy schedules to meet as a community. Champions climate survey results were finalized and shared with the community at our All Champions call. Also, as 2018 came to a close, Champions finished by celebrating our 10-year anniversary. In 2018, Champions staff and volunteer leadership began to utilize the fifth Tuesday of the month for technical workshops on topics such as Markdown, Git and GitHub. These workshops are presented by volunteer members of the community (including Student Champion, Sateesh Peri, who presented in January), and attendance has averaged 50 participants per call. In addition, Champions staff added a new prototype event as part of our effort to bring interesting and relevant technical information to our community with topics typically drawn from discussions on our list or in our community calls. Jeff Pummill assumed responsibility for this effort entitled “Tech Talks” and works with technical experts to bring topics such as EasyBuild and Spack, XDMoD, the GABBs Project and the upcoming Sustainability via On-Campus Teams to our community. This effort is aimed at providing a consistent technical component to the ever growing Champions community, with much of the content to be provided by the Champions themselves. One of the wonderful things about a community like the Champions is the ability to find a “friendly” face in a sea of strangers. To support this, XSEDE hosted a “Breakfast with the Champions” at their booth at SC18. Champions were able to come together in an informal (but dynamic!) setting to network, meet fellow Champions in person, engage with XSEDE staff as well as potential new Champions, partners and supporters. This was also another great opportunity to celebrate our 10-year anniversary! In addition, the Champions held a formal meeting at SC18

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(also broadcast virtually for those unable to attend SC18) where we hosted guest speaker Dr. Dan Stanzione (TACC). He discussed Frontera and described how NSF’s new leadership class system will operate. He also announced that the project includes direct support for the Champions participating in conferences like PEARC. The meeting also provided another opportunity to network and added visibility to our volunteer leaders, as well as a platform for Champions to provide “flash talks” on everything from system rebuilds with CRI to new efforts with Women in HPC that might be of interest to other members of the community. We wrapped up the Champions Climate Survey review with Lorna Rivera’s presentation of the results to the Leadership Team in November and the full Champions community in December. Generally, staff and leadership are very pleased with the results of the survey. The results showed that Champions are thinking about our sustainable future. With a response rate just over 40%, the general findings of the survey showed that Champions rated their overall experience with the program positively. The results showed that the “Champions community impact extends beyond individual knowledge and skills improvement to the institutionalization of the champion role, changes in institutional research computing policy, and inclusion of Champions in strategic talent recruitment for their organizations.” The Champions culture was reported as being equitable and inclusive, with “respondents from non-research-intensive organizations and members of marginalized groups including women and underrepresented racial/ethnic groups in HPC, report[ing] positive experiences in the organization that mirrors majority respondents.” Other key findings included that specific resources like the mailing list, peer mentoring and Champions allocations were all seen as critical. In fact, when asked whether the email list has been useful in guiding policy/technology decisions at their institutions, 85% of respondents said “yes.” The Regional program, while needing further refinement, was seen as useful for the onboarding of new Champions. Finally, the Champions community spent the whole of 2018 celebrating our 10-year anniversary. It was exciting to see the participation and support in various activities (including our 10-year anniversary video8, PEARC18 and SC18 activities) as well as active participation in discussions about the importance of having a community like the Champions and our ongoing efforts to create a sustainable future for the program. For other metrics with respect to this WBS, see Appendix §12.2.2.1.5.

8 XSEDE, “Campus Champions Celebrate 10 Years,” YouTube, 24-Jul-2018. [Online]. Available: https://www.youtube.com/watch?v=4rZKzL48DRY. [Accessed: 14-Feb-2019].

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5. Extended Collaborative Support Service (WBS 2.2) The Extended Collaborative Support Service (ECSS) improves the productivity of the XSEDE user community through meaningful collaborations and well-planned training activities. The objective is to optimize applications, improve work and data flows, increase effective use of the XSEDE digital infrastructure, and broadly expand the XSEDE user base by engaging members of underrepresented communities and domain areas. The ECSS program provides professionals who can be part of a collaborative team—dedicated staff who develop deep, collaborative relationships with XSEDE users—helping them make the best use of XSEDE resources to advance their work. These professionals possess combined expertise in many fields of computational science and engineering. They have a deep knowledge of underlying computer systems and of the design and implementation principles for optimally mapping scientific problems, codes, and middleware to these resources. ECSS includes experts in not just the traditional use of advanced computing systems but also in data-intensive work, workflow engineering, and the enhancement of scientific gateways. ECSS projects fall into five categories: Extended Support for Research Teams (ESRT), Novel and Innovative Projects (NIP), Extended Support for Community Codes (ESCC), Extended Support for Science Gateways (ESSGW), and Extended Support for Training, Education and Outreach (ESTEO). Project-based ECSS support is requested by researchers via the XSEDE peer-review allocation process, or, in some cases, suggested by reviewers as something that would benefit the researchers. If reviewers recommend support and if staff resources are available, projects progress through three activities. First, the project is assigned to an ECSS expert. Second, the project is quantified with the formation of a work plan through collaboration with the research group. The work plan includes concrete quarterly goals and staffing commitments from both the PI team and ECSS. Third, when the project is completed, the ECSS expert produces a final report with input from the research group. A successful project is the completion of all three phases. Each state of the progression is measured to provide an assessment of progress. Submission of work plans within 45 days of initial contact, 90% of projects with work plans completed, and 85% of completed projects with final reports within three months are additional criteria for success. The ECSS managers review work plans and also track progress via Interim Project Reports. Key Performance Indicators for Extended Collaborative Support Service are listed in the table below. Additional information about these KPIs can be found on the XSEDE KPIs & Metrics wiki page. For other metrics with respect to this WBS, see Appendix §12.2.2.2.

Table 5-1: KPIs for Extended Collaborative Support Service.

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of Deepen/Extend RY5 completed ECSS — Deepen use projects to existing RY4 communities (§3.1.1) RY3 45/yr 17 10 12

RY2 50/yr 16 9 10 12 47

RY1 50/yr * 10 13 25 48

RY5

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Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Aggregate Deepen/Extend RY4 mean rating of — Deepen use ECSS impact by to existing RY3 4 of 5/yr NA 3.9 4.4 PIs communities (§3.1.1) RY2 4 of 5/yr 4.1 4.0 3.8 4.3 4.0 4 of 5 RY1 * 4.6 4.6 3.3 4.1 /qtr Aggregate Deepen/Extend RY5 mean rating of — Deepen use PI satisfaction to existing RY4 with ECSS communities support 4.5 of (§3.1.1) RY3 NA 4.3 4.8 5/yr 4.5 of RY2 4.7 4.6 4.2 4.8 4.5 5/yr 4.5 of 5 RY1 * 4.9 4.7 4.6 4.5 /qtr NA - There are no Impact and Satisfaction ratings as ECSS recently changed the way these assessments are recorded. Future ratings obtained from the PI interviews will be validated by an anonymous survey conducted by the XSEDE’s Strategic Planning, Policy & Evaluation (SP&E) team. In the current reporting period, ECSS has comfortably met the goals for its KPIs. The completion of 12 ECSS projects, combined with the 27 from the previous two reporting periods, puts us on track to hit our target of 45/year. The ECSS impact and satisfaction ratings were 4.4 and 4.8, respectively. ECSS Highlights Fake News Shelf life: Content, Reach, and Ephemerality of Hyperpartisan News This is a collaborative project based at Arizona State University, led by PI Michael Simeone, who is also an XSEDE domain champion. The project is interested in misinformation campaigns on Twitter, which are often rife with deleted and banned accounts. For this reason, doing after-the- fact data collection risks missing out on critical information about the causes and impacts of misinformation as given events unfold. To address this challenge, the project aims to collect and archive both tweets and linked video or text from those tweets in a high fidelity archive, then analyze the text they collect. The team plans to provide access to data and tools by building a Science Gateway. This project requires the use of both advanced storage and computing resources. Dr. Simeone worked with Dr. Alan Craig of ECSS NIP to write a successful startup grant proposal focused on the computational aspects of IU Jetstream. The NIP team then organized a discussion with ECSS Gateway experts at IU, in which it was agreed that the most suitable resource for hosting the project’s 10 TB of data is IU/TACC Wrangler storage. The project is now moving forward to create a work plan for ECSS support and also Science Gateways Community Institute support. Modeling AGN Feedback Physics in Galaxy Clusters with Ultra-High Resolution Cosmological Simulations Erwin Lau, the PI at Yale University, and TACC consultant, Albert Lu, addressed both general performance and scaling shortcomings for the Adaptive Refinement Tree (ART) code. ART is used to model the interactions of Active Galactic Nuclei (AGN) with the surrounding galaxies. The code is parallelized with MPI and OpenMP and is intended to run on small to medium node counts. Testing showed that the code suffered particularly from poor parallel performance and

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scaling of the OpenMP parallel sections while the MPI parallel efficiency was quite satisfactory. The effects of the poor OpenMP scaling were exacerbated by a poor choice of the number of MPI tasks and OpenMP threads on each node. A remarkable speedup of 7x was achieved when comparing the performance of the original code/task setup with the final configuration. This comparison was performed on the Skylake nodes. The PI submitted a renewal proposal to the recent XRAC and highlighted the achievements of the ECSS project to strengthen the application. ECSS Director’s Office (WBS 2.2.1) The ECSS Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the ECSS area. This oversight includes providing direction to the L3 management team, coordination of and participation in ECSS planning activities and reports through the area’s Project Manager, and monitoring compliance with budgets, and retargeting effort, if necessary. The Director’s Office also attends and supports the preparation of project-level reviews and activities. The ECSS Director’s Office will continue to manage and set direction for ECSS activities and responsibilities. They will contribute to and attend bi-weekly Senior Management Team calls, contribute to the project level plan, schedule, and budget, contribute to XSEDE quarterly, annual, and other reports as required by the NSF, and attend XSEDE quarterly and annual meetings. The Director’s Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. The office consists of two Level 2 Co- Directors, Philip Blood, who manages ESRT and NIP activities, and Bob Sinkovits, who manages ESCC, ESSGW, and ESTEO activities. The office also has three project managers (Marques Bland, Sonia Nayak, and Leslie Morsek). Blood and Sinkovits carry out the post-project interviews with all project PIs who have received ECSS support, both to get their assessment of how the project went, and to hear and act on any concerns they may express. Sinkovits also organizes the monthly symposium series, serves as one of the contributors to staff training, and runs the Campus Champions Fellows program (§4.6). Blood convenes User Advisory Committee meetings and supports the User Advisory Committee Chair. The project managers aid in the management of the day-to-day activities of ECSS, which includes the management of project requests (XRAC and startups), active projects, project assignments, and staffing. They continuously refine the ECSS project lifecycle, further defining processes to aid in the management of over 100 active projects. They also administer JIRA for the management and tracking of projects. Extended Support for Research Teams (WBS 2.2.2) Extended Support for Research Teams (ESRT) accelerates scientific discovery by collaborating with researchers, engineers, and scholars to optimize their application codes, improve their work and data flows, and increase the effectiveness of their use of XSEDE digital infrastructure. ESRT projects are initiated as a result of support requests or recommendations obtained during the allocation process. Most projects focus on research codes associated with specific research teams, as community codes fall under ESCC (§5.4), but are not exclusively restricted to this classification. The primary mandate of ESRT is the support of individual research teams within the context of their research goals. ESRT is on track to nearly meet its target of 27 projects per year. Currently, 18 projects have been completed. Another eight projects are expected to complete within the next three months, for a total of 26 projects. ESRT will continue to initiate projects and verify project status in order

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to increase the number of completed projects. Additionally, ESRT has met the impact and satisfaction rating goals of 5 and 4.5, respectively. For other metrics with respect to this WBS, see Appendix §12.2.2.2.1. Novel & Innovative Projects (WBS 2.2.3) Novel and Innovative Projects (NIP) accelerates research, scholarship, and education provided by new communities that can strongly benefit from the use of XSEDE’s ecosystem of advanced digital services. Working closely with the XSEDE Outreach team, the NIP team identifies a subset of scientists, scholars, and educators from new communities, i.e., from disciplines or demographics that have not yet made significant use of advanced computing infrastructure, who are now committed to projects that appear to require XSEDE services, and are in a good position to use them efficiently. NIP staff then provide personal mentoring to these projects, helping them to obtain XSEDE allocations and use them successfully. XSEDE projects generated by, and mentored by, the personal efforts of the NIP experts should stimulate additional practitioners in their field to become interested in XSEDE. Strategies used include building and promotion of science gateways serving communities of end-users and the enhancement of the Domain Champions program by which successful practitioners spread the word about the benefits of XSEDE to their colleagues. In PY8, NIP is focused on generating and mentoring projects with significant data analytics components from all disciplines. NIP is successfully using a list of projects to contact for needs analysis, as well as a collection of training resources enabling NIP team members to add data analytics skills to their expertise. There is strong community interest in data-centric projects, but many of them require a lot of initial guidance in implementing them on XSEDE resources. This increases the staff effort per user group needed to get them started, and can result in a lower number of projects generated by the efforts of NIP staff in some reporting periods, as is the case for IPR8. However, NIP’s KPIs and area metrics in IPR8 indicate successful adoption of XSEDE services by an increasing number of users. Indications are that, once we succeed in getting projects started, they tend to become successful. For other metrics with respect to this WBS, see Appendix §12.2.2.2.2. Extended Support for Community Codes (WBS 2.2.4) Extended Collaborative Support for Community Codes (ESCC) extends the use of XSEDE resources by collaborating with researchers and community code developers to deploy, harden, and optimize software systems necessary for research communities to create new knowledge. ESCC supports users via requested projects and XSEDE-initiated projects. ESCC projects may be created in two different ways. Most ESCC projects are initiated as a result of requests for assistance during the allocation process. These projects are similar in nature to ESRT projects but involve community codes rather than codes developed for and by individual research groups. ESCC projects may also be initiated by staff to support a community’s needs. ESCC has already exceeded its annual goal of nine projects per report year with 10 completed projects in the third reporting period. Although, the number of active projects is a little low. We expect the number of active projects to stay around nine or ten as projects complete or are initiated every reporting period. If the number of active projects continues to decrease, we will investigate rebalancing the FTE support level for ESCC vs ESRT. The average Satisfaction and Impact ratings for this reporting period were 4 and 5, respectively. Although our target for Satisfaction is 4.5, there was only one PI Interview/survey so this number was not averaged. We expect our average satisfaction rating to rise.

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For other metrics with respect to this WBS, see Appendix §12.2.2.2.3. Extended Support for Science Gateways (WBS 2.2.5) Extended Support for Science Gateways (ESSGW) broadens science impact and accelerates scientific discovery by collaborating in the development and enhancement of science-centric gateway interfaces and by fostering a science gateway community ecosystem. ESSGW has exceeded its metric target of nine projects completed with 11 ESSGW projects completed this year. The average Satisfaction and Impact ratings for this reporting period were both 4.5. Five ESSGW projects were completed this reporting period. The average Satisfaction and Impact ratings were both 4.5. For other metrics with respect to this WBS, see Appendix §12.2.2.2.4. Extended Support for Training, Education, & Outreach (WBS 2.2.6) Extended Collaborative Support for Training, Education & Outreach (ESTEO) prepares the current and next generation of researchers, engineers, and scholars in the use of advanced digital technologies by providing the technical support for Training, Education, and Outreach planned activities. Typical events include train-the-trainers events, on-site classes requested by Campus Champions, regional workshops, conferences, and summer schools (including the International HPC Summer School). Staff also create and review online documentation and training modules. This on-demand training is increasingly popular with the user community when both time and travel budgets are limited. ESTEO is on track to meet its metric goals for PY8. The survey to collect satisfaction from the previous year Campus Champions fellows has been issued, so ESTEO will be able to report on this in the next reporting period. For other metrics with respect to this WBS, see Appendix §12.2.2.2.5.

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6. XSEDE Cyberinfrastructure Integration (WBS 2.3) The mission of XSEDE Cyberinfrastructure Integration (XCI) is to facilitate interaction, sharing, interoperability, and compatibility of all relevant software and related services across the national CI community, building and improving upon the foundational efforts of XSEDE. XCI envisions a national cyberinfrastructure that is consistent and straightforward to understand, and practical for use by researchers and students. Service to XSEDE Service Providers (SPs) is a particularly important aspect of XCI’s activities. Service Providers should find it as simple as possible to provide resources to the national research community through shared expertise and, when appropriate, software and tools. We strive to make it possible for researchers and students to effortlessly use computational and data analysis resources ranging from those allocated by XSEDE to campus-based CI facilities, an individual’s workstation, and commercial cloud providers, and to interact with these resources via CI software services such as science gateways and Globus Online. Through XCI, XSEDE serves an aligning function within the nation, not by rigorously defining a particular architecture, but rather by assembling a technical infrastructure that facilitates interaction and interoperability across the national CI ecosystem. In turn, this infrastructure is adopted by campus, regional, and national CI providers because it makes their task of delivering services easier and the delivered services better. The suite of interoperable and compatible software tools that XSEDE will make available to the CI community will be based on those already in use, and services will be added that address emerging needs including data and computational services. XCI provides two essential integrating services. First, in regards to XSEDE, XCI provides the software glue that ties XSEDE together; particularly, it enables the interoperability of the several advanced computing resources supported by XSEDE with each other and with the XSEDE portal and other underlying infrastructure (e.g., accounting information). Beyond the specific systems directly tied to XSEDE, XCI improves the capabilities of campus cyberinfrastructure administrators anywhere in the US to manage local facilities in ways that are easily interoperable with the evolving national CI fabric while simultaneously leveraging training and educational materials created and disseminated by XSEDE. The XSEDE User Requirements Evaluation and Prioritization Working Group (UREP) prioritizes users’ needs for XCI. The UREP provides the primary direction and prioritization for the Requirements Analysis and Capability Delivery team (RACD). Given that, as a source of prioritization, the group of NSF-funded Level 1 SPs is generally customer number one for XCI. These are the nationally-accessible cyberinfrastructure resources created and operated by Service Providers that are required to be interoperable with XSEDE services in general, and to be allocated via the XRAC process. The Level 1 SPs, then, are the cyberinfrastructure Service Providers most invested in having important requirements from XSEDE and important requirements for XSEDE (and XCI) to fulfill. Level 2 and 3 SPs represent the next two most motivated and engaged groups of cyberinfrastructure providers in the U.S. The SP Forum is another source for user needs and priorities; the members of the SP Forum constitute national leaders and exemplars for the US national cyberinfrastructure community as a whole. By engaging with Level 1, 2, and 3 SPs and the SP Forum in particular, we believe that we can get the most detailed statements of needs and priorities. New tools implemented under the leadership of XCI are most likely to be widely and quickly adopted by the national community of CI providers if they are first adopted by members of the SP Forum. Because the XSEDE Cyberinfrastructure Resource Integration team (CRI) deals primarily with Level 1, 2, and 3 SPs, along with campus cyberinfrastructure administrators and support experts, the SP Forum and Campus Champions are XCI’s primary sources of direction regarding prioritization of efforts.

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The Community Software Repository (CSR) is a new service and tool catalog for the national research community to facilitate connecting resources, software, and services into the broader cyberinfrastructure ecosystem. Key Performance Indicators for XSEDE Cyberinfrastructure Integration are listed in the table below. Additional information about these KPIs can be found on the XSEDE KPIs & Metrics wiki page. For other metrics with respect to this WBS, see Appendix §12.2.2.3.

Table 6-1: KPIs for XSEDE Cyberinfrastructure Integration (XCI).

Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Aggregate Advance — RY5 mean rating of Create an open satisfaction and evolving e- RY4 with XCI infrastructure services (§3.2.1) RY3 4.5 of 5 4.1 4.5 4.8

RY2 4 of 5 4.3 4.5 4.4 4.7 4.5

RY1 4 of 5 * 4.8 4.8 4.5 4.5 Number of non- Advance — RY5 XSEDE Create an open partnerships and evolving e- RY4 with XCI infrastructure (§3.2.1) RY3 8/year 131 121 11

RY2 - - - - - 8

RY1 * - - - 8 Mean time to Sustain — RY5 issue Provide resolution excellent user RY4 (days) support (§3.3.2) RY3 14/qtr 22.3 7.3 2.9 <30 RY2 4.0 7.0 8.0 5.0 6.1 days <45 RY1 * 7.0 4.0 16.0 9.0 days 1 Value updated as it was previously miscalculated. XCI continues to pursue community satisfaction with XCI services. A high satisfaction rating indicates that XCI offerings are useful and adopted by the community. Users gave Web SSO documentation and integration assistance a top rating of 5 this period, which accounts for the high overall rating. XCI partnerships with non-XSEDE organizations represent relationships with cyberinfrastructure software developers, implementers at the campus and regional level, and with other cyberinfrastructure organizations. These partnerships allow XSEDE to extend and leverage its offerings successfully. This metric is reported annually, but XCI has already conducted a number of activities with partners this reporting year and is exceeding the target. RACD anticipates a small and stable number of non-XSEDE partnerships with one or two new partnerships by the end of the year. XCI leadership will discuss this KPI target and will adjust if for the next reporting year, as appropriate.

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Time to issue resolution indicates that XCI staff are able to quickly address issues brought to the team’s attention, which ensures that the XSEDE user community receives adequate service in a timely fashion in order to support its needs. So far, the average time to issue resolution has remained well within the target range, and while individual issues can be highly variable in terms of time to resolution, both of the XCI teams have been able to exceed targets in this area. Most reporting periods, XCI resolves one or more issues that are complex and challenging. There were no such issues this period to skew the overall results. XCI Highlights This reporting period, XCI migrated XSEDE information services (https://info.xsede.org) to Amazon Web Services (AWS), re-architecting them to enhance scalability, reliability, and performance; collaborated with Operations to manage all AWS services using the Ansible configuration management tool to ensure that all services have a known, reproducible, and secure configuration; and developed Information Services testing scripts to measure the initial performance of new AWS services and to help track longer-term performance as services are maintained and enhanced. XCI continues coordination of web single sign-on (Web SSO) implementation on XSEDE and affiliated web servers to streamline and standardize login to websites. This past reporting period, new documentation was provided for website operators and was used by the Resource Description Repository Service to implement Web SSO bringing a total of seven XSEDE and affiliated websites using Web SSO. XCI Director’s Office (WBS 2.3.1) The XCI Director’s Office has been established to provide necessary oversight to ensure the greatest efficiency and effectiveness of the XCI area. This oversight includes providing direction to the L3 management team, coordination of and participation in XCI planning activities and reports through the area’s project manager, and monitoring compliance with budgets, retargeting effort, if necessary. The Director’s Office also attends and supports the preparation of project-level reviews and activities. The XCI Director’s Office will continue to manage and set direction for XCI activities and responsibilities. They will contribute to and attend bi-weekly Senior Management Team calls; contribute to the project level plan, schedule, and budget; contribute to XSEDE IPRs, annual reports and other reports as required by the NSF; and attend XSEDE quarterly and annual meetings. Lastly, the Director’s Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. Requirements Analysis & Capability Delivery (WBS 2.3.2) The Requirements Analysis & Capability Delivery (RACD) team facilitates the integration, maintenance, and support of cyberinfrastructure capabilities addressing user technical requirements. The process begins by preparing Capability Delivery Plans (CDPs) that describe the technical gaps in XSEDE’s prioritized Use Cases. To fill the gaps, RACD evaluates and/or tests existing software solutions, engages with software providers, and facilitates software and service integration. To ensure software and service adoption and ROI, RACD will involve users, Service Providers, and operators in an integration process that uses engineering best practices and instruments components to measure usage. Once components are integrated, RACD will facilitate software maintenance and enhancements in response to evolving user needs and an evolving infrastructure environment.

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Important activity highlights this reporting period include: migrating XSEDE information services (https://info.xsede.org) to Amazon Web Services (AWS), re-architecting them to enhance scalability, reliability, and performance; collaborating with Operations to manage all AWS services using the Ansible configuration management tool to ensure that all services have a known, reproducible, and secure configuration; developed Information Services testing scripts to measure the initial performance of new AWS services and to help track longer-term performance as services are maintained and enhanced; Resource Description Repository (RDR) enhancements to support posting resources to ORCID; improvements to the Community Software Repository (CSR) to better account for capabilities supported in production; enhanced XSEDE services API documentation by moving it to the CSR; and lastly, continued coordination of web single sign-on (Web SSO) implementation on XSEDE and affiliated web servers to streamline and standardize login to websites. This past reporting period, new documentation was provided for website operators and was used by the Resource Description Repository Service to implement Web SSO bringing a total of seven XSEDE and affiliated websites using Web SSO. This reporting period, RACD met its target to deliver a total of 17 significant fixes and enhancements to production components; the total for this year is 44 so we are on track to meet our PY8 annual target of 48. New documentation was released to operators on how to enable XSEDE Web SSO into their web portals and 16 significant fixes and enhancements to the services mentioned above as well as the RDR, Information Services, CSR, the Single Sign-on Hub (SSO Hub) and CILogon services. RACD has completed instrumentation of CILogon for usage information and has completed tracking usage for the SSO Hub service. We plan to complete tracking of the gateway_submit_attributes, xdusage, and CILogon services by the end of next reporting period, meeting our target of 4 components. RACD prepared and delivered 14 Use Cases and CDPs for UREP review and conducted the UREP prioritization review. The UREP provided very helpful recommendations on the relative priority of proposed capabilities, and areas where further exploration may be necessary before a decision to move forward is made. These results will guide XCI’s PY9 plans. The average satisfaction rating of RACD services is currently on target with a rating of 4.9. RACD started a new engagement with XSEDE affiliated web server operators to assist them in implementing Web SSO. This increased our target to 5, which is 1 more than our target for the year. For other metrics with respect to this WBS, see Appendix §12.2.2.3.1. Cyberinfrastructure Resource Integration (WBS 2.3.3) The mission of the Cyberinfrastructure Resource Integration (CRI) team is to work with SPs, CI providers, and campuses to maximize the aggregate utility of national cyberinfrastructure. CRI will facilitate the incorporation of XSEDE software at SPs and encourage SPs to publish their information in the RDR. CRI’s activities are reflected in the uptake of CRI-integrated toolkits, such as the XSEDE Campus Bridging Cluster toolkit and XSEDE National Integration Toolkit, but also Globus Transfer clients and other toolkits as developed.

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This reporting period, CRI was able to add another large scale system to the XCBC adopters list, completing a site visit with the University of Cincinnati, which added a considerable amount of compute capacity to the aggregate compute supported by CRI’s OpenHPC implementation. In addition, the team updated three infrastructure toolkits including an update to the Jetstream virtual cluster toolkit to track changes to Jetstream’s charging scheme. The team is currently in discussions to develop an OpenStack toolkit similar to the OpenHPC toolkit, which will allow campuses to implement local research clouds much like the clusters. In order to support this, the team will leverage OpenStack expertise at Cornell (participating in the Aristotle Federated Cloud DIBBS award) and Indiana (from members of the Jetstream team). Furthermore, the team is looking into the integration of the Spack toolkit for packaging scientific software for more campus toolkit deployments. In addition, this reporting period has seen a large uptick in requests for letters of collaboration on infrastructure proposals, which have gone through XSEDE Project Director John Towns, as described in the “Collaborating with XSEDE” page on XSEDE’s website. For other metrics with respect to this WBS, see Appendix §12.2.2.3.2.

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7. XSEDE Operations (WBS 2.4) The mission of XSEDE Operations is to install, connect, maintain, secure, and evolve an integrated cyberinfrastructure that incorporates a wide range of digital capabilities to support national scientific, engineering, and scholarly research efforts. In addition to the Operations Director’s Office (§7.1), Operations staff is subdivided into four teams based on the work breakdown structure: Cybersecurity (SecOps) (§7.2), Data Transfer Services (DTS) (§7.3), XSEDE Operations Center (XOC) (§7.4), and Systems Operational Support (SysOps) (§7.5). The Operations management team meets weekly and individual Operations groups meet approximately bi-weekly with all meeting minutes posted to the XSEDE wiki. Key Performance Indicators for Operations are listed in the table below. Additional information about these KPIs can be found on the XSEDE KPIs & Metrics wiki page. For other metrics with respect to this WBS, see Appendix §12.2.2.4.

Table 7-1: KPIs for Operations.

Report Sub-goal Target RP1 RP2 RP3 RP4 Total Area Metric Year Supported Mean rating of Sustain — RY5 user Provide satisfaction reliable, RY4 with tickets efficient, and closed by the 4.5 of 5 / secure RY3 4.5 4.9 4.6 XOC qtr infrastructure 4.5 of 5 / (§3.3.1) RY2 4.5 4.8 4.8 4.4 4.6 qtr RY1 4 of 5 / qtr * 4.8 4.2 5.0 4.7 Hours of Sustain — RY5 downtime Provide with direct reliable, RY4 user impacts efficient, and from an secure RY3 0 / qtr 0 0 0 XSEDE infrastructure security (§3.3.1) RY2 0 / qtr 0 0 0 0 0 incident RY1 < 24 / qtr * 0 146 0 146

Both area KPI targets were met this reporting period. The XOC continues to demonstrate a commitment to superior service based on the high satisfaction results of users surveyed. In addition, the project had no downtime from a security incident. Results for both KPIs have remained consistent, with slight upward trending in the satisfaction metric. Operations Highlights Alex Withers, Senior Security Engineer at NCSA, has joined XSEDE as the co-manager of the cybersecurity group and the XSEDE Security Officer (XSO), replacing Randy Butler (NCSA), who had been serving the role on an interim basis. System Operations Support (SysOps) worked closely with XSEDE Cyberinfrastructure Integration (§6) group to migrate XSEDE services (e.g., XRAS_submit, XRAS_review, XRAS_admin) to the cloud (Amazon Web Services). The Resource Allocation Service (RAS) group (§8) was also involved in complimentary migration tasks. Because there was no user-facing

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downtime of services, these efforts demonstrate excellent and successful cross WBS collaboration. Operations Director’s Office (WBS 2.4.1) The Operations Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the Operations area. This oversight includes providing direction to the L3 management team, coordination of and participation in Operations planning activities and reports through the area’s Project Manager, and monitoring compliance with budgets, retargeting effort if necessary. The Director’s Office also attends and supports the preparation of project-level reviews and activities. The Operations Director’s Office will continue to manage and set direction for Operations activities and responsibilities. The Office will contribute to and attend bi-weekly Senior Management Team calls, contribute to the project-level plan, schedule, and budget, contribute to XSEDE IPR, annual, and other reports as required by the NSF, and attend XSEDE quarterly and annual meetings. Lastly, the Director’s Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. Cybersecurity (WBS 2.4.2) The Cybersecurity Security (SecOps) group protects the confidentiality, integrity and availability of XSEDE resources and services. Users expect XSEDE resources to be reliable and secure, thus the security team’s goal is to minimize any interruption of services related to a security event. The XSEDE Security working group recently completed an activity mapping XSEDE policies to the NIST Special Publication 800-171A "Assessing Security Requirements for Controlled Unclassified Information.” The result is a spreadsheet with each of the security controls and corresponding implementation status for the XSEDE project. For each control that was not satisfied, the group added two additional columns that indicate the significance (impact) of the control along with cost or effort level. The group currently does not have the resources to address every unsatisfied control and, in many cases, they are not applicable. As we conduct a new risk assessment this year, the results from the control mapping will serve as an input to this process. The risk assessment process considers the impact and probability of an event to calculate risk. Once completed, the results of the risk assessment will assist in prioritizing the resources of the XSEDE security team. This activity was influenced by a recommendation from a past NSF review panel. The results were shared with the Director of Operations with a request to provide any strategic input from XSEDE senior management. For other metrics with respect to this WBS, see Appendix §12.2.2.4.1. Data Transfer Services (WBS 2.4.3) The Data Transfer Services (DTS) group facilitates data movement and management for the community by maintaining and continuously evolving XSEDE data services and resources. DTS has completed the hands-on pilot engagement with University of Colorado in which the group assisted them in evaluating and improving the performance of a recently installed Data Transfer Node (DTN). The Colorado team is working through implementation of improvements recommended by the DTS team and will report back to the DTS team on their results. XSEDEnet migration work continues, and is nearly complete, on the move from the old Internet2 Advanced Layer-2 Service (AL2S) network to the new Layer-3 Virtual Private Network (L3VPN) topology. Migrating to the new L3VPN topology brings with it a number of improvements to XSEDEnet: 1) Service Provider sites can more easily link up to XSEDEnet, 2) routes between sites

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can change dynamically as needed to recover from link failures, and 3) fewer Internet addresses are needed to maintain the network, enabling more total sites to link to XSEDEnet. The migration work is expected to be completed before the end of the next reporting period. For other metrics with respect to this WBS, see Appendix §12.2.2.4.2. XSEDE Operations Center (WBS 2.4.4) The XSEDE Operations Center (XOC) staff serve as user advocates, providing timely and accurate assistance to the XSEDE community, while simultaneously monitoring and troubleshooting user- facing systems and services. In addition to meeting its satisfaction target this reporting period, the XOC lowered its mean time to first response (11 minutes) for the third consecutive reporting period and kept its mean time to resolution to just under 30 minutes. For other metrics with respect to this WBS, see Appendix §12.2.2.4.3. System Operations Support (WBS 2.4.5) Systems Operational Support (SysOps) provides enterprise-level support and system administration for all XSEDE central services. In addition to meeting its KPI target this reporting period, SysOps focused its efforts on helping XCI (§6) migrate enterprise services into Amazon Web Services (AWS) while maintaining existing services with little to no downtime. Also, SysOps secured AWS computing and database funding to support both XRAS and XCI services for the remainder of the XSEDE project. For other metrics with respect to this WBS, see Appendix §12.2.2.4.4.

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8. Resource Allocation Service (WBS 2.5) The Resource Allocation Service (RAS) is building on XSEDE’s current allocation processes and evolving to meet the challenges presented by new types of resources to be allocated via XSEDE, new computing and data modalities to support increasingly diverse research needs, and large- scale demands from the user community for limited XSEDE-allocated resources. RAS is pursuing these objectives through three activities: managing the XSEDE allocations process in coordination with the XD Service Providers, enhancing and maintaining the RAS infrastructure and services, and anticipating changing community needs. Key Performance Indicators for the Resource Allocation Service are listed in the table below. Additional information about these KPIs can be found on the XSEDE KPIs & Metrics wiki page. For other metrics with respect to this WBS, see Appendix §12.2.2.5.

Table 8-1: KPIs for Resource Allocation Service.

Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average mean Sustain — RY5 rating of user Provide satisfaction excellent user RY4 with support allocations 4 of 5/ (§3.3.2) RY3 4.1 4.1 4.2 process qtr 4 of 5/ RY2 4.1 4.0 4.1 3.9 4.0 qtr 4 of 5/ RY1 * 4.0 4.0 4.0 4.0 qtr Aggregate Sustain — RY5 mean rating of Provide user reliable, RY4 satisfaction efficient, and with XRAS 4 of 5/ secure RY3 4.0 4.2 4.1 qtr infrastructure 4 of 5/ (§3.3.1) RY2 4.0 4.0 4.1 3.9 4.0 qtr 4 of 5/ RY1 * 4.0 4.0 4.0 4.0 qtr KPI targets were met for this reporting period. RAS Highlights ORCID integration is complete across the XSEDE User Portal, RDR, and XRAS. The integration is available to all XRAS clients. Production use for XSEDE is now waiting for XSEDE’s GRID (https://www.grid.ac/) identifier to be released, which will occur upon completion of testing, but there is currently no set date. XRAS supports posting Research Resource information for awarded allocation requests to users’ ORCID profiles. The feature was demonstrated for ORCID’s User Facilities Working Group in February 2019. XRAS was also the topic of an External Relations-coordinated case study by the Illinois Business Consulting (IBC) program at the University of Illinois. The findings suggest a sufficient interest in XRAS as a service to continue pursuing this sustainability approach for XRAS. RAS will continue pursuing some of the potential new clients identified by the IBC students.

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The A3M and APP teams also worked together to improve a number of XRAS Admin features, including multi-phase review, a combined request and person search, a dashboard for pending requests, and improved listings of requests submitted to Opportunities. RAS Director’s Office (WBS 2.5.1) The RAS Director’s Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the RAS area. This oversight includes providing direction to the L3 management team, coordination of and participation in RAS planning activities and reports through the area’s Project Manager, and monitoring compliance with budgets, and retarget effort if necessary. The Director’s Office also attends and supports the preparation of project-level reviews and activities. The RAS Director's Office also contributes to an analytics effort to support NSF, Service Providers, and XSEDE in understanding and projecting the stewardship of, demand for, and impact of CI resources and services. The RAS Director’s Office will continue to manage and set direction for RAS activities and responsibilities. They will contribute to and attend bi-weekly Senior Management Team calls, contribute to the project-level plan, schedule, and budget, contribute to XSEDE IPR, annual, and other reports as required by the NSF, and attend XSEDE quarterly and annual meetings. Lastly, the Director’s Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. XSEDE Allocations Process & Policies (WBS 2.5.2) Allocations enable the national open science community to easily gain access to XSEDE’s advanced digital resources, allowing them to achieve their research and education goals. For the December 2018 XSEDE Resource Allocations Committee (XRAC) meeting, 269 proposals were submitted for XSEDE resources, of which, 193 were recommended for award—a success rate of 72%. For the meeting, requests for compute resources were 3.5 times the amounts available; approximately 540.6M SUs were requested with 242.4M SUs recommended and 154.1M SUs awarded. This resulted in recommendations that exceeded what was available by 88M SUs, requiring significant reductions in almost all recommended awards, the largest of which was 48%. It is likely that the increase of recommended requests and slight increase in success rate was due to additional education of the PIs and the improvements to the XRAS submission form for gathering required details about access to other resources. In terms of agency support, 146 requests (54.3%) were either entirely or partially supported by NSF awards, 60 requests (22.3%) had support only from non-NSF sources, and 63 (23.4%) listed no supporting grants. Some notable facts about this quarterly research opportunity were that 126 different PI institutions were represented, 22 from MSIs and 46 from EPSCoR states. Biophysics led all fields of science with 17.5% of the approved SUs, followed by Materials Research with 14.6% of the approved SUs. Along with the New and Renewal submissions for the December Research Opportunity, the RAS APP team managed the usual steady stream of requests for other allocation types and management actions for active projects during this reporting period. The team processed 179 Startup New and Renewal requests, 57 Educational New and Renewal requests, 30 Campus Champions New and Renewal requests, and two New and Renewal XSEDE Staff projects. For management actions across all allocation types, the team also processed 226 Extensions, 146 Supplements, 98 Transfers, and 55 Advances.

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In addition to the quarterly handling of allocation requests and the XRAC meeting, the RAS APP group continues to present two webinars about writing/submitting a successful Research proposal each quarter and addresses help inquiries from users. The group also continues to recruit new members to the XRAC review panel. The APP group also works with the A3M team to recommend improvements to the XRAS system. Leveraging new features in XRAS, including the admin dashboard and multi-phase review process, all the pieces of the allocations workflow that have been managed with the RT ticket system are being moved. For other metrics with respect to this WBS, see Appendix §12.2.2.5.1. Allocations, Accounting, & Account Management CI (WBS 2.5.3) The Allocations, Accounting and Account Management CI (A3M) group maintains and improves the interfaces, databases, and data transfer mechanisms for XSEDE-wide resource allocations, accounting of resource usage, and user account management. As a result of the collaboration with Pixo, XRAS now supports the ability to declare and name multiple allocation type review phases, associate different panels with each phase, and distinguish the reviews for each phase. While not originally anticipated, the A3M and APP teams are working together to see if this feature can replace the need for the RT ticket system as a way to manage requests and improve their workflow. ORCID integration is complete within XRAS and available to all XRAS clients. XRAS supports posting Research Resource information for awarded allocation requests to ORCID. The A3M team worked closely with the XSEDE User Portal team to collect the needed XSEDE users’ ORCID permissions that would allow the posting to happen, if permission is granted to XSEDE. RDR was enhanced to allow for this support. As part of improving the Administrator functions within XRAS, the A3M team worked with the current XSEDE and XRAS client administrators on the following features: • A Dashboard was deployed and was well received. With the Dashboard, an admin has the most recent allocation request actions that need to be processed at a glance. There is also a summary of activity in current, recent, and upcoming opportunities. • The A3M team enhanced the list of requests for an Opportunity with additional data fields to better support the Administrators’ actual needs for managing the review process. • A3M also improved the search feature on the main XRAS-Admin navigation bar by implementing a way to search for both allocation requests and people. Efforts are underway to redevelop our XSEDE Accounting Service. The team has been documenting current features and capabilities that are currently supported. Use Cases in three categories were defined: accounting, allocations, and identity. Current “pain points” were documented in order to ensure they are not recreated. The RAS team also met with XCI and XUP team members to engage them in the process and collect their feedback. Involvement with these two other XSEDE areas will continue as the redesign progresses. The team also completed a number of other tasks in the reporting period: • XRAS Review handling of PDF documents was enhanced using pdfunite to produce smaller files faster. • Also in XRAS Review, the navigation tabs for the request summary were renamed and combined in an attempt to make it easier for reviewers to understand the purpose of each navigation tab and the information displayed on relevant pages.

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• The A3M and APP teams worked together to allow Adaptive reviewers to rate their preferences for upcoming and future XRAC allocation requests. • Documentation on XRAS Admins Clients Guide continued during this period: Rules, Reports, Panels, Opportunities, Actions, Users, and Initial Set-up. • The A3M team worked jointly with the XSEDE External Relations (§9.2) team on XSEDE Directorate reports for NSF by contributing accounting data to produce those reports. For other metrics with respect to this WBS, see Appendix §12.2.2.5.2.

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9. Program Office (WBS 2.6) The purpose of the Program Office is to ensure critical project level functions are in place and operating effectively and efficiently. The oversight provided via the Project Office is necessary to provide consistent guidance and leadership to the L3 managers across the project. A common and consistent approach to managing projects and risks is provided by the Project Management, Reporting and Risk Management (PM&R) team, while Business Operations manages all financial functions and sub-awards. The crucial aspect of communications to all stakeholders is the focus of the External Relations team. Finally, Strategy, Planning, Policy, Evaluation & Organizational Improvement (SP&E) focuses attention in precisely those areas to ensure the best possible structure continues to exist within XSEDE to allow the support of all significant project activities and enable efficient and effective performance of all project responsibilities. Key Performance Indicators for the Program Office are listed in the table below. Additional information about these KPIs can be found on the XSEDE KPIs & Metrics wiki page. For other metrics with respect to this WBS, see Appendix §12.2.2.6.

Table 9-1: KPIs for Program Office. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Variance between Sustain — RY5 relevant report Operate an submission and effective and RY4 due date (days) productive virtual RY3 0 0 0 0 organization (§3.3.3) RY2 0 0 0 0 0 0

RY1 0 * NA 0 0 0 Percentage of Sustain — RY5 subaward invoices Operate an processed within effective and RY4 target duration productive virtual RY3 95/qtr 82.4 77.8 94.4 organization (§3.3.3) RY2 95/qtr 100 90.9 88.0 67.4 74.6

RY1 90/qtr * NA 100 - 100 Percentage of Sustain — RY5 recommendations Operate an addressed by effective and RY4 relevant project productive areas within 90 virtual RY3 90/yr 46 74 0 days organization (§3.3.3) RY2 90/qtr 23 15 37 49 31

RY1 90/qtr * NA 100 57 67 Aggregate mean Sustain — RY5 rating of Operate an satisfaction with innovative RY4 content and virtual accessibility of the 3.5 of organization RY3 - 3.9 - XSEDE Staff Wiki 5/yr (§3.3.4)

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Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported RY2 * * * * *

RY1 * * * * * Number of staff Sustain — RY5 publications Operate an innovative RY4 virtual organization RY3 20/yr 19 16 7 (§3.3.4) RY2 20/yr 2 6 0 1 9

RY1 70/yr * 5 0 13 18 Aggregate mean Sustain — RY5 rating of Inclusion Operate an in XSEDE innovative RY4 virtual organization RY3 4.1/yr - 4.1 - (§3.3.4) 5% RY2 improve * 4.3 * * 4.3 ment/yr RY1 * * * * * Aggregate mean Sustain — RY5 rating of Equity in Operate an XSEDE innovative RY4 virtual organization RY3 4.0/yr - 4.1 - (§3.3.4) RY2 * * * * *

RY1 * * * * * Number of XSEDE- Deepen/ RY5 related social Extend — media impressions Raise RY4 awareness 320,000/ of the value RY3 112,806 63,269 93,803 yr of advanced 300,00 digital RY2 69,607 55,506 59,490 128,180 312,783 /yr services 190,000 (§3.1.4) RY1 * 52,200 128,675 88,332 262,207 /yr Number of XSEDE- RY5 Deepen/ related media hits Extend — RY4 Raise awareness RY3 169/yr 23 29 54 of the value of advanced RY2 169/yr 42 30 44 29 145 digital services RY1 147/yr * 32 30 18 80 (§3.1.4) - Data reported annually.

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The majority of the Program Office KPI targets were met or are forecasted to meet annual targets. An exception to this is the Recommendations addressed in 90 days KPI. During this reporting period, the process and tool for tracking and communicating formal recommendations was completely reworked. Recommendations are now tracked in JIRA with automated notifications and reminders to owners and project managers. This reworked process and tool, along with the more focused attention on recommendations during the senior management calls, is expected to significantly improve the results of this KPI. Considerable work has been put into the publication initiative to increase the project’s publication contributions to the community. These efforts have resulted in exceeding the PY8 publication target in two reporting periods. The newly released XSEDE Technical Report Series and expected move to JIRA for the tracking of publications is expected to allow the project to maintain a higher level of contribution in this area. The PY9 target will be increased to reflect this expectation. We are in the process of purchasing Cision software to better track our media hits going forward, as the free tools we have been using have reported inconsistent amounts. If our media hits remain similar to what the free Newsmeter software found for this reporting period, we should meet our PY8 target. Program Office Highlights During this reporting period, four members of the XAB were rotated off the board and replaced by new members: Lisa Arafune, Coalition for Academic Scientific Computation (CASC); Ken Bloom, University of Nebraska; Toni Collis, Appentra; and Rudolph Eigenmann, University of Delaware. An XSEDE Overview session was held to help the new members better understand the project’s vision, mission, strategic goals, governance, and general operation. The XSEDE wiki (https://confluence.xsede.org/x/Y4AZ) has been updated with the new members and project overview presentations. Project Office (WBS 2.6.1) The Project Office has been established to provide the necessary oversight to ensure the greatest efficiency and effectiveness of the Program Office area and to establish responsibility for assuring advisory activities of the project occur. This oversight includes providing direction to the L3 management team and coordination of and participation in Program Office planning activities and reports through the area’s Project Manager. The Project Office also attends and supports the preparation of project-level reviews and activities. Importantly, the Project Office is responsible for ensuring that the XSEDE Advisory Board, the User Advisory Committee, and the SP Forum are functioning. The Project Office is responsible for coordination of project-level meetings such as the bi-weekly Senior Management Team (SMT) teleconference calls and the project quarterly meetings. Lastly, the Project Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. External Relations (WBS 2.6.2) External Relations’ (ER) mission is to communicate the value and importance of XSEDE to all stakeholders (including the internal audience) through creative and strategic communications. External Relations’ largest outreach effort during RP3 was coordinating an exhibition booth at SC18. XSEDE staff members volunteered to be at the booth at all times to answer questions and provide information about XSEDE. As part of ER’s SC18 efforts, it hosted two events in the XSEDE booth aimed at attracting people on the show floor to the booth. The team held a “Discover More with XSEDE” event where it publicly launched the new marketing campaign and

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had XSEDE leadership in the booth to answer in-depth questions. The other event, “Breakfast with Champions,” was an opportunity for the Campus Champions to connect, share a meal, and meet with other people in the community. Attendance was high for both events, but especially at the Breakfast with Champions event which attracted nearly 200 people. During this reporting period, ER also worked with the CEE (§4) team to promote the XSEDE EMPOWER internships. The CEE team reported a 3x increase in the number of applicants for the Spring 2019 session, which they attributed to ER’s promotional efforts. The team also developed several new print materials for SC18, which will continue to be used for the rest of the year at various recruiting events (e.g., our 6th edition of the XSEDE Highlights book). You may view and request copies of these materials here. Finally, the team concluded its market analysis of XRAS with the Illinois’ Business Consulting team and are developing further plans to implement their marketing strategies. ER exceeded the target KPI for social media impressions by over 17%, due largely to the “Discover More with XSEDE” social media campaign which ran over the holiday break. ER also exceeded the KPI target for number of media hits, likely due to an increased quantity of articles this past reporting period, some of which performed better than expected in the media. For other metrics with respect to this WBS, see Appendix §12.2.2.6.1. Project Management, Reporting, & Risk Management (WBS 2.6.3) The Project Management, Reporting & Risk Management (PM&R) team enables an effective virtual organization through the application of project management principles, provides visibility to project progress, successes, and challenges, brings new ideas and management practices into the project, and disseminates lessons learned in XSEDE to other virtual organizations. Communication is critical to success in this highly distributed virtual organization. The transition of the Senior Project Manager and L3 Manager from TACC to NCSA continues. The transition is expected to be completed with the person filling these roles identified and in-place prior to the end of the next reporting period. For other metrics with respect to this WBS, see Appendix §12.2.2.6.2. Business Operations (WBS 2.6.4) The Business Operations (BO) group, working closely with staff at the University of Illinois’ Grants and Contracts Office (GCO) and National Center for Supercomputing Applications’ (NCSA) Business Office, manages budgetary issues and sub-awards, and ensures timely processing of sub-award amendments and invoices. The sub-award invoice processing time has returned to the expected rate. Continued work focuses on more timely tracking of sub-award spend, financial analysis, and financial forecasting. The financial analysis for PY6 through PY7 was released to the Project Director and L2 Directors. This will be used by the management team to make decisions about how to adjust spending to address the current underspend. For other metrics with respect to this WBS, see Appendix §12.2.2.6.3.

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Strategy, Planning, Policy, Evaluation & Organizational Improvement (WBS 2.6.5) XSEDE dedicates effort to project-wide strategic planning, policy development, evaluation and assessment, and organizational improvement in support of sustaining an effective and productive virtual organization. XSEDE has engaged an independent Evaluation Team designed to provide XSEDE with information to guide program improvement and assess the impact of XSEDE services. Evaluations are based on five primary data sources: (1) an Annual User Survey that is part of the XSEDE annual report and program plan; (2) an Enhanced Longitudinal Study, encompassing additional target groups (e.g., faculty, institutions, disciplines, etc.) and additional measures (e.g., publications, citations, research funding, promotion and tenure, etc.); (3) an Annual XSEDE Staff Climate Study; (4) XSEDE KPIs, Area Metrics, and Organizational Improvement efforts, including ensuring that procedures are in place to assess these data; and (5) Specialized Studies as contracted by Level 2 directors and the Program Office. The 2018 Staff Climate Survey results were finalized and provided to the Project Director and each of the L2 Directors. A summary of the report was released and posted to the IDEALS repository9. The 2019 User Survey has been drafted and reviewed by the Program Director and each L2 Director. The survey will be deployed during the next reporting period. For other metrics with respect to this WBS, see Appendix §12.2.2.6.4.

9 Rivera, Lorna, Lizanne DeStefano, and Julie Wernert. 2019. The eXtreme Science and Engineering Discovery Environment 2018 XSEDE Staff Climate Study Report Executive Summary. University of Illinois. http://hdl.handle.net/2142/102745.

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10. Financial Information The XSEDE Business Operations team (§9.4) tracks and manages the financial aspect of the XSEDE project. This section conveys the financial status at a project level. The focus is on spending against the approved budget. Note that closing out any given reporting period could take up to nine months after the reporting period ends. The actual duration is dependent on the timeliness of invoice submissions by the partner institutions, plus the University of Illinois invoice processing, typically 30-45 days. The table below shows the status of reported and paid costs during the defined reporting period. The understood delay in receipt and processing of invoices results in the need to update data associated with prior reporting periods, upon the release of each IPR. The tables below show the financial summary, at a project level and a partner institution level, as of the submission of this report.

Table 10-1: Project Level Financial Summary. Invoices Reporting Period Budgeted Spent Projected (Paid/Expected) RY1 RP2: Sept '16 – Oct '16 38 of 38 $3,504,153 $2,478,940 N/A RY1 RP3: Nov '16 – Jan '17 57 of 57 $5,256,230 $4,230,094 N/A RY1 RP4: Feb '17 – Apr '17 57 of 57 $5,256,230 $5,118,021 N/A RY1 Total: Sept ’16 – Apr ‘17 152 of 152 $14,016,613 $11,813,504 N/A RY2 RP1: May '17 – Jul '17 57 of 57 $5,256,230 $5,455,524 N/A RY2 RP2: Aug '17 – Oct '17 57 of 57 $5,325,759 $5,012,569 N/A RY2 RP3: Nov '17 – Jan '18 57 of 57 $5,423,416 $4,909,549 N/A RY2 RP4: Feb '18 – April '18 57 of 57 5,423,416 $4,831,465 N/A RY2 Total: Sept '17 – April '18 228 of 228 21,428,820 $20,179,942 N/A RY3 RP1: May '18 – Jul '18 57 of 57 5,423,416 $5,911,056 N/A RY3 RP2: Aug '18 – Oct '18 48 of 57* 5,468,952 $4,099,933 $1,084,537 RY3 RP3: Nov '18 – Jan '19 15 of 57* 7,469,892 $2,261,011 $4,063,859 RY3 RP4: Feb '19 – Apr '19 RY3 Total: Sept '18 – April '19 RY4 RP1: May '19 – Jul '19 RY4 RP2: Aug '19 – Oct '19 RY4 RP3: Nov '19 – Jan '20 RY4 RP4: Feb '20 – Apr '20 RY4 Total: Sept '19 – April '20 RY5 RP1: May '20 – Jul '20 RY5 RP2: Aug '20 – Oct '20 RY5 RP3: Nov '20 – Jan '21 RY5 RP4: Feb '21 – Apr '21 Extended RY5: May '21 – Aug '21 * For these periods, not all invoices have been received. Data will be updated in subsequent reports to reflect received invoices.

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The following tables reflect received invoices and the breakdown distribution within the XSEDE Invoice Portal. The expenses reported may reflect back transfers, and we are reporting based on the month the expense posted to the grant/subaward account.

Table 10-2: Partner Institution Level Financial Summary. RY1 RP2: Sept ’16 – Oct '16 RY1 RP3: Nov ’16 – Jan ‘17 Invoice Invoices Partner s Paid Budgeted Spent Paid Budgeted Spent Institution (of 2) (of 3) NCSA 2 $733,278 $179,527 3 $1,099,917 $623,728 TACC 2 $550,304 $451,031 3 $825,457 $676,546 PSC/MPC 2 $532,169 $487,486 3 $789,253 $738,316 SDSC/UCSD 2 $463,680 $471,304 3 $695,520 $681,232 NICS/UTK 2 $286,575 $263,222 3 $429,862 $397,643 U Chicago/ANL 2 $226,231 $188,387 3 $339,346 $221,649 Indiana 2 $189,172 $122,602 3 $283,758 $293,470 University Shodor 2 $107,262 $109,862 3 $160,893 $137,963 Cornell 2 $105,112 $89,470 3 $157,668 $124,968 University NCAR/UCAR 2 $66,215 $0 3 $99,323 $69,224 Purdue 2 $52,897 $54,084 3 $79,345 $111,537 University Georgia Tech 2 $55,357 $0 3 $83,035 $13,550 SURA 2 $38,333 $31,027 3 $57,500 $55,141 OK State (OSU) 2 $33,573 $8,574 3 $50,361 $16,321 Ohio State 2 $18,367 $0 3 $27,550 $32,197 (OSC) USC-ISI 2 $13,333 $0 3 $20,000 $8,853 U Oklahoma 2 $11,261 $10,578 3 $16,892 $15,866 (OU) U Georgia 2 $10,567 $3,040 3 $15,850 $4,560 U Arkansas 2 $10,467 $8,749 3 $15,700 $16,184 Project Level 38 of 38 $3,504,153 $2,478,940 57/57 $5,256,230 $4,23946

RY1 RP4: Feb ’17 – Apr ‘17 RY1 Total: Sept ’16 – May ‘17 Invoices Invoices Partner Paid Budgeted Spent Paid Budgeted Spent Institution (of 3) (of 152) NCSA 3 $1,099,917 $1,207,527 8 $2,933,112 $2,010,781 TACC 3 $825,457 $690,974 8 $2,201,218 $1,818,550 PSC/MPC 3 $798,253 $801,638 8 $2,128,675 $2,027,439 SDSC/UCSD 3 $695,520 $711,326 8 $1,854,720 $1,863,862 NICS/UTK 3 $429,862 $406,402 8 $1,146,299 $1,067,266 U Chicago/ANL 3 $339,346 $491,477 8 $904,923 $901,513 Indiana University 3 $283,758 $256,475 8 $756,688 $672,547 Shodor 3 $160,893 $117,830 8 $429,048 $365,655 Cornell University 3 $157,668 $107,949 8 $420,448 $322,387 NCAR/UCAR 3 $99,323 $90,254 8 $264,861 $159,478 Purdue University 3 $79,345 $58,688 8 $211,587 $224,308 Georgia Tech 3 $83,035 $22,355 8 $221,427 $0 SURA 3 $57,500 $63,772 8 $153,333 $149,940

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RY1 RP4: Feb ’17 – Apr ‘17 RY1 Total: Sept ’16 – May ‘17 OK State (OSU) 3 $50,361 $14,547 8 $134,295 $39,442 Ohio State (OSC) 3 $27,550 $32,471 8 $73,467 $64,667 USC-ISI 3 $20,000 $33,141 8 $53,333 $33,141 U Oklahoma (OU) 3 $16,892 $15,867 8 $45,045 $42,310 U Georgia 3 $15,850 $4,560 8 $42,267 $12,160 U Arkansas 3 $15,700 $13,123 8 $41,867 $38,056 Project Level 57 of 57 $5,256,230 $5,140,375 152 $14,016,613 $11,813,504

RY2 RP1: May ’17 – Jul ‘17 RY2 RP2: Aug ’17 – Oct ‘17 Invoices Invoices Partner Paid Budgeted Spent Paid Budgeted Spent Institution (of 3) (of 3) NCSA 3 $1,099,917 $1,147,967 3 $1,098,678 $974,612 TACC 3 $825,457 $916,498 3 $816,652 $847,586 PSC/MPC 3 $798,253 $830,786 3 $807,749 $828,314 SDSC/UCSD 3 $695,520 $722,633 3 $705,544 $723,763 NICS/UTK 3 $429,862 $496,015 3 $459,812 $404,335 U Chicago/ANL 3 $339,346 $336,242 3 $350,002 $147,712 Indiana University 3 $283,758 $263,689 3 $277,231 $275,840 Shodor 3 $160,893 $164,529 3 $162,986 $134,506 Cornell University 3 $157,668 $152,928 3 $178,616 $125,746 NCAR/UCAR 3 $99,323 $83,082 3 $107,430 $98,807 Purdue University 3 $79,345 $68,088 3 $72,795 $70,039 Georgia Tech 3 $83,035 $62,090 3 $75,542 $107,992 SURA 3 $57,500 $59,371 3 $58,229 $62,519 OK State (OSU) 3 $50,361 $38,016 3 $54,250 $125,817 Ohio State (OSC) 3 $27,550 $33,963 3 $27,830 $25,220 USC-ISI 3 $20,000 $33,955 3 $20,300 $13,152 U Oklahoma (OU) 3 $16,892 $15,404 3 $20,455 $14,054 U Georgia 3 $15,850 $0 3 $15,888 $0 U Arkansas 3 $15,700 $17,975 3 $15,772 $15,682 Project Level 57 of 57 $5,256,230 $5,443,231 57 $5,325,759 $4,995,698

RY2 RP3: Nov ’17 – Jan ‘18 Invoices Partner Institution Paid Budgeted Spent Projected (of 3) NCSA 3 $1,154,307 $1,016,137 $0 TACC 3 $812,249 $869,772 $0 PSC/MPC 3 $812,496 $851,171 $0 SDSC/UCSD 3 $710,556 $716,848 $0 NICS/UTK 3 $474,787 $395,172 $0 U Chicago/ANL 3 $355,330 $148,934 $0 Indiana University 3 $273,968 $272,403 $0 Shodor 3 $164,032 $127,160 $0 Cornell University 3 $189,090 $144,466 $0 NCAR/UCAR 3 $111,483 $101,823 $0 Purdue University 3 $69,520 $74,499 $0 Georgia Tech 3 $78,439 $42,557 $0

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RY2 RP3: Nov ’17 – Jan ‘18 SURA 3 $58,594 $42,633 $0 OK State (OSU) 3 $56,195 $37,584 $0 Ohio State (OSC) 3 $27,970 $22,023 $0 USC-ISI 3 $20,450 $16,735 $0 U Oklahoma (OU) 3 $22,237 $13,229 $0 U Georgia 3 $15,907 $0 $0 U Arkansas 3 $15,808 $16,404 $0 Project Level 57 $5,423,416 $4,909,549 $0 RY2 RP4: Feb ’18 – Apr ‘18 Invoices Partner Institution Paid Budgeted Spent Projected (of 3) NCSA 3 $1,154,307 $935,931 $0 TACC 3 $812,249 $303,524 $0 PSC/MPC 3 $812,496 $809,751 $0 SDSC/UCSD 3 $710,556 $727,397 $0 NICS/UTK 3 $474,787 $413,610 $0 U Chicago/ANL 3 $355,330 $730,303 $0 Indiana University 3 $273,968 $258,732 $0 Shodor 3 $164,032 $128,962 $0 Cornell University 3 $189,090 $141,914 $0 NCAR/UCAR 3 $111,483 $136,090 $0 Purdue University 3 $69,520 $66,940 $0 Georgia Tech 3 $78,439 $24,414 $0 SURA 3 $58,594 $55,266 $0 OK State (OSU) 3 $56,195 $30,217 $0 Ohio State (OSC) 3 $27,970 $22,727 $0 USC-ISI 3 $20,450 $19,177 $0 U Oklahoma (OU) 3 $22,237 $13,228 $0 U Georgia 3 $15,907 $0 $0 U Arkansas 3 $15,808 $13,283 $0 Project Level 57 $5,423,416 $4,831,465 $0

RY2 Total: May ’17 – Apr ‘18 Invoices Partner Institution Paid Budgeted Spent Projected (of 12) NCSA 12 $4,507,209 $4,074,646 $0 TACC 12 $3,266,607 $2,937,380 $0 PSC/MPC 12 $3,230,994 $3,320,022 $0 SDSC/UCSD 12 $2,822,175 $2,890,641 $0 NICS/UTK 12 $1,839,249 $1,709,132 $0 U Chicago/ANL 12 $1,400,007 $1,363,192 $0 Indiana University 12 $1,108,925 $1,070,664 $0 Shodor 12 $651,942 $555,158 $0 Cornell University 12 $714,463 $565,054 $0 NCAR/UCAR 12 $429,719 $419,801 $0 Purdue University 12 $291,179 $279,566 $0 Georgia Tech 12 $315,454 $237,053 $0 SURA 12 $232,917 $219,789 $0 OK State (OSU) 12 $217,001 $231,634 $0 Ohio State (OSC) 12 $111,320 $103,934 $0 USC-ISI 12 $81,200 $83,018 $0 U Oklahoma (OU) 12 $81,822 $55,914 $0 U Georgia 12 $63,553 $0 $0 U Arkansas 12 $63,087 $63,345 $0

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RY2 Total: May ’17 – Apr ‘18 Project Level 228 $21,428,820 $20,179,942 $0 RY3 RP1: May ’18 – July ‘18 Invoices Partner Institution Paid Budgeted Spent Projected (of 3) NCSA 3 $1,154,307 $1,061,000 $0 TACC 3 $812,249 $1,371,895 $0 PSC/MPC 3 $812,496 $822,138 $0 SDSC/UCSD 3 $710,556 $709,180 $0 NICS/UTK 3 $474,787 $543,968 $0 U Chicago/ANL 3 $355,330 $278,271 $0 Indiana University 3 $273,968 $280,742 $0 Shodor 3 $164,032 $187,195 $0 Cornell University 3 $189,090 $156,765 $0 NCAR/UCAR 3 $111,483 $107,236 $0 Purdue University 3 $69,520 $64,669 $0 Georgia Tech 3 $78,439 $146,226 $0 SURA 3 $58,594 $70,674 $0 OK State (OSU) 3 $56,195 $34,666 $0 Ohio State (OSC) 3 $27,970 $21,567 $0 USC-ISI 3 $20,450 $27,463 $0 U Oklahoma (OU) 3 $22,237 $14,601 $0 U Georgia 3 $15,907 $0 $0 U Arkansas 3 $15,808 $12,801 $0 Project Level 57 $5,423,416 $5,911,056 $0

RY3 RP2: Aug ’18 – Oct ‘18 Invoices Partner Institution Paid Budgeted Spent Projected (of 3) NCSA 3 $1,171,622 $1,134,369 $0 TACC 3 $801,654 $839,725 $0 PSC/MPC 0 $838,620 $0 $810,091 SDSC/UCSD 3 $721,408 $696,515 $0 NICS/UTK 3 $461,707 $410,580 $0 U Chicago/ANL 1 $353,771 $46,874 $185,083 Indiana University 3 $269,780 $251,812 $0 Shodor 3 $173,934 $128,270 $0 Cornell University 3 $191,982 $171,593 $0 NCAR/UCAR 3 $106,323 $77,394 $0 Purdue University 3 $70,595 $72,124 $0 Georgia Tech 2 $83,434 $50,170 $31,173 SURA 2 $59,543 $41,887 $22,910 OK State (OSU) 3 $58,385 $102,256 $0 Ohio State (OSC) 3 $33,257 $26,865 $0 USC-ISI 3 $20,757 $29,259 $0 U Oklahoma (OU) 3 $22,563 $14,276 $0 U Georgia 2 $13,545 $0 $31,173 U Arkansas 2 $16,072 $5,963 $4,106 Project Level 48 $5,468,952 $4,099,933 $1,084,537

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RY3 RP3: Nov ’18 – Jan ‘18 Invoices Partner Institution Paid Budgeted Spent Projected (of 3) NCSA 0 $1,716,663.08 $1,029,286 $1,023,123 TACC 1 $1,061,807.65 $304,601 $568,824 PSC/MPC 0 $1,135,575.25 $0 $810,091 SDSC/UCSD 2 $969,112.09 $467,720 $239,465 NICS/UTK 1 $606,889.91 $120,462 $322,104 U Chicago/ANL 0 $470,655.55 $0 $277,624 Indiana University 1 $356,913.53 $88,810 $180,468 Shodor 1 $238,512.31 $45,582 $108,955 Cornell University 1 $257,903.83 $81,922 $106,709 NCAR/UCAR 2 $138,324.21 $63,736 $44,736 Purdue University 1 $94,843.88 $22,031 $45,720 Georgia Tech 0 $114,574.33 $0 $93,520 SURA 0 $80,022.33 $0 $68,731 OK State (OSU) 2 $79,307.21 $16,527 $20,725 Ohio State (OSC) 2 $47,867.33 $15,754 $8,255 USC-ISI 0 $27,880.26 $0 $29,547 U Oklahoma (OU) 1 $30,301.47 $4,581 $9,422 U Georgia 0 $21,133.50 $0 $93,520 U Arkansas 0 $21,604.67 $0 $12,319 Project Level 15 $7,469,892.40 $2,261,011 $4,063,859

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11. Project Improvement Fund The XSEDE Project Improvement Fund (PIF) is an extension of the XSEDE PY7-PY9 annual planning and budget review process to strategically invest approximately 2% of the annual budget ($465k annually) towards short-term project improvements specific to the XSEDE project. A lightweight Phase-Gate process is used to facilitate the review and prioritization of proposed project improvements by the XSEDE Senior Management Team (SMT). Funding is allocated based on this prioritization. The details of the process can be found on the XSEDE wiki PIF page. It is important to note that the annual budget for project improvement funds, while expected to be used during a specific project year, are not intended to be fully allocated during the project year planning period. The funds are allocated prior to and throughout the first half of the project year via a review process for the project to fund important ideas that bring value to the project and, in particular, to the community we support. To date, 12 Idea Submissions have been received and reviewed. The following is a summary of the current status: PY7 Project Improvement Fund Status Total PIF funds allocated: $389,000. State/Phase Submissions Comments Funded/Monitor 4 Pending Closeout Report Funded/Execute 2 Not Funded 4 Other recent efforts were similar, or the submission requested annually recurring funding Withdrawn 2 Significant overlap of two submissions Notes: • PY7 PIF funds have been reduced by $76,000, as per PCR #14, to fund the 2018 International HPC Summer School. The NSF proposal to cover the funding of the summer school was not approved by NSF, resulting in PCR #14 being triggered to ensure sufficient funding for the summer school. • The PY8 PIF funds will be reduced by approximately $138,000 to fund the 2019 International HPC Summer School This is due to the lack of NSF funding for the Summer School event. PY8 Project Improvement Fund Status Total PIF funds allocated: $70,745 State/Phase Submissions Comments Funded/Execute 3 No change for this reporting period In Process/Planning 0 No change for this reporting period The SMT will determine the allocation of the PY8 Project Improvement Funds based on the planning details provided for those submissions moved to Phase 2. Due to the lack of PIF submissions, the complete allocation plan will be extended to May 2019.

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12. Appendices Glossary and List of Acronyms ACRONYM DESCRIPTION Notes A3M Allocations, Accounting & Account Management A&AM Accounting & Account Management A&D Architecture & Design ADR Architecture Design Review AL2S Advanced Layer 2 Service Enables Internet2 users to create point-to-point VLANs AMIE Account Management Information Exchange API Application Programming Interface Area Metric A quantifiable measure that is used to track and assess the status of a specific process. Area Metrics can measure performance or operational status. Area Metrics relating to performance can be used alone or in combinations as a key performance indicator (KPI) for the project. AWS Amazon Web Services

BoF Birds of a Feather Group of community members who informally gather to discuss best practices and/or plans CaRCC Campus Research Computing Consortium

CB Campus Bridging Infrastructure to make XSEDE resources appear to be proximal to the researcher’s desktop CC Campus Champion CDPs Capability Delivery Plans CEE Community Enhancement & Engagement co-Pi Co-Principal Investigator CRI Cyberinfrastructure Resource Integration CRM Customer Relationship Management CS&E Computational Science & Engineering CSR Community Software Repository CTSC Center for Trustworthy Scientific Infrastructure DNS Domain Name Service DNSKEY Domain Name Service Key DNSSEC DNS Security DTS Data Transfer Services E&O Education and Outreach ECSS Extended Collaborative Service e-infrastructure The integration of networks, grids, data centers and collaborative environments, and are intended to include supporting operation centers, service registries, and credential delegation services.

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ER External Relations ESRT Extended Support for Research Teams ESSGW Extended Collaborative Support for Science Gateways ESTEO Extended Support for Training, Education, & Outreach FTE Full Technical Equivalent GAAMP General Automated Atomic Model Parameterization GFFS Globus Federated File System GridFTP Grid File Transfer Protocol HBCUs Historically Black Colleges and Universities HPC High Performance Computing HPCU HPC University HSI Hispanic Service Institution HSM Hardware Security Models I2 Internet2 IC Industry Challenge IdM Identity Management INCA/Nagios A service monitoring tool IPR Interim Project Report IR Incident Reports JIRA an activity tracking tool KB KB documents KPI Key Performance Indicators - A metric or combination of metrics meant to measure performance in key areas of the program so that actions and decisions which move the metrics in the desired direction also move the program in the direction of the desired outcomes and goals. L2 WBS Level 2 L3 WBS Level 3 MFC Minority Faculty Council MS Microsoft MSI Minority Serving Institution MTTR Mean Time To Resolution NCAR National Center for Atmospheric Research NCSA National Center for Supercomputing Applications NICS National Institute of Computational Science NIP Novel & Innovative Projects OSG Open Science Grid OTP One Time Password PEARC Practice & Experience in Advanced Research Computing Conference Series (www.pearc.org) PEP Program Execution Plan perfSONAR PERFormance Service Oriented Network monitoring Architecture PI Principal Investigator

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PM Project Management/Project Manager PM&R Project Management, Reporting & Risk Management POPS PACI Online Proposal System this is no longer an acronym and POPS is just the name for the allocation submission system; being supplanted by XRAS PRACE Partnership for Advanced Computing in Europe PSC Pittsburgh Supercomputing Center PY Program Year RAS Resource Allocations Service RACD Requirements Analysis & Capability Delivery RDR Resource Description Repository RESTful Representational state transfer rocks roll An open source cluster distribution solution that simplifies the processes of deploying, managing, upgrading, and scaling high-performance parallel computing clusters. RT Request Tracker Ticketing System SACNAS Society for Advancement of Chicanos and Native Americans SCxy Supercomputing Conference (e.g. SC16) SD&I Software Development & Integration SDIACT Software Development & Integration Activity SDSC San Diego Supercomputer Center SecOps Operations - Cybersecurity SH2 SH2 Security Shodor A National Resource for Computational Science Education SP Service Provider SP&E Strategy, Planning, Policy, Evaluation & Organizational Improvement STEM Science Technology Engineering Mathematics SysOps Systems Operations TACC Texas Advanced Computing Center TEOS Training, Education and Outreach Service TAS Technology Audit Service TeraGrid An e-Science grid computing infrastructure combining resources at eleven partner sites. UCCAN Canonical Use Case UCCB Campus Bridging Use Case UCDA Data Analytics Use Case UCDM Data Management Use Case UCF Federation & Interoperation Use Case UCFC First Connecting Instrumentation Use Case UCHPC High Performance Computing Use Case UCHTC High Throughput Computing Use Case

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UCSGW Science Gateway Use Case UCSW Scientific Workflow Use Case UCVIS Visualization Use Case UE User Engagement UII User Interfaces & Information URC Under-represented communities URCE Under-Represented Community Engagement UREP User Requirement Evaluation & Prioritization URM Under-Represented Minority US United States WBS Work Breakdown Structure Numerical code for each group within XSEDE XCBC XSEDE Compatible Basic Cluster Enables campus resource administrators to build a local cluster operating on open source software and compatible with XSEDE supported resources from scratch. XCI XSEDE Cyberinfrastructure Integration

XDCDB XSEDE Central Database The XDCDB contains 24 schemas, notably the accounting, resource repository, portal, and AMIE databases. XDMoD XSEDE Metrics on Demand Comprehensive HPC system management tool

XMS XD Net Metrics Services XNIT XSEDE National Integration Toolkit A suite of software modules intended for extant clusters so they are easily interoperable with XSEDE-supported resources. XOC XSEDE Operation Center XRAC XSEDE Resource Allocation Committee XRAS XSEDE Resource Allocations System XSEDE eXtreme Science and Engineering Discovery Environment XSEDE CA XSEDE Certificate Authorities Entity responsible for certifying encryption keys for identity management XSEDE KDC XSEDE Kerberos XSEDE14 XSEDE Conference in 2014 XSEDEnet an XSEDE-only network XSO XSEDE Security Officer XSP XSEDE Scholars Program XSWoG XSEDE Working Group XTED XSEDE Technology Evaluation Database XUP XSEDE User Portal The XSEDE web pages at http://xsede.org

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XWFS XSEDE Wide File System

Metrics 12.2.1. SP Resource and Service Usage Metrics To demonstrate its success and help focus management attention on areas in need of improvement, XSEDE monitors a wide range of metrics in support of different aspects of “success” for the program. The metrics presented in this section provide a view into XSEDE’s user community, including XSEDE’s success at expanding that community, the projects and allocations through which XSEDE manages access to resources, and the subsequent use of the resources by the community. Table 12-1 summarizes a few key measures of the user community, the projects and allocations, and resource utilization. Expanded information and five-year historical trends are shown in three corresponding subsections. In Q4 2018, most XSEDE user community metrics were mixed. The number of open HPC user accounts dropped below 11,000; and the number of institutions represented among the users running jobs dropped to 465. However, the number of gateway users rebounded to 15,660, and the number of active command-line users climbed back above 4,000. More details are in §12.2.1.1. Project and allocation activity held strong, with resource requests 5.7 times what was available; and the XRAC recommended support for 1.5 times what was available. More details are in §12.2.1.2. Total XSEDE-allocated resource capacity remained dipped to 19.4 Pflops (peak) with the official removal of Stanford’s Xstream system from the XSEDE-allocated portfolio. The central accounting system showed 12 compute resources reporting activity. Altogether, SP resources delivered 51.3 billion NUs of computing, an 4.1% increase over the previous quarter. More details are in §12.2.1.3.

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Table 12-1: Quarterly activity summary.

User Community Q1 2018 Q2 2018 Q3 2018 Q4 2018 Open user accounts 11,273 12,095 12,311 10,733 Active individuals 3,994 4,378 3,967 4,113 Gateway users 13,414 17,256 11,152 15,660 New user accounts 2,553 1,957 2,092 1,500 Active fields of science 37 41 41 40 Active institutions 488 516 537 465 Projects and Allocations NUs available at XRAC 59.5B 59.2B 57.9B 54.5B NUs requested at XRAC 159.6B 140.7B 186.2B 312.9B NUs recommended by XRAC 57.6B 61.7B 58.3B 83.7B NUs awarded at XRAC 58.7B 60.3B 58.3B 54.4B Open projects 1,982 1,995 2,035 2,299 Active projects 1,338 913 1,416 1,517 Active gateways 18 18 12 17 New projects 259 256 102 277 Closed projects 341 275 180 302 Resources and Usage Resources open (all types) 22 22 22 23 Total peak petaflops 18.9 18.9 18.9 19.4 Resources reporting use 12 12 11 12 Jobs reported 2.94M 3.13M 3.16M 2.99M NUs delivered 39.6B 45.3B 49.2B 51.3B

12.2.1.1. User community metrics Figure 13 shows the five-year trend in the XSEDE user community, including open user accounts, total active XSEDE users, active individual accounts, active gateway users, the number of new HPC user accounts, and the total number of new XUP accounts at the end of each quarter. The quarter had 10,733 open accounts and saw 4,113 traditional users charging jobs. The drop in open accounts may be related to the end of Stanford’s Xstream and impending end of LSU’s SuperMIC. The number of active gateway users rebounded to 15,660. Figure 14 shows the activity on XSEDE resources according to field of science across program years, including the relative fraction of PIs, open accounts, active users, allocations, and NUs used according to discipline. The figure shows the fields of science that consume ~2% or more of delivered NUs per quarter. PIs and users are counted more than once if they are associated with projects in different fields of science. The quarterly data show that the percentages of PIs and accounts associated with the “other” disciplines represent nearly 40% of all PIs, nearly 60% of direct-access user accounts, and nearly 45% of active users. Collectively the “other” fields of science represented 10% of total quarterly usage. The growth in the “Other” categories appears to have been caused in part by a large number of Training projects (82) with a very large number of open and active user accounts (over 3,000 open and over 500 active in the quarter).

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Figure 13: XSEDE user census, excluding XSEDE staff. The dramatic increases in gateway users starting in Q4 2016 is due to the I-TASSER gateway beginning to use XSEDE-allocated resources.

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Figure 14: Quarterly XSEDE user, allocation, and usage summary by field of science, in order by usage, excluding staff projects. Note: PIs and users may appear under more than one field of science. Table 12-2 and Table 12-3 highlight aspects of the broader impact of XSEDE. The former shows that graduate students, postdoctoral researchers, and undergraduates consistently make up two-thirds of the XSEDE user base. The latter table shows XSEDE’s reach into targeted institutional communities, including a substantial increase in representation from MSIs and from EPSCoR state institutions. Institutions with Campus Champions represent a large portion of usage because this table shows all users at Campus Champion institutions, not just those on the champion’s project. The table also shows XSEDE’s reach into EPSCoR states, the MSI community, and countries outside the U.S.

Table 12-2: End of quarter XSEDE open user accounts by type, excluding XSEDE staff.

Category Q1 2018 Q2 2018 Q3 2018 Q4 2018 Graduate Student 4,991 5,505 5,594 4,599 Faculty 1,903 1,986 1,999 1,965 Postdoctoral 1,328 1,329 1,347 1,182 Undergraduate Student 1,229 1,316 1,390 1,215 University Research Staff (excluding postdocs) 674 703 689 616 High school 115 115 140 152 Others 1,033 1,141 1,152 1,004 TOTALS 11,273 12,095 12,311 10,733

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Table 12-3: Active institutions in selected categories. Institutions may be in more than one category. Category Q1 2018 Q2 2018 Q3 2018 Q4 2018 Campus Sites 119 123 122 119 Champions Users 2,071 2,274 2,004 2,041 % total NUs 51% 48% 51% 49% EPSCoR Sites 81 89 86 78 states Users 505 614 634 604 % total NUs 16% 15% 16% 14% MSIs Sites 48 48 47 51 Users 253 280 276 382 % total NUs 3.3% 3.8% 2.4% 5.9% International Sites 62 76 117 67 Users 75 114 169 76 % total NUs 1% 1% 2% 1% Total Sites 488 516 537 465 Users 4,009 4,378 3,967 4,109

12.2.1.2. Project and allocation metrics

Figure 15 shows the five-year trend for requests and awards at XSEDE quarterly allocation meetings. NUs requested were 5.7x greater than NUs available, rebounding from lower levels over the previous three quarters to the highest over request level ever recorded. The XRAC recommendations were about 1.5x greater than the NUs available, requiring the reconciliation process to be invoked for the first time in 2018.

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Figure 15: Five-year allocation history, showing NUs requested, awarded, available, and recommended. Table 12-4 presents a summary of overall project activity, and Table 12-5 shows projects and activity in key project categories as reflected in allocation board type. Note that Science Gateways may appear under any board. As a special class of projects, science gateway activity is detailed in Figure 16 showing continued high levels of usage and users from these projects.

Table 12-4: Project summary metrics. Project metric Q1 2018 Q2 2018 Q3 2018 Q4 2018 XRAC requests 224 222 221 269 XRAC request success 68% 65% 70% 72% XRAC new awards 46 39 50 61 Startups requested 185 342 211 194 Startups approved 180 298 201 211 Projects new 259 256 261 275 Projects closed 341 275 153 297

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Table 12-5: Project activity by allocation board type.

Q1 2018 Q2 2018 Q3 2018 Q3 2018

Open Active % Open Active % Open Active % Open Active % projects projects NUs projects projects NUs projects projects NUs projectsprojects NUs

Campus 137 65 0.5% 142 69 0.4% 139 61 0.7% 156 65 0.3% Champions

Discretionary 2 2 0.0% 2 2 0.0% 3 1 0.0% 2 1 0.0%

Educational 142 86 1.0% 151 98 1.0% 159 93 0.8% 170 111 1.2%

Staff 15 12 0.9% 15 12 0.2% 15 12 0.3% 13 10 0.2%

Startup 913 455 3.6% 901 1 3.5% 932 1 3.0% 1115 581 3.6%

XRAC 773 718 94.1% 784 731 94.9% 787 730 95.1% 843 749 94.8%

Totals 1,982 1,338 100% 1,995 913 100% 2,035 898 100% 2,299 1,517 100%

Figure 16: Quarterly gateway usage (NUs), jobs submitted, users (reported by ECSS), registered gateways, and active gateways. 12.2.1.3. Resource and usage metrics SP systems delivered 51.4 billion NUs in Q4 2018, climbing 4% from the previous quarter. Table 12-6 breaks out the resource activity according to different resource types. Figure 17 shows the

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total NUs delivered by XSEDE-allocated SP computing systems, as reported to the central accounting system over the past five years.

Table 12-6: Resource activity, by type of resource, excluding staff projects. Note: A user will be counted for each type of resource used.

Q1 2018 Q2 2018 Q3 2018 Q4 2018

High-performance Resources 7 7 7 8 computing Jobs 1,933,545 2,283,925 2,025,736 1,665,787

Users 3,486 3,921 3,405 3,573

Nus 36,630,069,367 43,355,599,669 46,774,954,026 47,423,339,744

Data-intensive Resources 2 2 2 2 computing Jobs 126,655 82,307 117,361 121,377

Users 138 142 192 125

Nus 234,059,772 270,549,691 309,652,889 277,655,138

High-throughput Resources 1 1 1 1 computing Jobs 3,890 6,737 5,089 5,131

Users 10 15 11 10

Nus 301,101,707 248,236,516 317,900,289 638,228,195

Visualization system Resources 1 1

Jobs 7,627 34

Users 34 3

Nus 63,027,599 2,899,014

Cloud system Resources 1 1 1 1

Jobs 872,909 733,045 937,617 1,170,426

Users 496 437 535 507

Nus 2,006,128,794 1,490,930,287 1,769,595,198 2,936,149,906

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Figure 17: Total XSEDE resource usage in NUs. 12.2.1.4. Data Services XSEDE supports monitoring for the Globus data transfer service for connecting XSEDE service providers and external sites. Table 12-7 shows summary metrics and increasing Globus adoption over the past five years. Figure 18 shows the trends in Globus data transfer activity and user adoption over five years.

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Table 12-7: Globus data transfer activity to and from XSEDE endpoints, excluding XSEDE speed page user.

RY2 RP4 RY3 RP1 RY3 RP2 RY3 RP3

Files to XSEDE (millions) 47 86 100 81

TB to XSEDE 1,023 1,719 1,692 1,392 To/from XSEDE Files from XSEDE (millions) 89 77 108 92 endpoint TB from XSEDE 1,869 2,096 1,733 2,366

Users 601 627 702 622

Files to XSEDE (millions) 14 3 8 7

TB to XSEDE 109 52 60 55 To/from XSEDE Files from XSEDE (millions) 25 19 36 31 via Globus Connect TB from XSEDE 310 185 230 179

Users 413 427 505 447

TB to XSEDE 442 416 709 285 To/from TB from XSEDE 707 512 439 362 XSEDE from/to Campuses Campuses 55 60 57 52 Campus endpoints 85 86 74 67

TB to Campuses 25,916 20,191 22,674 28,897

TB from Campuses 26,846 21,384 24,645 28,959 To/from Campus Campuses 122 136 133 125

Campus endpoints 412 427 433 395

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Figure 18: Top: Aggregate Globus file and data transfer activity to and from XSEDE endpoints. Bottom: Numbers of active Globus users and campuses moving data to and from XSEDE endpoints. 12.2.2. Other Metrics 12.2.2.1. Community Engagement & Enrichment (WBS 2.1) (Gaither)

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RY3 Metrics RP1 TRP2 RP3 RP4 Total a Number of sustained users of XSEDE r resources and services via the portal 3,500/qtr 4,196 g4,089 3,099 (Project KPI) e Number of sustained underrepresented t individuals using XSEDE resources and 1,500/yr 529 509 343 services via the portal (Project KPI)

Number of new users of XSEDE resources 3,000/qtr 1,905 2,743 2,489 and services via the portal (Project KPI)

Number of new underrepresented individuals using XSEDE resources and 200/qtr 134 155 129 services via the portal (Project KPI) Number of participant hours of live training 7,259 40,000/yr 14,140 8,274 delivered by XSEDE (Project KPI) Number of students benefiting from XSEDE 1,145 resources and services through training, 1,250/qtr 1,613 1,666 XSEDE projects, or conference attendance (Area KPI) Percentage of underrepresented students 26.8 benefiting from XSEDE resources and 50/qtr 27.8 26.3 services through training, XSEDE projects, or conference attendance (Area KPI) Aggregate mean rating of training impact 4.5 for attendees registered through the portal 4.4 of 5/qtr 4.5 4.4 (Area KPI) Number of institutions with a Champion 277 250 259 266 (Area KPI)

Percentage of user requirements addressed 89 90 100 100%/qtr (36/36) within 30 days (Area KPI) (40/45) (47/52)

12.2.2.1.1. Workforce Development (WBS 2.1.2) (Houchins) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of unique attendees, 1,200/yr 721 459 468 synchronous training Number of total attendees, synchronous training 1,400/yr 752 491 498 (One person can take several classes)

Number of unique attendees, 1,200/yr 136 141 123 asynchronous training

Number of total attendees, asynchronous training (One person can take several 4,000/yr 339 411 326 classes) Aggregate mean rating of training impact for attendees registered through the 4.4 of 5 4.5 4.4 4.5 portal (Area KPI)

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of formal degree, minor, and certificate programs added to the 3/yr 0 0 1 curricula Number of materials contributed to 50/yr 18 10 7 public repository Number of materials downloaded from 62,000/ 33,217 32,819 35,267 the repository yr Number of computational science 40/yr 0 0 0 modules added to courses Number of students benefiting from XSEDE resources and services through 1,250/ 1,613 1,666 1,145 training, XSEDE projects, or conference qtr attendance (Area KPI) Percentage of underrepresented students benefiting from XSEDE resources and services through training, XSEDE 50 27.8 26.3 26.8 projects, or conference attendance (Area KPI) 12.2.2.1.2. User Engagement (WBS 2.1.3) (Snead) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Percentage of active and new PIs 100 100 100 100 (2,058) contacted quarterly (2,038) (2,467)2

Percentage of user requirements 89 90 100 100 (36/36) addressed within 30 days (Area KPI) (40/45) (47/52) Number of responses to PI emails each 143 quarter 121 138 Number of responses to each microsurvey 3181 593 NA4 Number of annual user satisfaction survey NA respondents interviewed NA NA Number of XSEDE-wide tickets 13 17 10 Number of XSEDE-wide tickets addressed 13 17 10 1 318 responses to End-User ROI Micro Survey, Data transfer microsurvey currently underway. 2 327 active users were inadvertently contacted twice during L3 management transition. 3 59 responses to XSEDE Data Transfer Service Survey. 4 No microsurveys this reporting period. NA - Survey report not available yet. 12.2.2.1.3. Broadening Participation (WBS 2.1.4) (Akli) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of new underrepresented individuals using XSEDE resources and 200/qtr 134 155 129 services via the portal (Project KPI) Number of sustained underrepresented individuals using XSEDE resources and 1,500/yr 529 5091 343 services via the portal (Project KPI) 1 Updated to correct the value.

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12.2.2.1.4. User Interfaces & Online Information (WBS 2.1.5) (Dahan) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of new users of XSEDE 3,000/ resources and services via the portal 1,095 2,743 2,489 qtr (Project KPI) Number of sustained users of XSEDE 3,500/ resources and services via the portal 4,196 4,089 3,099 qtr (Project KPI) Number of pageviews to the XSEDE 80,000/ 40,190 41,932 34,018 website qtr Number of pageviews to the XSEDE User 250,000/ 248,120 274,739 230,115 Portal qtr User satisfaction with website 4 of 5 - 4.2 - User satisfaction with User Portal 4 of 5 - 4.21 - User satisfaction with user 4 of 5 - 4.2 - documentation - Data reported annually. 1 Updated to correct the value. 12.2.2.1.5. Campus Engagement (WBS 2.1.6) (Neeman, Brunson) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of Institutions with a Champion 250 259 266 277 (Area KPI) Number of unique contributors to the Champion email list 125 151 138 128 ([email protected]) Number of activities that (i) expand the emerging CI workforce and/or (ii) improve the extant CI workforce, 40 35 45 30 participated in by members of the Campus Engagement team 12.2.2.2. Extended Collaborative Support Services (WBS 2.2) (Blood, Sinkovits) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Percentage of sustained allocation users from non-traditional disciplines of XSEDE 20/qtr 22.1 20.7 25.8 resources and services (Project KPI) Percentage of new allocation users from non-traditional disciplines of XSEDE 30/qtr 33.1 26.0 38.7 resources and services (Project KPI) Number of completed ECSS projects 45/yr 17 10 12 (ESRT + ESCC + ESSGW) (Area KPI)

Aggregate mean rating of ECSS impact by 4 of 5/yr NA 3.9 4.4 PIs (Area KPI) Aggregate mean rating of PI satisfaction 4.5 of 5/yr NA 4.3 4.8 with ECSS support (Area KPI)

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Average estimated months saved due to 12 mo/ 6 11.9 16.5 ECSS support project NA - There are no Impact and Satisfaction ratings, as ECSS recently changed the way these assessments are recorded. Future ratings obtained from the PI interviews will be validated by an anonymous survey conducted by the XSEDE evaluation team. 12.2.2.2.1. Extended Support for Research Teams (WBS 2.2.2) (Crosby) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of completed ESRT projects 27/yr 6 7 5 Average ESRT impact rating 4 of 5/yr NA 4.3 5 Average satisfaction with ESRT support 4.5 of 5/yr NA 4.4 4.5 Number of projects initiated 3 5 8 Number of projects discontinued 1 0 2 Number of PI interviews 1 5 2 Number of active projects 27 27 25 Average estimated months saved due to 12 mo/ 6 15 24 ESRT support project NA - There are no Impact and Satisfaction ratings, as ECSS recently changed the way these assessments are recorded. Future ratings obtained from the PI interviews will be validated by an anonymous survey conducted by the XSEDE evaluation team. 12.2.2.2.2. Novel & Innovative Projects (WBS 2.2.3) (Sanielevici) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of new users from non- traditional disciplines of XSEDE 500/yr 170 124 208 resources and services Number of sustained users from non- traditional disciplines of XSEDE 500/qtr 1,774 1,773 2,098 resources and services Number of new XSEDE projects from 30 28 31 25 target communities generated by NIP Number of successful XSEDE projects from target communities mentored by 25 41 48 30 NIP

12.2.2.2.3. Extended Support for Community Codes (WBS 2.2.4) (Cazes) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of completed ESCC projects 9/yr 7 1 2 Average ESCC impact rating 4 of 5/yr NA¹ 1 4 Average satisfaction with ESCC support 4.5 of 5/yr NA¹ 3.5 5 Number of projects initiated 4 1 1 Number of projects discontinued 2 0 1 Number of active projects 13 10 6

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of PI interviews 0 1 1 Average estimated months saved due to 12 mo/ NA² 0 24 ESCC support project ¹ There are no Impact and Satisfaction ratings, as ECSS recently changed the way these assessments are recorded. Future ratings obtained from the PI interviews will be validated by an anonymous survey conducted by the XSEDE evaluation team. ² There were no ESCC PI interviews conducted this period and thus, no metrics for estimated months saved. 12.2.2.2.4. Extended Support for Science Gateways (WBS 2.2.5) (Quick) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of completed ESSGW projects 9/yr 4 2 5 Average ESSGW impact rating 4 of 5/yr NA¹ 4.5 4.5 Average satisfaction with ESSGW support 4.5 of 5/ yr NA¹ 4.5 4.5 Number of projects initiated 2 0 1 Number of projects discontinued 0 0 0 Number of active projects 16 18 14 Number of PI interviews 0 2 2 Average estimated months saved due to 12 mo/ NA² 10.5 24.0 ESSGW support project ¹ There are no Impact and Satisfaction ratings, as ECSS recently changed the way these assessments are recorded. Future ratings obtained from the PI interviews will be validated by an anonymous survey conducted by the XSEDE evaluation team. ² There were no ESSGW PI interviews conducted this period and thus, no metrics for estimated months saved. 12.2.2.2.5. Extended Support for Education Outreach, & Training (WBS 2.2.6) (Alameda) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of Campus Champions fellows 4 - 5 - Average score of fellows assessment 4.5 of 5 - - -

Number of live training events staffed 20 8 9 3 Number of staff training events 2 1 0 0 Attendees at staff training events 40 32 0 0

Attendees at ECSS Symposia 300 70 46 78 Live training event contact hours 36.5 49.5 5.5 Requests for service 25 176 26

Training modules reviewed 1 0 0 Training modules produced 0 3 1 Meetings and BoFs 15 5 5

Mentoring 18 11 10

Talks and presentations 10 6 5

Education proposals reviewed 34 64 63

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- Data reported annually. 12.2.2.3. XSEDE Cyberinfrastructure Integration (WBS 2.3) (Lifka) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Total number of capabilities in 81 76 86 87 production (Project KPI)

Aggregate mean rating of satisfaction 4.5/5 4.1 4.5 4.8 with XCI services (Area KPI)

Number of non-XSEDE partnerships with 8/yr 131 121 11 XCI (Area KPI)

Mean time to issue resolution (Area KPI) 14 days 2.1 7.3 2.9

Number of new capabilities made 7 0 10 2 available for production deployment 700 by the Total number of systems that use one or end of RY2 724 813 964 more CRI provided toolkit

Percentage of Level 1 SPs that fully incorporate all of the recommended 100% - - - tools from the XSEDE Community Repository Percentage of Level 2 SPs that allocate resources through XSEDE that fully incorporate all of the recommended 100% - - - tools from the XSEDE Community Repository Percentage of Level 2 SPs that do not allocate resources through XSEDE that fully incorporate all of the recommended 100% - - - tools from the XSEDE Community Repository Percentage of Level 3 SPs that fully incorporate all of the recommended 100% - - - tools from the XSEDE Community Repository - Data reported annually 1 This number was previously underreported. 12.2.2.3.1. Requirements Analysis & Capability Delivery (WBS 2.3.2) (Navarro) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of capability delivery plans 12/yr 2 0 14 (CDPs) prepared for UREP prioritization Number of CI integration assistance 6 4 51 5 engagements Average time from support request to 30 days or 22 7 3 solution less Number of new components instrumented and tracked for usage and 4/yr 0 0 0 ROI analysis

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of significant fixes and enhancements to production 16/yr 12 15 17 components Number of maintenance releases and upgrades delivered of service provider 4/yr 1 2 1 software Number of fixes and enhancements to 12/yr 11 13 16 centrally operated services Average satisfaction rating of RACD 4 of 5/yr 3.7 4 4.9 services User rating of components delivered in 4 of 5/yr NA NA NA production Operator rating of components 4 of 5/yr 3.7 NA 4.8 delivered for production deployment Software/Service Provider rating of our 4 of 5/yr 4.5 4 5.0 integration assistance NA - No components were delivered this period. 1 This number was previously underreported. 12.2.2.3.2. Cyberinfrastructure Resource Integration (WBS 2.3.3) (Knepper) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Total number of systems that use one 700 by the 724 813 734 or more CRI provided toolkits end of RY2 User satisfaction with CRI services 4 of 5/yr 5 5 5 Number of repository subscribers to 150 107 111 112 CRI cluster and laptop toolkits

Aggregate number of TeraFLOPS of 200/yr 800 812 927 cluster systems using CRI toolkits Number of partnership interactions between CRI and SPs, national CI 12 9 7 6 organizations, and campus CI providers Toolkit updates 4/yr 4 1 3 New Toolkits released 2/yr 0 1 0 Average time from support request to <14 days * 10 5 solution * New metric beginning RP2. 12.2.2.4. XSEDE Operations (WBS 2.4) (Peterson) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Average composite availability of core services (geometric mean of critical 99.9 / qtr 99.9 99.9 99.9 services and XRAS) (Project KPI) Hours of downtime with direct user impacts from an XSEDE security 0/qtr 0 0 0 incident (Area KPI)

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Mean time to ticket resolution by XOC and WBS ticket queues (hrs) (Project <24 /qtr 12.3 18.5 23.2 KPI) Mean rating of user satisfaction with 4.5 of 5 / 4.5 4.9 4.6 tickets closed by the XOC (Area KPI) qtr 12.2.2.4.1. Cybersecurity (WBS 2.4.2) (Withers, Marsteller) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Hours of downtime with direct user 0/qtr impacts from an XSEDE security 0 0 0 incident. (Area KPI) Hours of downtime WITHOUT direct user impacts from an XSEDE (affects central service or multiple SPs) < 24 0 0 0 security incident XSEDE account exposures < 10 0 0 0 Time, beyond 24 hours, to disable XSEDE accounts 0 0 0 0 12.2.2.4.2. Data Transfer Services (WBS 2.4.3) (Boerner) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Performance (Gbps) of instrumented, 3.0 Gbps 2.7 2.5 3.0 intra-XSEDE transfers > 1GB New services added 0 0 0 Services retired 0 0 0 Total Globus Online users 627 702 622 Total new Globus Online users 190 257 150 Total transfers (Million) inbound 86 100 81 Total transfers (Million) outbound 77 108 92 Size of transfers (TBs) inbound 1,719 1,692 1,392

Size of transfers (TBs) outbound 2,096 1,733 2,366

Total number of days in which any 0 Network Interface error occurred 0 0 XSEDEnet maximum bandwidth used 23.2 (Gbps) 20.4 19.9

12.2.2.4.3. XSEDE Operations Center (WBS 2.4.4) (Pingleton) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Mean time to resolution in XOC queue < 1 0.6 0.35 .5 (hrs) Mean time to ticket resolution by XOC and WBS ticket queues (hrs) (Project < 24 12.3 18.5 23.2 KPI) User satisfaction with tickets closed by the XOC (Area KPI) 4.5 of 5 4.5 4.9 4.6 Mean time to resolution in WBS queue 26.8 49.2 59.6

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total Number of Support tickets opened for WBS queues 413 374 313 Number of Support tickets closed by WBS queues 347 338 260 Number of Support tickets opened for XOC 431 571 415 Number of Support tickets closed by XOC 431 571 415 Mean time to first response by XOC (hrs) 0.7 0.6 0.2 12.2.2.4.4. System Operations Support (WBS 2.4.5) (Rogers) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Average availability of critical enterprise services (%) [geometric 99.9 99.9 99.9 99.9 mean] (Project KPI) Total enterprise services 48 48 48 Core enterprise services 8 8 8 Services added/subtracted +2 0 0 12.2.2.5. Resource Allocation Service (WBS 2.5) (Hart) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Aggregate mean rating of user satisfaction with allocations process 4 of 5/ qtr 4.1 4.1 4.2 (Area KPI) Aggregate mean rating of user 4 of 5/ qtr 4.0 4.2 4.1 satisfaction with XRAS (Area KPI) Aggregate mean rating of user satisfaction with allocations process and 4 of 5/ yr 4.1 4.2 4.1 support services (Project KPI) Percentage of research requests 85 / qtr 65 70 72 successful (not rejected) (Project KPI) 12.2.2.5.1. XSEDE Allocations Process & Policies (WBS 2.5.2.) (Hackworth) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

User satisfaction with allocations process 4.2 4 of 5 4.1 4.1 (Area KPI) 14 calendar Average time to process Startup requests days or 8 10 10 less/ qtr

Percentage of XRAC-recommended SUs 100 97.7 100.0 64.0 allocated Percentage of research requests 85/qtr 65 70 72 successful (not rejected) (Project KPI) Continuous allocation requests processed 395 561 456

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Research allocation requests processed 222 221 269 12.2.2.5.2. Allocations, Accounting, & Account Management CI (WBS 2.5.3) (Soriano) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

User satisfaction with XRAS system (Area 4 of 5 4.0 4.2 4.1 KPI)

Availability of the XRAS systems 99.9 99.9 99.9 100.0

Number of XRAC client organizations 4 4 4 12.2.2.6. Program Office (WBS 2.6) (Payne) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Aggregate mean rating of user awareness 3.5 of 5 / of XSEDE resources and services (Project 3.7 - - yr KPI) Percent increase in social media 15 / yr 62 14 58 impressions over time (Project KPI) Aggregate mean rating of user satisfaction with XSEDE technical support services 3.5 of 5/yr 3.6 3.7 - (Project KPI) Percentage of users who indicate the use of XSEDE-managed and/or XSEDE- 79/yr - 79 - associated resources in the creation of their work product (Project KPI) Mean rating of importance of XSEDE resources and services to researcher 4.2 of 5/yr 4.4 - - productivity (Project KPI) Percentage of Project Improvement Fund proposals resulting in innovations in the 60 / yr - - - XSEDE organization (Project KPI) Mean rating of innovation within the 3.5 of 5 / - 4.0 - organization by XSEDE staff (Project KPI) yr Variance between relevant report submission and due date (days) (Area 0 0 0 0 KPI) Percentage of sub-award invoices processed within target duration (Area 95/qtr 82.4 77.8 94.4 KPI) Percentage of recommendations addressed by relevant project areas 90% 46% - 0 within 90 days (Area KPI) Aggregate mean rating of satisfaction with content and accessibility of the XSEDE 3.5 of 5/yr - 3.9 - Staff Wiki (Area KPI) Number of staff publications (Area KPI) 20/yr 19 16 7 Aggregate mean rating of Inclusion in 4.1/yr - 4.1 - XSEDE (Area KPI) Aggregate mean rating of Equity in XSEDE 4.0/yr - 4.1 - (Area KPI)

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of XSEDE-related social media 320,000/ 112,806 63,269 93,803 impressions (Area KPI) yr Number of XSEDE-related media hits 169/yr 23 29 18 (Area KPI) - Data reported annually 12.2.2.6.1. External Relations (WBS 2.6.2) (Remmert) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Number of XSEDE-related social media 320,000/ 112,806 63,269 93,803 impressions (Area KPI) yr Number of XSEDE-related media hits 54 169/yr 23 29 (Area KPI)

Open: Open: Open: Open: 32% 32.6% 29.3% 27.3% Monthly open and click-through rates of

XSEDE’s newsletter Click- Click- Click- Click- through: through: through: through: 3% 2% 2% 2% 12.2.2.6.2. Project Management, Reporting, & Risk Management (WBS 2.6.3) (Wells) RY3 Metrics Target RP1 RP2 RP3 RP4 Total Variance, in days, between relevant report submission and due date (Area 0/report 0 0 0 KPI) Aggregate mean rating of satisfaction with content and accessibility of the 3.5 of 5/yr - 3.9 - XSEDE Staff Wiki (Area KPI) Percentage of risks reviewed 100 100 100 100 <30 Average number of days to execute PCR calendar 21 15 156 days Number of total risks 156 156 131 Number of active risks 130 130 0 Number of new risks 0 1 6 Number of risks triggered 0 4 1 Number of risks retired 0 1 Risk States 126 Monitor 125 126 5 Execute Contingency 5 4 22 Total Retired 20 21 3 Inactive 6 5 7 Number of PCRs submitted 3 11 1 KPI/Metrics 1 1 0 Technical 0 0 0 Scope 0 0 0

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RY3 Metrics Target RP1 RP2 RP3 RP4 Total Budget 2 4 3 Staff 1 5 3 Other 1 1 0 - Data reported annually. 12.2.2.6.3. Business Operations (WBS 2.6.4) (Payne) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Percentage of sub-award invoices 95/qtr 82.4 77.8 94.4 processed within target duration (Area KPI) 12.2.2.6.4. Strategy, Planning, Policy, Evaluation & Organizational Improvement (WBS 2.6.5) (Payne) RY3 Metrics Target RP1 RP2 RP3 RP4 Total

Aggregate mean rating of user awareness of XSEDE resources and services (Project 3.5 of 5 / yr 3.7 - - KPI) Aggregate mean rating of user satisfaction with XSEDE technical support 3.5 of 5/yr 3.6 3.7 - services (Project KPI) Percentage of users who indicate the use of XSEDE-managed and/or XSEDE- 79/yr - 79 - associated resources in the creation of their work product1 (Project KPI) Mean rating of importance of XSEDE resources and services to researcher 4.2 of 5/yr 4.4 - - productivity (Project KPI) Percentage of Project Improvement Fund proposals resulting in innovations in the 60 / yr - - - XSEDE organization (Project KPI) Mean rating of innovation within the 3.5 of 5 / yr - 4.0 - organization by XSEDE staff (Project KPI) Percentage of recommendations addressed by relevant project areas 90 46 - 0 within 90 days (Area KPI) Number of staff publications (Area KPI) 20/yr 19 16 7 Aggregate mean rating of Inclusion in 4.1/yr - 4.1 - XSEDE (Area KPI) Aggregate mean rating of Equity in 4.0/yr - 4.1 - XSEDE (Area KPI) - Data reported annually. 1 New KPI added in RY3 RP2. Scientific Impact Metrics (SIM) and Publications Listing This appendix presents the current Scientific Impact Metrics data as of January 31, 2019. This is part of the XD Metrics Service (XMS) (previously NSF Technology Audit Service (TAS)) effort.

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12.3.1. Summary Impact Metrics Table SIM-1 shows the essential scientific summary impact metrics as of January 31, 2019. The increasing values for each metric are listed in the table indicating the changes during the last quarter. By calculating such metrics periodically, we can show the trends, as depicted in Figure SIM-2 and Figure SIM-3. Both show steadily increasing trends. Table SIM-1: Overall Scientific Impact Metrics Data

Number of i10-index (Number of externally verified Overall publications cited at h-index g-index unique citation count* least 10 times) publications* Since 2005 (TG+XD) 15,023 6,996 357,676 194 351 Since 2011 (XD) 12,536 5,160 222,181 145 252 Change from last quarter (TG+XD) +534 +344 +21,296 +5 +10 Change from last quarter (XD) +518 +337 +19,374 +7 +13 * Data updated as of January 31, 2019.

12.3.2. Historical Trend Figure SIM-2 and Figure SIM-3 show the increasing quarterly trend regarding publications, citations, and other impact metrics such as H-Index and G-Index. Both suggest the increasing impact of XSEDE during the past year, based on verified unique publication count; citation count; H-index and G-index.

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Figure SIM-2. Counts of all externally verified publications for TG/XD (since 2005) and XD (since 2011) and of those being cited at least 10 times (i10-index).

Figure SIM-3. Accumulated citation count (line, left axis) as well as h-index and g-index metrics (bar, right axis) for TG/XD (since 2005) and XD (since 2011).

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12.3.3. Publications Listing

Figure 19 shows the number of publications, conference papers, and presentations reported by XSEDE users each quarter, including the 789 reported by 204 projects in Q4 2018; these publications are listed below according to allocated project. Starting with the December 2017 XRAC meeting, all submitters must add publications to their user profiles in the XSEDE User Portal, which may have contributed to the decline at the end of 2017 and early 2018.

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Figure 19: Publications, conference papers, and presentations reported by XSEDE users. 12.3.3.1. XSEDE Staff Publications XSEDE staff reported seven publications from October to December 2018. Three publications were reported via staff members’ XSEDE User Portal user profiles, and four reports and documents were published by staff in the XSEDE Digital Object Repository (XDOR). 1. Dutta, B., V. Sharma, N. Sassu, Y. Dang, C. Weerakkody, J. Macharia, R. Miao, A. R. Howell, and S. L. Suib (2017), Cross dehydrogenative coupling of N-aryltetrahydroisoquinolines (sp3 C–H) with indoles (sp2 C–H) using a heterogeneous mesoporous manganese oxide catalyst, Green Chemistry, 19(22), 5350–5355, doi:10.1039/c7gc01919j. (published) 2. Kumar, P., V. Sharma, F. A. Reboredo, L.-M. Yang, and R. Pushpa (2016), Tunable magnetism in metal adsorbed fluorinated nanoporous graphene, Scientific Reports, 6(1), doi:10.1038/srep31841. (published) 3. Nayak, S. K., C. J. Hung, V. Sharma, S. P. Alpay, A. M. Dongare, W. J. Brindley, and R. J. Hebert (2018), Insight into point defects and impurities in titanium from first principles, npj Computational Materials, 4(1), doi:10.1038/s41524-018-0068-9. (published) 4. Wernert, Julie; DeStefano, Lizanne; Rivera, Lorna. (2018). Practice & Experience in Advanced Research Computing (PEARC18) Evaluation Report. http://hdl.handle.net/2142/102157 5. XSEDE (2018), Extreme Science and Engineering Discovery Environment (XSEDE) Highlights, 6th Edition. http://hdl.handle.net/2142/102126 6. Wernert, Julie; DeStefano, Lizanne; Rivera, Lorna. (2019). 2018 eXtreme Science and Engineering Discovery Environment (XSEDE) Annual User Satisfaction Survey. http://hdl.handle.net/2142/102280 7. (2019). XSEDE: The Extreme Science and Engineering Discovery Environment Post-XSEDE 2.0 Preliminary Transition Plan. http://hdl.handle.net/2142/102340 12.3.3.2. Publications from XSEDE Users The following publications were submitted by users to their XSEDE User Portal profiles in Q4 2018. Most publications are associated with submissions to the December 2018 meeting, while some may be from Startup or other projects. Also note that the final set of publications are associated with “XSEDE Allocation Request” and not a specific project; we will be working to

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encourage users to associate such publications with the proper XSEDE project. The publications are organized by the proposal with which they were associated. This quarter, 204 projects identified 789 publications and other products that were published, in press, accepted, submitted, or in preparation. Because these publications are submitted by users and not manually verified by XSEDE staff, there is a small chance of data entry error. 1. TG-ASC150024, TG-DBS170012 1. Howard, II, J. 2019. Phonetic Spelling Algorithm Implementations for R. (accepted) [Comet, Data Oasis, SDSC] 2. TG-ASC150046 2. Mai, U., and S. Mirarab (2018), TreeShrink: fast and accurate detection of outlier long branches in collections of phylogenetic trees, BMC Genomics, 19(S5), doi:10.1186/s12864-018-4620-2. (published) [Comet] 3. Rabiee, M., and S. Mirarab (2018), INSTRAL: Discordance-aware Phylogenetic Placement using Quartet Scores, , doi:10.1101/432906. (published) [Comet, SDSC] 4. Rabiee, M., E. Sayyari, and S. Mirarab (2018), Multi-allele species reconstruction using ASTRAL, , doi:10.1101/439489. (published) [Comet, SDSC] 5. Sarmashghi, S., K. Bohmann, M. T. P. Gilbert, V. Bafna, and S. Mirarab (2017), Assembly-free and alignment- free sample identification using genome skims, , doi:10.1101/230409. (published) [Comet, SDSC] 6. Zhang, C., M. Rabiee, E. Sayyari, and S. Mirarab (2018), ASTRAL-III: polynomial time species tree reconstruction from partially resolved gene trees, BMC Bioinformatics, 19(S6), doi:10.1186/s12859-018- 2129-y. (published) [Comet, SDSC] 7. Zhang, C., E. Sayyari, and S. Mirarab (2017), ASTRAL-III: Increased Scalability and Impacts of Contracting Low Support Branches, Lecture Notes in Computer Science, 53–75, doi:10.1007/978-3-319-67979-2_4. (published) [Comet, SDSC] 3. TG-ASC170024 8. Gong, M., Zhang, K., Huang, B., Glymour, C., Tao, D., et al. 2018. Causal Generative Domain Adaptation Networks. (published) 9. Salman, H., Yadollahpour, P., Fletcher, T., Batmanghelich, K. 2018. Deep Diffeomorphic Normalizing Flows. (published) 4. TG-ASC170047 10. Xie, J., Lu, Y., Gao, R., Wu, Y. 2018. ooperative learning of energy-basedmodel and latent variable model via MCMC teaching. AAAI (New Orleans). (published) 5. TG-ASC170047, TG-ASC170063 11. Hill, M., Erik, N., Zhu, S. 2018. Building a Telescope to Look Into High-Dimensional Image Spaces.. (accepted) 12. Nian, Y., Xie, J., Zhu, S. 2018. Sparse and deep generalizations of the FRAME model. (accepted) 13. Wu, Y., Gao, R. 2018. Learning generative ConvNets via multigrid modeling and sampling. CVPR (Salt Lake City). (published) 14. Wu, Y., Gao, R., Tian, H., Zhu, S. 2018. A tale of three probabilistic families:discriminative, descriptive and generative models. (accepted) 15. Xie, J., Lu, Y., Gao, R., Wu, Y. 2018. Cooperative training of descriptor and generator networks. (accepted) 6. TG-ASC180008 16. Huang, B., Reichman, D., Collins, L., Bradbury, K., Malof, J. 2018. Dense labeling of large remote sensing imagery with convolutional neural networks: a simple and faster alternative to stitching output label maps. (in preparation) [Bridges GPU, PSC] 7. TG-AST040034N 17. García-Segura, G., P. M. Ricker, and R. E. Taam (2018), Common Envelope Shaping of Planetary Nebulae, The Astrophysical Journal, 860(1), 19, doi:10.3847/1538-4357/aac08c. (published) [TACC]

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8. TG-AST040034N 18. Ricker, P., Taam, R., Webbink, R., Timmes, F. 2018. Common Envelope Evolution of Massive Binaries. IAU Symposium 346: High-mass X-ray binaries: illuminating the passage from massive binaries to merging compact objects (Vienna, Austria). (in preparation) [Stampede, TACC] 9. TG-AST080026N, TG-AST080028 19. Chael, A., Narayan, R., Johnson, M. 2018. Two-temperature, Magnetically Arrested Disc simulations of the jet from the supermassive black hole in M87. (published) 20. Chael, A., M. Rowan, R. Narayan, M. Johnson, and L. Sironi (2018), The role of electron heating physics in images and variability of the Galactic Centre black hole Sagittarius A*, Monthly Notices of the Royal Astronomical Society, 478(4), 5209–5229, doi:10.1093/mnras/sty1261. (published) 21. Contopoulos, I., A. Nathanail, A. Sądowski, D. Kazanas, and R. Narayan (2017), Numerical simulations of the Cosmic Battery in accretion flows around astrophysical black holes, Monthly Notices of the Royal Astronomical Society, 473(1), 721–727, doi:10.1093/mnras/stx2249. (published) 22. Guo, X., L. Sironi, and R. Narayan (2018), Electron Heating in Low Mach Number Perpendicular Shocks. II. Dependence on the Pre-shock Conditions, The Astrophysical Journal, 858(2), 95, doi:10.3847/1538- 4357/aab6ad. (published) 23. Rowan, M. E., L. Sironi, and R. Narayan (2017), Electron and Proton Heating in Transrelativistic Magnetic Reconnection, The Astrophysical Journal, 850(1), 29, doi:10.3847/1538-4357/aa9380. (published) 10. TG-AST090107 24. Brandenburg, A., S. Mathur, and T. S. Metcalfe (2017), Evolution of Co-existing Long and Short Period Stellar Activity Cycles, The Astrophysical Journal, 845(1), 79, doi:10.3847/1538-4357/aa7cfa. (published) [Science Gateways, Stampede2, TACC] 25. Judge, P. G., R. Egeland, T. S. Metcalfe, E. Guinan, and S. Engle (2017), The Magnetic Future of the Sun, The Astrophysical Journal, 848(1), 43, doi:10.3847/1538-4357/aa8d6a. (published) [Science Gateways, Stampede2, TACC] 26. Karoff, C. et al. (2018), The Influence of Metallicity on Stellar Differential Rotation and Magnetic Activity, The Astrophysical Journal, 852(1), 46, doi:10.3847/1538-4357/aaa026. (published) [Science Gateways, Stampede2, TACC] 27. Metcalfe, T., Egeland, R. 2019. Understanding the Limitations of Gyrochronology for Old Field Stars. (accepted) [Science Gateways, Stampede2, TACC] 28. Metcalfe, T. S., and J. van Saders (2017), Magnetic Evolution and the Disappearance of Sun-Like Activity Cycles, Solar Physics, 292(9), doi:10.1007/s11207-017-1157-5. (published) [Science Gateways, Stampede2, TACC] 11. TG-AST110035 29. Graus, A., Bullock, J., Kelley, T., Boylan-Kolchin, M., Garrison-Kimmel, S., et al. 2018. How low does it go? Too few Galactic satellites with standard reionization quenching. (published) 12. TG-AST120010 30. Ball, D., L. Sironi, and F. Özel (2018), Electron and Proton Acceleration in Trans-relativistic Magnetic Reconnection: Dependence on Plasma Beta and Magnetization, The Astrophysical Journal, 862(1), 80, doi:10.3847/1538-4357/aac820. (published) 13. TG-AST120046 31. Barrow, K. S. S., A. Aykutalp, and J. H. Wise (2018), Observational signatures of massive black hole formation in the early Universe, Nature Astronomy, 2(12), 987–994, doi:10.1038/s41550-018-0569-y. (published) [Maverick, Stampede2, TACC] 32. Barrow, K. S. S., J. H. Wise, A. Aykutalp, B. W. O’Shea, M. L. Norman, and H. Xu (2017), First light – II. Emission line extinction, population III stars, and X-ray binaries, Monthly Notices of the Royal Astronomical Society, 474(2), 2617–2634, doi:10.1093/mnras/stx2973. (published) [Maverick, TACC] 33. Barrow, K. S. S., J. H. Wise, M. L. Norman, B. W. O’Shea, and H. Xu (2017), First light: exploring the spectra of high-redshift galaxies in the Renaissance Simulations, Monthly Notices of the Royal Astronomical Society, 469(4), 4863–4878, doi:10.1093/mnras/stx1181. (published) [Maverick, TACC]

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14. TG-AST120060 34. Chamandy, L., A. Frank, E. G. Blackman, J. Carroll-Nellenback, B. Liu, Y. Tu, J. Nordhaus, Z. Chen, and B. Peng (2018), Accretion in common envelope evolution, Monthly Notices of the Royal Astronomical Society, 480(2), 1898–1911, doi:10.1093/mnras/sty1950. (published) [Ranch, Stampede2, TACC] 15. TG-AST120067 35. Huk, L., SNSPOL, . 2017. Time Lapse Spectropolarimetry: Constraining the Nature and Progenitors of Interacting CCSNe. American Astronomical Society Meeting Abstracts #229. 229. 308.07. (published) [Stampede, TACC] 36. Lin, A. 2018. Polarization observations of the unresolved bow shock of HD 230561. BS thesis, University of Denver. (accepted) [Stampede, TACC] 37. Lin, A., Shrestha, M., Wolfe, T., Stencel, R., Hoffman, J. 2018. Astronomy In Denver: Polarization of Stellar Wind Bow Shocks. American Astronomical Society Meeting Abstracts #232. 232. 117.05. (published) [Stampede, TACC] 38. Shrestha, M. 2018. Polarized bow shocks reveal features of the winds and environments of massive stars. Dissertation, not yet available online. (accepted) [Stampede, TACC] 39. Shrestha, M. 2018. Polarized bow shocks reveal features of the winds and environments of massive stars. American Astronomical Society Meeting Abstracts #231. 231. 424.06. (published) [Stampede, TACC] 16. TG-AST130004 40. Broderick, A., Tiede, P., Chang, P., Lamberts, A., Pfrommer, C., et al. 2018. Missing Gamma-ray Halos and the Need for New Physics in the Gamma-raySky. (published) 41. Caldwell, S., and P. Chang (2017), The accelerating pace of star formation, Monthly Notices of the Royal Astronomical Society, 474(4), 4818–4823, doi:10.1093/mnras/stx3037. (published) 42. Chang, P., and N. Murray (2017), GW170817: a neutron star merger in a mass-transferring triple system, Monthly Notices of the Royal Astronomical Society: Letters, 474(1), L12–L16, doi:10.1093/mnrasl/slx186. (published) 43. Lipnicky, A., S. Chakrabarti, and P. Chang (2018), Relating the H i gas structure of spiral discs to passing satellites, Monthly Notices of the Royal Astronomical Society, 481(2), 2590–2600, doi:10.1093/mnras/sty2330. (published) 44. Murray, D., S. Goyal, and P. Chang (2017), The effects of protostellar jet feedback on turbulent collapse, Monthly Notices of the Royal Astronomical Society, 475(1), 1023–1035, doi:10.1093/mnras/stx3153. (published) 45. Shalaby, M., A. E. Broderick, P. Chang, C. Pfrommer, A. Lamberts, and E. Puchwein (2018), Growth of Beam– Plasma Instabilities in the Presence of Background Inhomogeneity, The Astrophysical Journal, 859(1), 45, doi:10.3847/1538-4357/aabe92. (published) 46. Tiede, P., A. E. Broderick, M. Shalaby, C. Pfrommer, E. Puchwein, P. Chang, and A. Lamberts (2017), Bow Ties in the Sky. II. Searching for Gamma-Ray Halos in the Fermi Sky Using Anisotropy, The Astrophysical Journal, 850(2), 157, doi:10.3847/1538-4357/aa9375. (published) [SDSC, TACC] 17. TG-AST130017 47. Chen, Y., Heinz, S. 2018. Feedback in the Perseus Cluster: Magnetized Jets, Bubbles, and Heat Pumps. IAU Symposium 342: Perseus in Sicily: From Black Hole to Cluster Outskirts (Sicily, Italy). (accepted) [Stampede, TACC] 18. TG-AST130017, TG-AST140042 48. Chen, Y., Heinz, S. 2019. The effect of magnetic field topology in AGN jets on radio source evolution. (in preparation) 19. TG-AST140040 49. Jenkins, S., Richardson, C. 2018. Investigating the Temperature Problem in Narrow Line Emitting AGN. Poster Presentation. American Astronomical Society. (published) [Comet, SDSC] 50. Richardson, C., Kannappan, S., Moffett, A. 2018. Addressing the [O III] / Hβ offset in metal poor star forming galaxies found in the RESOLVE survey and ECO catalog. Poster Presentation. American Astronomical Society. (published) [Comet, SDSC]

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20. TG-AST140042 51. Chen, Y., Heinz, S. 2019. Cluster-scale Feedback by Radio Galaxies: The Effects of Entropy Transport. (in preparation) 21. TG-AST140044, TG-PHY130001 52. Lang, M., K. Holley-Bockelmann, and M. Sinha (2014), BAR FORMATION FROM GALAXY FLYBYS, The Astrophysical Journal, 790(2), L33, doi:10.1088/2041-8205/790/2/l33. (published) [Stampede, TACC] 22. TG-AST140080 53. Fitts, A. et al. (2018), No assembly required: mergers are mostly irrelevant for the growth of low-mass dwarf galaxies, Monthly Notices of the Royal Astronomical Society, 479(1), 319–331, doi:10.1093/mnras/sty1488. (published) 54. Garrison-Kimmel, S. et al. (2018), The origin of the diverse morphologies and kinematics of Milky Way-mass galaxies in the FIRE-2 simulations, Monthly Notices of the Royal Astronomical Society, 481(3), 4133–4157, doi:10.1093/mnras/sty2513. (published) 23. TG-AST160069 55. Barnes, D., Kannan, R., Vogelsberger, M., Pfrommer, C., Puchwein, E., et al. 2018. Enhancing AGN efficiency and cool-core formation with anisotropic thermal conduction. (published) [Stampede, TACC] 24. TG-AST170024 56. Ryan, B. 2018. General relativistic radiation magnetohydrodynamics, with applications to black hole accretion disks. University of Illinois Ph.D. Thesis of Benjamin Ryan. University of Illinois. http://hdl.handle.net/2142/101012. (published) [Stampede, TACC] 57. Ryan, B. R., S. M. Ressler, J. C. Dolence, C. Gammie, and E. Quataert (2018), Two-temperature GRRMHD Simulations of M87, The Astrophysical Journal, 864(2), 126, doi:10.3847/1538-4357/aad73a. (published) [Stampede, TACC] 25. TG-AST170025, TG-PHY060027N 58. Beachley, R. J., M. Mistysyn, J. A. Faber, S. J. Weinstein, and N. S. Barlow (2018), Accurate closed-form trajectories of light around a Kerr black hole using asymptotic approximants, Classical and Quantum Gravity, 35(20), 205009, doi:10.1088/1361-6382/aae0cd. (published) [Comet, SDSC] 26. TG-ATM100022, TG-ATM160026, TG-MCA95C006, TG-MCA95T006 59. Clark, A., Jirak, I., Dembek, S., Creager, G., Kong, F., et al. 2018. The Community Leveraged Unified Ensemble (CLUE) in the 2016 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. (published) 60. Grasso, L., Lindsey, D., Noh, Y., O’Dell, C., Kong, F. 2018. Improvements to cloud top brightness temperatures computed from the CRTM at 3.9 µm.. (accepted) 27. TG-ATM100026 61. Sugar, G., M. M. Oppenheim, Y. S. Dimant, and S. Close (2018), Formation of Plasma Around a Small Meteoroid: Simulation and Theory, Journal of Geophysical Research: Space Physics, 123(5), 4080–4093, doi:10.1002/2018ja025265. (published) [Ranch, Stampede2, TACC] 28. TG-ATM140018 62. De Sales, F., G. S. Okin, Y. Xue, and K. Dintwe (2018), On the effects of wildfires on precipitation in Southern Africa, Climate Dynamics, doi:10.1007/s00382-018-4174-7. (published) 63. Huang, H., Y. Gu, Y. Xue, J. Jiang, and B. Zhao (2018), Assessing aerosol indirect effect on clouds and regional climate of East/South Asia and West Africa using NCEP GFS, Climate Dynamics, doi:10.1007/s00382-018- 4476-9. (published) 64. Lee, J., Y. Xue, F. De Sales, I. Diallo, L. Marx, M. Ek, K. R. Sperber, and P. J. Gleckler (2018), Evaluation of multi- decadal UCLA-CFSv2 simulation and impact of interactive atmospheric-ocean feedback on global and regional variability, Climate Dynamics, doi:10.1007/s00382-018-4351-8. (published)

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65. Liu, Y., Y. Xue, G. MacDonald, P. Cox, and Z. Zhang (2018), Global vegetation variability and its response to elevated CO₂, global warming, and climate variability – a study using the offline SSiB4/TRIFFID model and satellite data, Earth System Dynamics Discussions, 1–40, doi:10.5194/esd-2018- 40. (published) 66. Qiu, B., Y. Xue, J. B. Fisher, W. Guo, J. A. Berry, and Y. Zhang (2018), Satellite Chlorophyll Fluorescence and Soil Moisture Observations Lead to Advances in the Predictive Understanding of Global Terrestrial Coupled Carbon-Water Cycles, Global Biogeochemical Cycles, 32(3), 360–375, doi:10.1002/2017gb005744. (published) 67. Xue, Y. et al. (2018), Spring Land Surface and Subsurface Temperature Anomalies and Subsequent Downstream Late Spring-Summer Droughts/Floods in North America and East Asia, Journal of Geophysical Research: Atmospheres, 123(10), 5001–5019, doi:10.1029/2017jd028246. (published) 68. Yao, T. et al. (2018), Recent Third Pole’s rapid warming accompanies cryospheric melt and water cycle intensification and interactions between monsoon and environment: multi-disciplinary approach with observation, modeling and analysis, Bulletin of the American Meteorological Society, doi:10.1175/bams-d- 17-0057.1. (published) 29. TG-ATM160014 69. Hu, J., Y. Li, T. Zhao, J. Liu, X.-M. Hu, D. Liu, Y. Jiang, J. Xu, and L. Chang (2018), An important mechanism of regional O₃ transport for summer smog over the Yangtze River Delta in East China, Atmospheric Chemistry and Physics Discussions, 1–23, doi:10.5194/acp-2018-479. (published) [SDSC] 70. Li, J., G. Wang, M. A. Mayes, S. D. Allison, S. D. Frey, Z. Shi, X. Hu, Y. Luo, and J. M. Melillo (2018), Reduced carbon use efficiency and increased microbial turnover with soil warming, Global Change Biology, doi:10.1111/gcb.14517. (published) [SDSC] 30. TG-ATM160026, TG-MCA95C006 71. Snook, N., Kong, F., Brewster, K., Xue, M., Thomas, K., et al. 2018. Evaluation of Convection- Permitting Precipitation Forecast Products using WRF, NMMB, and FV3 for the 2016-2017 NOAA Hydrometeorology Testbed Flash Flood and Intense Rainfall Experiments. (submitted) 31. TG-ATM170024 72. Pryor, S. C., R. J. Barthelmie, and T. J. Shepherd (2018), The Influence of Real-World Wind Turbine Deployments on Local to Mesoscale Climate, Journal of Geophysical Research: Atmospheres, 123(11), 5804– 5826, doi:10.1029/2017jd028114. (published) 32. TG-ATM170028 73. Ghaisas, N., Ghate, A., Lele, S. 2018. Large-eddy simulation study of multi-rotor wind turbines. Journal of Physics: Conference Series. 1037. 072021. (published) 74. Ghate, A., Ghaisas, N., Lele, S., Towne, A. 2018. Interaction of small scale Homogenenous Isotropic Turbulence with an Actuator Disk. 2018 Wind Energy Symposium. 0753. (published) 75. Ghate, A., Lele, S. 2017. Subfilter-scale enrichment of planetary boundary layer large eddy simulation using discrete Fourier--Gabor modes. (published) 76. Howland, M., Ghate, A., Lele, S. 2018. Influence of the horizontal component of Earth’s rotation on wind turbine wakes. Journal of Physics: Conference Series. 1037. 072003. (published) 33. TG-BCS170013 77. Garcia, F. E., and J. D. Bray (2019), Modeling the shear response of granular materials with discrete element assemblages of sphere-clusters, Computers and Geotechnics, 106, 99–107, doi:10.1016/j.compgeo.2018.10.003. (published) [Bridges Regular, Comet, Globus Online, LSU, PSC, Pylon, SDSC, SuperMIC] 34. TG-BCS180005, TG-CHE110014 78. Shi, Q., G. E. Sterbinsky, V. Prigiobbe, and X. Meng (2018), Mechanistic Study of Lead Adsorption on Activated Carbon, Langmuir, 34(45), 13565–13573, doi:10.1021/acs.langmuir.8b03096. (published) [Stampede2, TACC]

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35. TG-BIO150043 79. Fang, X., Monk, J., Nurk, S., Akseshina, M., Zhu, Q., et al. 2018. Metagenomics-based, strain-level analysis of Escherichia coli from a time-series of microbiome samples from a Crohn’s disease patient. (accepted) 80. Gonzalez, A. et al. (2018), Qiita: rapid, web-enabled microbiome meta-analysis, Nature Methods, 15(10), 796–798, doi:10.1038/s41592-018-0141-9. (published) 81. Janssen, S. et al. (2018), Phylogenetic Placement of Exact Amplicon Sequences Improves Associations with Clinical Information, edited by N. Chia, mSystems, 3(3), doi:10.1128/msystems.00021-18. (published) 82. McDonald, D. et al. (2018), American Gut: an Open Platform for Citizen Science Microbiome Research, edited by C. S. Greene, mSystems, 3(3), doi:10.1128/msystems.00031-18. (published) 83. McDonald, D., Vazquez-Baeza, Y., Koslicki, D., Reeve, N., Xu, Z., et al. 2018. Striped UniFrac: enabling microbiome analysis at unprecedented scale.. (accepted) 84. Mills, R., Vazquez-Baeza, Y., Zhu, Q., Gaffney, J., Humphrey, G., et al. 2019. Evaluating Metagenomic Prediction of the Metaproteome in a 4.5 Year Study of a Crohn’s Patient. (submitted) 85. Zhu, Q., Mai, U., Pfeiffer, W., Janssen, S., Sanders, J., et al. 2019. A reference phylogeny of 10,575 genomes redefines major clades of bacteria and archaea. (submitted) 36. TG-BIO160026 86. Cao, Y., E. K. Cartwright, G. Silvestri, and A. S. Perelson (2018), CD8+ lymphocyte control of SIV infection during antiretroviral therapy, edited by D. C. Douek, PLOS Pathogens, 14(10), e1007350, doi:10.1371/journal.ppat.1007350. (published) [Comet, Data Oasis, ECSS, Science Gateways, SDSC] 87. Cao, Y., X. Lei, R. M. Ribeiro, A. S. Perelson, and J. Liang (2018), Probabilistic control of HIV latency and transactivation by the Tat gene circuit, Proceedings of the National Academy of Sciences, 201811195, doi:10.1073/pnas.1811195115. (published) [Comet, Data Oasis, ECSS, Science Gateways, SDSC] 37. TG-BIO160070 88. Chen, R., Yu, H., Zhu, L. 2017. Effects of Initial Conditions on the Coalescence of Micro-bubbles. (published) 89. Sawchuk, A. P., M. Khan, A. Deb, R. L. Motaganahalli, X. Fang, F. Chen, and H. (Whitney) Yu (2018), Noninvasive and Patient-Specific Assessment of True Severity of Renal Artery Stenosis for New Guidelines for Planning Stent Therapy, Journal of Vascular Surgery, 68(3), e64–e65, doi:10.1016/j.jvs.2018.06.062. (published) 90. Yu, H., Anurag, D., Chen, X., Chen, R., Wang, D., et al. 2017. A Non-invasive Technique for Fast Assessment of Optimal LVAD Outflow Graft Implant Sites. (published) 91. Zhou, S., Y. Cao, R. Chen, T. Sun, K. Fezzaa, H. Yu, and L. Zhu (2018), Study on coalescence dynamics of unequal-sized microbubbles captive on solid substrate, Experimental Thermal and Fluid Science, 98, 362– 368, doi:10.1016/j.expthermflusci.2018.06.016. (published) 38. TG-BIO170011 92. Ackerman, A., Lodder, R. 2018. Characterization of BSN175: A Drug to Treat Prader-Willi Syndrome. (published) 39. TG-BIO170028 93. Gladstein, A. L., and M. F. Hammer (2018), Substructured population growth in the Ashkenazi Jews inferred with Approximate Bayesian Computation, , doi:10.1101/467761. (published) [Bridges Regular, Comet, IU, Jetstream, OSG, PSC, SDSC, TACC] 94. Gladstein, A. L., C. D. Quinto-Cortés, J. L. Pistorius, D. Christy, L. Gantner, and B. L. Joyce (2018), SimPrily: A Python framework to simplify high-throughput genomic simulations, SoftwareX, 7, 335–340, doi:10.1016/j.softx.2018.09.003. (published) [OSG] 40. TG-BIO170090, TG-CDA160015 95. Waldrop, L. D., Y. He, and S. Khatri (2018), What Can Computational Modeling Tell Us about the Diversity of Odor-Capture Structures in the Pancrustacea?, Journal of Chemical Ecology, 44(12), 1084–1100, doi:10.1007/s10886-018-1017-2. (published) [Bridges Regular, PSC]

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41. TG-CCR170008, TG-CIE170059 96. Puri, S., A. Paudel, and S. K. Prasad (2018), MPI-Vector-IO, Proceedings of the 47th International Conference on Parallel Processing - ICPP 2018, doi:10.1145/3225058.3225105. (published) [Comet, SDSC] 42. TG-CDA170009 97. Wilson, N. M., S. Marru, E. Abeysinghe, M. A. Christie, G. D. Maher, A. R. Updegrove, M. Pierce, and A. L. Marsden (2018), Using a Science Gateway to Deliver SimVascular Software as a Service for Classroom Instruction, Proceedings of the Practice and Experience on Advanced Research Computing - PEARC ’18, doi:10.1145/3219104.3229242. (published) 98. Yuan, V., Schiavone, N., Marsden, A. 2018. Investigting hemodynamics in the Berlin Heart EXCOR. 2018 Biomedical Engineering Society (Philadephia). (published) 43. TG-CHE040013N 99. Ahmed, T. S., J. M. Grandner, B. L. H. Taylor, M. B. Herbert, K. N. Houk, and R. H. Grubbs (2018), Metathesis and Decomposition of Fischer Carbenes of Cyclometalated Z-Selective Ruthenium Metathesis Catalysts, Organometallics, 37(14), 2212–2216, doi:10.1021/acs.organomet.8b00150. (published) 100. Barber, J. S., M. M. Yamano, M. Ramirez, E. R. Darzi, R. R. Knapp, F. Liu, K. N. Houk, and N. K. Garg (2018), Diels–Alder cycloadditions of strained azacyclic allenes, Nature Chemistry, 10(9), 953–960, doi:10.1038/s41557-018-0080-1. (published) 101. Chen, S., and K. N. Houk (2018), Origins of Stereoselectivity in Mannich Reactions Catalyzed by Chiral Vicinal Diamines, The Journal of Organic Chemistry, 83(6), 3171–3176, doi:10.1021/acs.joc.8b00037. (published) 102. Chen, S., X. Huang, E. Meggers, and K. N. Houk (2017), Origins of Enantioselectivity in Asymmetric Radical Additions to Octahedral Chiral-at-Rhodium Enolates: A Computational Study, Journal of the American Chemical Society, 139(49), 17902–17907, doi:10.1021/jacs.7b08650. (published) 103. Chen, S., Y. Zheng, T. Cui, E. Meggers, and K. N. Houk (2018), Arylketone π-Conjugation Controls Enantioselectivity in Asymmetric Alkynylations Catalyzed by Centrochiral Ruthenium Complexes, Journal of the American Chemical Society, 140(15), 5146–5152, doi:10.1021/jacs.8b00485. (published) 104. Gao, S.-S., T. Zhang, M. Garcia-Borràs, Y.-S. Hung, J. M. Billingsley, K. N. Houk, Y. Hu, and Y. Tang (2018), Biosynthesis of Heptacyclic Duclauxins Requires Extensive Redox Modifications of the Phenalenone Aromatic Polyketide, Journal of the American Chemical Society, 140(22), 6991–6997, doi:10.1021/jacs.8b03705. (published) 105. Hill, D. E., K. L. Bay, Y.-F. Yang, R. E. Plata, R. Takise, K. N. Houk, J.-Q. Yu, and D. G. Blackmond (2017), Dynamic Ligand Exchange as a Mechanistic Probe in Pd-Catalyzed Enantioselective C–H Functionalization Reactions Using Monoprotected Amino Acid Ligands, Journal of the American Chemical Society, 139(51), 18500– 18503, doi:10.1021/jacs.7b11962. (published) 106. Huang, xiongyi, M. Garcia-Borràs, K. Miao, S. B. J. Kan, A. Zutshi, K. N. Houk, and F. H. Arnold (2018), A Biocatalytic Platform for Synthesis of Chiral α-Trifluoromethylated Organoborons, , doi:10.26434/chemrxiv.7130852.v1. (published) 107. Jordan, R. S. et al. (2017), Synthesis of N = 8 Armchair Graphene Nanoribbons from Four Distinct Polydiacetylenes, Journal of the American Chemical Society, 139(44), 15878–15890, doi:10.1021/jacs.7b08800. (published) 108. Lewis, R. D., M. Garcia-Borràs, M. J. Chalkley, A. R. Buller, K. N. Houk, S. B. J. Kan, and F. H. Arnold (2018), Catalytic iron-carbene intermediate revealed in a cytochromeccarbene transferase, Proceedings of the National Academy of Sciences, 115(28), 7308–7313, doi:10.1073/pnas.1807027115. (published) 109. Li, G., M. Garcia-Borràs, M. J. L. J. Fürst, A. Ilie, M. W. Fraaije, K. N. Houk, and M. T. Reetz (2018), Overriding Traditional Electronic Effects in Biocatalytic Baeyer–Villiger Reactions by Directed Evolution, Journal of the American Chemical Society, 140(33), 10464–10472, doi:10.1021/jacs.8b04742. (published) 110. Lin, J. B., Y. Jin, S. A. Lopez, N. Druckerman, S. E. Wheeler, and K. N. Houk (2017), Torsional Barriers to Rotation and Planarization in Heterocyclic Oligomers of Value in Organic Electronics, Journal of Chemical Theory and Computation, 13(11), 5624–5638, doi:10.1021/acs.jctc.7b00709. (published) 111. Liu, F., Z. Yang, Y. Yu, Y. Mei, and K. N. Houk (2017), Bimodal Evans–Polanyi Relationships in Dioxirane Oxidations of sp3 C–H: Non-perfect Synchronization in Generation of Delocalized Radical Intermediates, Journal of the American Chemical Society, 139(46), 16650–16656, doi:10.1021/jacs.7b07988. (published) 112. Luo, S.-X., K. M. Engle, X. Dong, A. Hejl, M. K. Takase, L. M. Henling, P. Liu, K. N. Houk, and R. H. Grubbs (2018), An Initiation Kinetics Prediction Model Enables Rational Design of Ruthenium Olefin Metathesis Catalysts

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Bearing Modified Chelating Benzylidenes, ACS Catalysis, 8(5), 4600–4611, doi:10.1021/acscatal.8b00843. (published) 113. Ly, J. T., S. A. Lopez, J. B. Lin, J. J. Kim, H. Lee, E. K. Burnett, L. Zhang, A. Aspuru-Guzik, K. N. Houk, and A. L. Briseno (2018), Oxidation of rubrene, and implications for device stability, Journal of Materials Chemistry C, 6(14), 3757–3761, doi:10.1039/c7tc05775j. (published) 114. Mallinson, S. J. B. et al. (2018), A promiscuous cytochrome P450 aromatic O-demethylase for lignin bioconversion, Nature Communications, 9(1), doi:10.1038/s41467-018-04878-2. (published) 115. Newmister, S. A. et al. (2018), Structural basis of the Cope rearrangement and cyclization in hapalindole biogenesis, Nature Chemical Biology, 14(4), 345–351, doi:10.1038/s41589-018-0003-x. (published) 116. Picazo, E., S. M. Anthony, M. Giroud, A. Simon, M. A. Miller, K. N. Houk, and N. K. Garg (2018), Arynes and Cyclic Alkynes as Synthetic Building Blocks for Stereodefined Quaternary Centers, Journal of the American Chemical Society, 140(24), 7605–7610, doi:10.1021/jacs.8b02875. (published) 117. Saleem-Batcha, R., F. Stull, J. N. Sanders, B. S. Moore, B. A. Palfey, K. N. Houk, and R. Teufel (2018), Enzymatic control of dioxygen binding and functionalization of the flavin cofactor, Proceedings of the National Academy of Sciences, 115(19), 4909–4914, doi:10.1073/pnas.1801189115. (published) 118. Suh, S.-E., S. Chen, K. N. Houk, and D. M. Chenoweth (2018), The mechanism of the triple aryne–tetrazine reaction cascade: theory and experiment, Chemical Science, 9(39), 7688–7693, doi:10.1039/c8sc01796d. (published) 119. Walker, M., Chen, S., Mercado, B., Houk, K., Ellman, J. 2018. Formation of Aminocyclopentadienes from Silyldihydropyridines: Ring Contraditions Driven by Anion Stabilization. (published) 120. Xue, X.-S., B. J. Levandowski, C. Q. He, and K. N. Houk (2018), Origins of Selectivities in the Stork Diels–Alder Cycloaddition for the Synthesis of (±)-4-Methylenegermine, Organic Letters, 20(19), 6108–6111, doi:10.1021/acs.orglett.8b02548. (published) 121. Yang, Y.-F., P. Yu, and K. N. Houk (2017), Computational Exploration of Concerted and Zwitterionic Mechanisms of Diels–Alder Reactions between 1,2,3-Triazines and Enamines and Acceleration by Hydrogen- Bonding Solvents, Journal of the American Chemical Society, 139(50), 18213–18221, doi:10.1021/jacs.7b08325. (published) 122. Yang, Z., X. Dong, Y. Yu, P. Yu, Y. Li, C. Jamieson, and K. N. Houk (2018), Relationships between Product Ratios in Ambimodal Pericyclic Reactions and Bond Lengths in Transition Structures, Journal of the American Chemical Society, 140(8), 3061–3067, doi:10.1021/jacs.7b13562. (published) 123. Yang, Z., L. Zou, Y. Yu, F. Liu, X. Dong, and K. N. Houk (2018), Molecular dynamics of the two-stage mechanism of cyclopentadiene dimerization: concerted or stepwise?, Chemical Physics, 514, 120–125, doi:10.1016/j.chemphys.2018.02.020. (published) 124. Zhang, J., P. Yu, S.-Y. Li, H. Sun, S.-H. Xiang, J. (Joelle) Wang, K. N. Houk, and B. Tan (2018), Asymmetric phosphoric acid–catalyzed four-component Ugi reaction, Science, 361(6407), eaas8707, doi:10.1126/science.aas8707. (published) 125. Zhang, K., L. Cai, Z. Yang, K. N. Houk, and O. Kwon (2018), Bridged [2.2.1] bicyclic phosphine oxide facilitates catalytic γ-umpolung addition–Wittig olefination, Chemical Science, 9(7), 1867–1872, doi:10.1039/c7sc04381c. (published) 44. TG-CHE050017N, TG-CHE050018N, TG-CHE090034 126. (2017), Application of Computational Chemical Shift Prediction Techniques to the Cereoanhydride Structure Problem—Carboxylate Complications, Marine Drugs, 15(6), 171, doi:10.3390/md15060171. (published) 127. Bartels, F., Y. J. Hong, D. Ueda, M. Weber, T. Sato, D. J. Tantillo, and M. Christmann (2017), Bioinspired synthesis of pentacyclic onocerane triterpenoids, Chemical Science, 8(12), 8285–8290, doi:10.1039/c7sc03903d. (published) 128. Blümel, M., S. Nagasawa, K. Blackford, S. R. Hare, D. J. Tantillo, and R. Sarpong (2018), Rearrangement of Hydroxylated Pinene Derivatives to Fenchone-Type Frameworks: Computational Evidence for Dynamically- Controlled Selectivity, Journal of the American Chemical Society, 140(29), 9291–9298, doi:10.1021/jacs.8b05804. (published) 129. Campos, R. B., and D. J. Tantillo (2018), Coupled Electrocyclization/Prototropic Shift in the Biosynthesis of Crotinsulidane Diterpenoids, The Journal of Organic Chemistry, 83(2), 1073–1076, doi:10.1021/acs.joc.7b02904. (published) 130. Cool, L. G., K. E. Vermillion, G. R. Takeoka, S. C. Wang, and D. J. Tantillo (2018), Biosynthesis and Conformational Properties of the Irregular Sesquiterpenoids Isothapsadiene and β-Isothapsenol, The Journal of Organic Chemistry, 83(10), 5724–5730, doi:10.1021/acs.joc.8b00800. (published)

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131. Freund, G. S., T. E. O’Brien, L. Vinson, D. A. Carlin, A. Yao, W. S. Mak, I. Tagkopoulos, M. T. Facciotti, D. J. Tantillo, and J. B. Siegel (2017), Elucidating Substrate Promiscuity within the FabI Enzyme Family, ACS Chemical Biology, 12(9), 2465–2473, doi:10.1021/acschembio.7b00400. (published) 132. Hong, Y. J., and D. J. Tantillo (2018), A Maze of Dyotropic Rearrangements and Triple Shifts: Carbocation Rearrangements Connecting Stemarene, Stemodene, Betaerdene, Aphidicolene, and Scopadulanol, The Journal of Organic Chemistry, 83(7), 3780–3793, doi:10.1021/acs.joc.8b00138. (published) 133. Huang, A. C., Y. J. Hong, A. D. Bond, D. J. Tantillo, and A. Osbourn (2017), Diverged Plant Terpene Synthases Reroute the Carbocation Cyclization Path towards the Formation of Unprecedented 6/11/5 and 6/6/7/5 Sesterterpene Scaffolds, Angewandte Chemie International Edition, 57(5), 1291–1295, doi:10.1002/anie.201711444. (published) 134. Huang, A. C., S. A. Kautsar, Y. J. Hong, M. H. Medema, A. 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45. TG-CHE060069N, TG-CTS160013, TG-DMR150127, TG-PHY070010T 148. Fan, X., Y. Liu, S. Chen, J. Shi, J. Wang, A. Fan, W. Zan, S. Li, W. A. Goddard, and X.-M. Zhang (2018), Defect- enriched iron fluoride-oxide nanoporous thin films bifunctional catalyst for water splitting, Nature Communications, 9(1), doi:10.1038/s41467-018-04248-y. (published) 149. Fan, X., Y. Liu, S. Chen, J. Shi, J. Wang, A. Fan, W. Zan, S. Li, W. A. Goddard, and X.-M. Zhang (2018), Defect- enriched iron fluoride-oxide nanoporous thin films bifunctional catalyst for water splitting, Nature Communications, 9(1), doi:10.1038/s41467-018-04248-y. (published) 150. Qian, J., Q. An, A. Fortunelli, R. J. Nielsen, and W. A. Goddard (2018), Reaction Mechanism and Kinetics for Ammonia Synthesis on the Fe(111) Surface, Journal of the American Chemical Society, 140(20), 6288–6297, doi:10.1021/jacs.7b13409. (published) 151. Qian, J., Q. An, A. Fortunelli, R. J. Nielsen, and W. A. Goddard (2018), Reaction Mechanism and Kinetics for Ammonia Synthesis on the Fe(111) Surface, Journal of the American Chemical Society, 140(20), 6288–6297, doi:10.1021/jacs.7b13409. (published) 152. Shin, H., H. Xiao, and W. A. Goddard (2018), In Silico Discovery of New Dopants for Fe-Doped Ni Oxyhydroxide (Ni1–xFexOOH) Catalysts for Oxygen Evolution Reaction, Journal of the American Chemical Society, 140(22), 6745–6748, doi:10.1021/jacs.8b02225. (published) 153. Wang, C. et al. (2018), Monolayer atomic crystal molecular superlattices, Nature, 555(7695), 231–236, doi:10.1038/nature25774. (published) 154. Wang, C. et al. (2018), Monolayer atomic crystal molecular superlattices, Nature, 555(7695), 231–236, doi:10.1038/nature25774. (published) 155. Wang, Y. et al. (2018), Field-effect transistors made from solution-grown two-dimensional tellurene, Nature Electronics, 1(4), 228–236, doi:10.1038/s41928-018-0058-4. (published) 156. Wang, Y. et al. (2018), Field-effect transistors made from solution-grown two-dimensional tellurene, Nature Electronics, 1(4), 228–236, doi:10.1038/s41928-018-0058-4. (published) 157. Xiao, H., H. Shin, and W. A. Goddard (2018), Synergy between Fe and Ni in the optimal performance of (Ni,Fe)OOH catalysts for the oxygen evolution reaction, Proceedings of the National Academy of Sciences, 115(23), 5872–5877, doi:10.1073/pnas.1722034115. (published) 158. Yang, S., Liu, Y., Hao, Y., Yang, X., Goddard, W., et al. 2018. Oxygen-Vacancy Abundant Ultrafine Co3O4 Graphene Composites for High-Rate Supercapacitor Electrodes. DOI:10.1002/advs.201700659 (Invalid?). (published) 159. Yang, S., Liu, Y., Hao, Y., Yang, X., Goddard, W., et al. 2018. Oxygen-Vacancy Abundant Ultrafine Co3O4 Graphene Composites for High-Rate Supercapacitor Electrodes. DOI:10.1002/advs.201700659 (Invalid?). (published) 46. TG-CHE120052 160. Patel, N. D. et al. (2018), Computationally Assisted Mechanistic Investigation and Development of Pd- Catalyzed Asymmetric Suzuki–Miyaura and Negishi Cross-Coupling Reactions for Tetra-ortho-Substituted Biaryl Synthesis, ACS Catalysis, 8(11), 10190–10209, doi:10.1021/acscatal.8b02509. (published) [Comet, SDSC] 161. Qu, B. et al. (2018), Enantioselective Synthesis of α-(Hetero)aryl Piperidines through Asymmetric Hydrogenation of Pyridinium Salts and Its Mechanistic Insights, Organic Letters, 20(5), 1333–1337, doi:10.1021/acs.orglett.8b00067. (published) [Comet, SDSC] 162. Sha, S.-C., S. Tcyrulnikov, M. Li, B. Hu, Y. Fu, M. C. Kozlowski, and P. J. Walsh (2018), Cation−π Interactions in the Benzylic Arylation of Toluenes with Bimetallic Catalysts, Journal of the American Chemical Society, 140(39), 12415–12423, doi:10.1021/jacs.8b05143. (published) [Comet, SDSC] 163. Zatolochnaya, O. V. et al. (2018), Copper-catalyzed asymmetric hydrogenation of 2-substituted ketones via dynamic kinetic resolution, Chemical Science, 9(19), 4505–4510, doi:10.1039/c8sc00434j. (published) [Comet, SDSC] 164. Zou, Y., O. Gutierrez, A. C. Sader, N. D. Patel, D. R. Fandrick, C. A. Busacca, K. R. Fandrick, M. Kozlowski, and C. H. Senanayake (2017), A Computational Investigation of the Ligand-Controlled Cu-Catalyzed Site-Selective Propargylation and Allenylation of Carbonyl Compounds, Organic Letters, 19(22), 6064–6067, doi:10.1021/acs.orglett.7b02845. (published) [Gordon, SDSC]

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47. TG-CHE130047 165. Ali, G., P. E. VanNatta, D. A. Ramirez, K. M. Light, and M. T. Kieber-Emmons (2017), Thermodynamics of a μ- oxo Dicopper(II) Complex for Hydrogen Atom Abstraction, Journal of the American Chemical Society, 139(51), 18448–18451, doi:10.1021/jacs.7b10833. (published) [Comet, Gordon, SDSC] 48. TG-CHE130099 166. Chakraborty, K., K. Vijayan, A. E. X. Brown, D. E. Discher, and S. M. Loverde (2018), Glassy worm-like micelles in solvent and shear mediated shape transitions, Soft Matter, 14(20), 4194–4203, doi:10.1039/c8sm00080h. (published) 167. Kang, M., K. Chakraborty, and S. M. Loverde (2018), Molecular Dynamics Simulations of Supramolecular Anticancer Nanotubes, Journal of Chemical Information and Modeling, 58(6), 1164–1168, doi:10.1021/acs.jcim.8b00193. (published) 168. Lin, Y.-A., M. Kang, W.-C. Chen, Y.-C. Ou, A. G. Cheetham, P.-H. Wu, D. Wirtz, S. M. Loverde, and H. Cui (2018), Isomeric control of the mechanical properties of supramolecular filament hydrogels, Biomaterials Science, 6(1), 216–224, doi:10.1039/c7bm00722a. (published) 169. Manandhar, A., M. Kang, K. Chakraborty, and S. M. Loverde (2018), Effect of Nucleotide State on the Protofilament Conformation of Tubulin Octamers, The Journal of Physical Chemistry B, 122(23), 6164–6178, doi:10.1021/acs.jpcb.8b02193. (published) 49. TG-CHE130116 170. Fu, G., H. Zheng, Y. He, W. Li, X. Lü, and H. He (2018), Efficient near-infrared (NIR) polymer light-emitting diodes (PLEDs) based on heteroleptic iridium(iii) complexes with post-modification effects of intramolecular hydrogen bonding or BF2-chelation, Journal of Materials Chemistry C, doi:10.1039/c8tc03432j. (published) [Comet, SDSC] 50. TG-CHE140009 171. Mandal, T., and R. G. Larson (2018), Stretch and Breakage of Wormlike Micelles under Uniaxial Strain: A Simulation Study and Comparison with Experimental Results, Langmuir, 34(42), 12600–12608, doi:10.1021/acs.langmuir.8b02421. (published) [Stampede2, TACC] 172. Marson, R. L., Y. Huang, M. Huang, T. Fu, and R. G. Larson (2018), Inertio-capillary cross-streamline drift of droplets in Poiseuille flow using dissipative particle dynamics simulations, Soft Matter, 14(12), 2267–2280, doi:10.1039/c7sm02294h. (published) [Comet, SDSC] 173. Zhang, W., Larson, R. 2018. Tension-induced nematic phase separation in bidisperse homopolymer melt. (submitted) [Stampede2] 174. Zhang, W., and R. G. Larson (2018), Direct All-Atom Molecular Dynamics Simulations of the Effects of Short Chain Branching on Polyethylene Oligomer Crystal Nucleation, Macromolecules, 51(13), 4762–4769, doi:10.1021/acs.macromol.8b00958. (published) [Stampede2, TACC] 51. TG-CHE140065 175. Qi, R., L.-P. Wang, Q. Wang, V. S. Pande, and P. Ren (2015), United polarizable multipole water model for molecular mechanics simulation, The Journal of Chemical Physics, 143(1), 014504, doi:10.1063/1.4923338. (published) 52. TG-CHE140101 176. Fales, B. S., S. Seritan, N. F. Settje, B. G. Levine, H. Koch, and T. J. Martínez (2018), Large-Scale Electron Correlation Calculations: Rank-Reduced Full Configuration Interaction, Journal of Chemical Theory and Computation, 14(8), 4139–4150, doi:10.1021/acs.jctc.8b00382. (published) [Maverick, Standford, TACC, XStream] 177. Yang, C. et al. (2018), Impact of Stokes Shift on the Performance of Near-Infrared Harvesting Transparent Luminescent Solar Concentrators, Scientific Reports, 8(1), doi:10.1038/s41598-018-34442-3. (published) [Maverick, Standford, TACC, XStream] 53. TG-CHE140109 178. Goulas, K. A., Y. Song, G. R. Johnson, J. P. Chen, A. A. Gokhale, L. C. Grabow, and F. D. Toste (2018), Selectivity tuning over monometallic and bimetallic dehydrogenation catalysts: effects of support and particle size,

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58. TG-CHE160031 193. Abubekerov, M., V. Vlček, J. Wei, M. E. Miehlich, S. M. Quan, K. Meyer, D. Neuhauser, and P. L. Diaconescu (2018), Exploring Oxidation State-Dependent Selectivity in Polymerization of Cyclic Esters and Carbonates with Zinc(II) Complexes, iScience, 7, 120–131, doi:10.1016/j.isci.2018.08.020. (published) [Gordon, Stampede] 59. TG-CHE160037 194. Schure, M., Maier, R. 2018. Ellipsoidal particles for liquid chromatography: fluid mechanics, efficiency and wall effects. (accepted) [Stampede, TACC] 60. TG-CHE160044 195. Gahlon, H. L., A. R. Walker, G. A. Cisneros, M. H. Lamers, and D. S. Rueda (2018), Reduced structural flexibility for an exonuclease deficient DNA polymerase III mutant, Physical Chemistry Chemical Physics, 20(42), 26892–26902, doi:10.1039/c8cp04112a. (published) 61. TG-CHE160065, TG-DMR180027 196. Adams, J., Suh, K., Metz, C., Gurgis, G., Clayborne, A. 2018. Quantum Chemical and Spectroscopic Investigations of Si3(NH)3X6 and Si3(NH)3R6 (X= F, Cl, NH3; R = H, CH3, CH2CH3) Cyclic Clusters. Conference Abstract. https://www.frontiersin.org/10.3389/conf.fchem.2018.01.00033/event_abstract?sname=National_Organiz ation_for_the_Professional_Advancement_of_Black_Chemists_and_Chemical_Engineers_(NO. (published) [Bridges Regular, PSC, Training] 62. TG-CHE170025 197. Penchoff, D., Peterson, C., Auxier, J., Schweitzer, G., Harrison, R., et al. 2018. Structural analysis, complexation, and binding selectivity of lanthanides and actinides: A theoretical investigation. American Chemical Society Annual Meeting (New Orleans, LA). NUCL. 66. (published) [Stampede, Stampede2, TACC] 198. Penchoff, D. A., C. C. Peterson, J. P. Camden, J. A. Bradshaw, J. D. Auxier, G. K. Schweitzer, D. M. Jenkins, R. J. Harrison, and H. L. Hall (2018), Structural Analysis of the Complexation of Uranyl, Neptunyl, Plutonyl, and Americyl with Cyclic Imide Dioximes, ACS Omega, 3(10), 13984–13993, doi:10.1021/acsomega.8b02068. (published) [Stampede, Stampede2, TACC] 199. Penchoff, D. A., C. C. Peterson, M. S. Quint, J. D. Auxier, G. K. Schweitzer, D. M. Jenkins, R. J. Harrison, and H. L. Hall (2018), Structural Characteristics, Population Analysis, and Binding Energies of [An(NO3)]2+ (with An = Ac to Lr), ACS Omega, 3(10), 14127–14143, doi:10.1021/acsomega.8b01800. (published) [Stampede, Stampede2, TACC] 200. Penchoff, D., Peterson, C., Quint, M., Powers-Luhn, J., Auxier, J., et al. 2018. Experimental and theoretical analysis of selective binding of lanthanides and actinides. American Chemical Society Annual Meeting (Boston). NUCL. 23. (published) [Stampede, Stampede2, TACC] 63. TG-CHE170034 201. Dey, G., L. Yang, K.-B. Lee, and L. Wang (2018), Characterizing Molecular Adsorption on Biodegradable MnO2 Nanoscaffolds, The Journal of Physical Chemistry C, 122(50), 29017–29027, doi:10.1021/acs.jpcc.8b09562. (published) [Bridges Regular, Pylon] 202. Yang, L., S.-T. D. Chueng, Y. Li, M. Patel, C. Rathnam, G. Dey, L. Wang, L. Cai, and K.-B. Lee (2018), A biodegradable hybrid inorganic nanoscaffold for advanced stem cell therapy, Nature Communications, 9(1), doi:10.1038/s41467-018-05599-2. (published) 64. TG-CHE170059 203. Jiang, H., P. G. Debenedetti, and A. Z. Panagiotopoulos (2018), Communication: Nucleation rates of supersaturated aqueous NaCl using a polarizable force field, The Journal of Chemical Physics, 149(14), 141102, doi:10.1063/1.5053652. (published) 204. Jiang, H., A. Haji-Akbari, P. G. Debenedetti, and A. Z. Panagiotopoulos (2018), Forward flux sampling calculation of homogeneous nucleation rates from aqueous NaCl solutions, The Journal of Chemical Physics, 148(4), 044505, doi:10.1063/1.5016554. (published)

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65. TG-CHE170062 205. DeLyser, M., Noid, W. 2019. Grand canonical formalism for bottom-up coarse-grained models of interfaces. (in preparation) [Comet, SDSC] 206. Lebold, K., Noid, W. 2018. Systematic study of temperature and density variations in effective potentials for coarse-grained models of molecular liquids. (submitted) [Comet, SDSC] 207. Lebold, K., Noid, W. 2019. Communication: Bottom-up coarse-grained models that reproduce structure and energies. (in preparation) [Comet, SDSC] 66. TG-CHE170089 208. Hooker, L., Neufeldt, S. 2018. Ligation state of nickel during C—O bond activation with monodentate phosphines. (accepted) [Comet, SDSC] 67. TG-CHE170090 209. Forsythe, R., J. P. Scheifers, Y. Zhang, and B. P. T. Fokwa (2018), HT-NbOsB: Experimental and Theoretical Investigations of a Boride Structure Type Containing Boron Chains and Isolated Boron Atoms, European Journal of Inorganic Chemistry, 2018(28), 3297–3303, doi:10.1002/ejic.201800235. (published) 210. Jothi, R., Zhang, Y., Yubuta, K., Culver, D., Conley, M., et al. 2018. Abundant vanadium diboride with graphene- like boron layers for hydrogen evolution. (submitted) 211. Radzieowski, M., Block, T., Klenner, S., Zhang, Y., Fokwa, B., et al. 2018. Synthesis, Crystal and Electronic Structure, Physical Properties and 121Sb and 151Eu Mössbauer Spectroscopy of the Eu14AlPn11 series (Pn = As, Sb). (submitted) 212. Zhang, Y., Fokwa, B. 2018. Catalytic Activity of Metal Diborides for Hydrogen Evolution Reaction—A Density Functional Theory Investigation. Conference presentation. (published) 68. TG-CHE180011, TG-CHE180053 213. Lamim Ribeiro, J. M., and P. Tiwary (2018), Toward Achieving Efficient and Accurate Ligand-Protein Unbinding with Deep Learning and Molecular Dynamics through RAVE, Journal of Chemical Theory and Computation, 15(1), 708–719, doi:10.1021/acs.jctc.8b00869. (published) [Bridges GPU, Bridges Regular] 214. Ribeiro, J. M. L., S.-T. Tsai, D. Pramanik, Y. Wang, and P. Tiwary (2018), Kinetics of Ligand–Protein Dissociation from All-Atom Simulations: Are We There Yet?, Biochemistry, 58(3), 156–165, doi:10.1021/acs.biochem.8b00977. (published) [Bridges GPU, Bridges Regular] 215. Smith, Z., D. Pramanik, S.-T. Tsai, and P. Tiwary (2018), Multi-dimensional spectral gap optimization of order parameters (SGOOP) through conditional probability factorization, The Journal of Chemical Physics, 149(23), 234105, doi:10.1063/1.5064856. (published) [Bridges GPU, Bridges Regular, PSC] 69. TG-CHE180041 216. Rodriquez, D. et al. (2018), Measurement of Cohesion and Adhesion of Semiconducting Polymers by Scratch Testing: Effect of Side-Chain Length and Degree of Polymerization, ACS Macro Letters, 7(8), 1003–1009, doi:10.1021/acsmacrolett.8b00412. (published) 70. TG-CHE180056 217. Ascough, D. M. H., F. Duarte, and R. S. Paton (2018), Stereospecific 1,3-H Transfer of Indenols Proceeds via Persistent Ion-Pairs Anchored by NH···π Interactions, Journal of the American Chemical Society, 140(48), 16740–16748, doi:10.1021/jacs.8b09874. (published) [Bridges Regular, Comet, PSC, SDSC] 218. Franchino, A., J. Chapman, I. Funes-Ardoiz, R. S. Paton, and D. J. Dixon (2018), Catalytic Enantio- and Diastereoselective Mannich Addition of TosMIC to Ketimines, Chemistry - A European Journal, 24(67), 17660–17664, doi:10.1002/chem.201804099. (published) [Bridges Regular, Comet, PSC, SDSC] 219. Hilton, M. C., X. Zhang, B. T. Boyle, J. V. Alegre-Requena, R. S. Paton, and A. McNally (2018), Heterobiaryl synthesis by contractive C–C coupling via P(V) intermediates, Science, 362(6416), 799–804, doi:10.1126/science.aas8961. (published) [Bridges Regular, Comet, PSC, SDSC] 71. TG-CIE160013 220. Graves, S., Keiser, K. 2018. EarthCube Science-Drive Workbench Infrastructure. EarthCube All Hands Meeting (Alexandria, VA). (published) [Jetstream, TACC]

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221. Mahabal, A., Crichton, D., Djorgovski, S., Law, E., John, S. 2017. From Sky to Earth: Data Science Methodology Transfer in Astroinformatics. (published) [Jetstream, TACC] 222. Xia, J., Liu, C., Gui, Z., Huang, Q., Li, R. 2015. Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives.. (published) [Jetstream, TACC] 223. Yang, C., Li, Z., Liu, K., Hu, F. 2016. Big Data and Cloud Computing: Innovation Opportunities and Challenges. (published) [Jetstream, TACC] 72. TG-CIE170005 224. Dang, H.-V., R. Dathathri, G. Gill, A. Brooks, N. Dryden, A. Lenharth, L. Hoang, K. Pingali, and M. Snir (2018), A Lightweight Communication Runtime for Distributed Graph Analytics, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), doi:10.1109/ipdps.2018.00107. (published) [Stampede2, TACC] 225. Dathathri, R., G. Gill, L. Hoang, H.-V. Dang, A. Brooks, N. Dryden, M. Snir, and K. Pingali (2018), Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics, Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2018, doi:10.1145/3192366.3192404. (published) [Bridges GPU, Bridges Regular, PSC, Pylon, Stampede2, TACC] 73. TG-CIE170007 226. Das, A., Rad, P., Choo, K., Nouhi, B., Lish, J., et al. 2019. Distributed machine learning cloud teleophthalmology IoT for predicting AMD disease progression. (published) [Jetstream] 74. TG-CIE170026 227. Adrien Le Franc, ., Riebling, E., Karadayi, J., Wang, Y., Scaff, C., et al. 2018. The ACLEW DiViMe: An easy-to-use diarization tool. Proc. INTERSPEECH. (published) [Bridges Regular, PSC] 75. TG-CTS030026N 228. Balogh, P., and P. Bagchi (2018), Analysis of red blood cell partitioning at bifurcations in simulated microvascular networks, Physics of Fluids, 30(5), 051902, doi:10.1063/1.5024783. (published) [Stampede, TACC] 229. Campbell, E. J., and P. Bagchi (2018), A computational model of amoeboid cell motility in the presence of obstacles, Soft Matter, 14(28), 5741–5763, doi:10.1039/c8sm00457a. (published) [Stampede, TACC] 76. TG-CTS090025 230. Nguyen, Q., and D. Papavassiliou (2018), Quality Measures of Mixing in Turbulent Flow and Effects of Molecular Diffusivity, Fluids, 3(3), 53, doi:10.3390/fluids3030053. (published) [Stampede, Stampede2, TACC] 231. Papavassiliou, D., Pham, N., Voronov, R. 2017. Lattice Boltzmann methods for bioengineering applications. (M. Cerrolaza, S. Shefelbine and D. Garzon-Alvarado , eds.Elsevier: Cambridge, MA. Chapter 23. ISBN: 9780128117187. (published) [Lonestar, Stampede, TACC] 77. TG-CTS090025 232. Nguyen, Q., and D. V. Papavassiliou (2018), Scalar mixing in anisotropic turbulent flow, AIChE Journal, 64(7), 2803–2815, doi:10.1002/aic.16104. (published) [Stampede, Stampede2, TACC] 233. Papavassiliou, D., Feher, S., Nguyen, Q. 2017. Lagrangian Modeling of Turbulent Dispersion from Instantaneous Point Sources at the Center of a Turbulent Flow Channel. DOI:10.3390/fluids2030046 (Invalid?). (published) [Stampede, Stampede2, TACC] 234. Pham, N. H., and D. V. Papavassiliou (2017), Effect of spatial distribution of porous matrix surface charge heterogeneity on nanoparticle attachment in a packed bed, Physics of Fluids, 29(8), 082007, doi:10.1063/1.4999344. (published) [Stampede, Stampede2, TACC] 235. Pham, N. H., and D. V. Papavassiliou (2018), Hydrodynamic effects on the aggregation of nanoparticles in porous media, International Journal of Heat and Mass Transfer, 121, 477–487, doi:10.1016/j.ijheatmasstransfer.2017.12.150. (published) [Stampede, Stampede2, TACC]

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236. Vu, T. V., and D. V. Papavassiliou (2018), Oil-water interfaces with surfactants: A systematic approach to determine coarse-grained model parameters, The Journal of Chemical Physics, 148(20), 204704, doi:10.1063/1.5022798. (published) [Stampede, Stampede2, TACC] 78. TG-CTS100078 237. Huang, D., R. Ma, T. Zhang, and T. Luo (2018), Origin of Hydrophilic Surface Functionalization-Induced Thermal Conductance Enhancement across Solid–Water Interfaces, ACS Applied Materials & Interfaces, 10(33), 28159–28165, doi:10.1021/acsami.8b03709. (published) 238. Wei, X., and T. Luo (2018), The effect of the block ratio on the thermal conductivity of amorphous polyethylene–polypropylene (PE–PP) diblock copolymers, Physical Chemistry Chemical Physics, 20(31), 20534–20539, doi:10.1039/c8cp03433h. (published) 239. Wei, X., T. Zhang, and T. Luo (2016), Chain conformation-dependent thermal conductivity of amorphous polymer blends: the impact of inter- and intra-chain interactions, Physical Chemistry Chemical Physics, 18(47), 32146–32154, doi:10.1039/c6cp06643g. (published) 240. Wei, X., T. Zhang, and T. Luo (2017), Molecular Fin Effect from Heterogeneous Self-Assembled Monolayer Enhances Thermal Conductance across Hard–Soft Interfaces, ACS Applied Materials & Interfaces, 9(39), 33740–33748, doi:10.1021/acsami.7b07169. (published) 241. Wei, X., T. Zhang, and T. Luo (2017), Thermal Energy Transport across Hard–Soft Interfaces, ACS Energy Letters, 2(10), 2283–2292, doi:10.1021/acsenergylett.7b00570. (published) 242. Zhang, T., A. R. Gans-Forrest, E. Lee, X. Zhang, C. Qu, Y. Pang, F. Sun, and T. Luo (2016), Role of Hydrogen Bonds in Thermal Transport across Hard/Soft Material Interfaces, ACS Applied Materials & Interfaces, 8(48), 33326–33334, doi:10.1021/acsami.6b12073. (published) 243. Zhang, T., and T. Luo (2015), Giant Thermal Rectification from Polyethylene Nanofiber Thermal Diodes, Small, 11(36), 4657–4665, doi:10.1002/smll.201501127. (published) 79. TG-CTS130034 244. Lee, J. et al. (2018), Spatial and temporal variations in hemodynamic forces initiate cardiac trabeculation, JCI Insight, 3(13), doi:10.1172/jci.insight.96672. (published) 245. Liu, J., and A. L. Marsden (2018), A unified continuum and variational multiscale formulation for fluids, solids, and fluid–structure interaction, Computer Methods in Applied Mechanics and Engineering, 337, 549– 597, doi:10.1016/j.cma.2018.03.045. (published) 246. Schiavazzi, D. E., T. Y. Hsia, and A. L. Marsden (2016), On a sparse pressure-flow rate condensation of rigid circulation models, Journal of Biomechanics, 49(11), 2174–2186, doi:10.1016/j.jbiomech.2015.11.028. (published) 247. Vedula, V., J. Lee, H. Xu, C.-C. J. Kuo, T. K. Hsiai, and A. L. Marsden (2017), A method to quantify mechanobiologic forces during zebrafish cardiac development using 4-D light sheet imaging and computational modeling, edited by S. L. Diamond, PLOS Computational Biology, 13(10), e1005828, doi:10.1371/journal.pcbi.1005828. (published) 248. Verma, A., M. Esmaily, J. Shang, R. Figliola, J. A. Feinstein, T.-Y. Hsia, and A. L. Marsden (2018), Optimization of the Assisted Bidirectional Glenn Procedure for First Stage Single Ventricle Repair, World Journal for Pediatric and Congenital Heart Surgery, 9(2), 157–170, doi:10.1177/2150135117745026. (published) 80. TG-CTS130035 249. Johnson, L. C., B. J. Landrum, and R. N. Zia (2018), Yield of reversible colloidal gels during flow start-up: release from kinetic arrest, Soft Matter, 14(24), 5048–5068, doi:10.1039/c8sm00109j. (published) [Ranch, Stampede, Stampede2, TACC] 250. Johnson, L., Moghimi, E., Petekidis, G., Zia, R. 2018. Influence of structure on the linear response rheology of colloidal gels . (submitted) [Ranch, Stampede, Stampede2, TACC] 251. Padmanabhan, P., and R. Zia (2018), Gravitational collapse of colloidal gels: non-equilibrium phase separation driven by osmotic pressure, Soft Matter, 14(17), 3265–3287, doi:10.1039/c8sm00002f. (published) [Ranch, Stampede, TACC] 81. TG-CTS140009 252. He, Y., and S. Laursen (2018), The surface and catalytic chemistry of the first row transition metal phosphides in deoxygenation, Catalysis Science & Technology, 8(20), 5302–5314, doi:10.1039/c8cy01134f. (published)

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253. Poudyal, S., Laursen, S. 2018. Photocatalytic CO2 Reduction by H2O: Insights from Modeling Electronically Relaxed Mechanisms. (submitted) 254. Song, Y., Laursen, S. 2018. Control of Surface Reactivity towards Unsaturated C-C Bonds and H over Ni-based Intermetallic Compounds in Semi-hydrogenation of Acetylene . (submitted) 82. TG-CTS150030 255. Chen, R., H. W. Yu, L. Zhu, R. M. Patil, and T. Lee (2016), Spatial and temporal scaling of unequal microbubble coalescence, AIChE Journal, 63(4), 1441–1450, doi:10.1002/aic.15504. (published) 256. Rou Chen, Whitney Yu, Yousheng Xu, and Luoding Zhu. (2018). Scalings of inverse energy transfer and energy decay in 3-D decaying isotropic turbulence with non-rotating or rotating frame of reference. Journal of Applied and Computational Mechanics, (Online First). https://doi.org/10.22055/jacm.2018.26826.1361 (published) 83. TG-CTS150057 257. Akhade, S. A., R. M. Nidzyn, G. Rostamikia, and M. J. Janik (2018), Using Brønsted-Evans-Polanyi relations to predict electrode potential-dependent activation energies, Catalysis Today, 312, 82–91, doi:10.1016/j.cattod.2018.03.048. (published) [Stampede, TACC] 258. Chen, X., I. T. McCrum, K. A. Schwarz, M. J. Janik, and M. T. M. Koper (2017), Co-adsorption of Cations as the Cause of the Apparent pH Dependence of Hydrogen Adsorption on a Stepped Platinum Single-Crystal Electrode, Angewandte Chemie International Edition, 56(47), 15025–15029, doi:10.1002/anie.201709455. (published) [Stampede, TACC] 259. Gao, D., McCrum, I., Deo, S., Choi, Y., Scholten, F., et al. 2018. Activity and selectivity control in CO2 electroreduction over CuOx catalysts via electrolyte design. (accepted) [Stampede, TACC] 260. Gong, L., Y. Mu, and M. J. Janik (2018), Mechanistic roles of catalyst surface coating in nitrobenzene selective reduction: A first-principles study, Applied Catalysis B: Environmental, 236, 509–517, doi:10.1016/j.apcatb.2018.05.015. (published) [Stampede, TACC] 261. Jonayat, A., Chen, S., van Duin, A., Janik, M. 2018. Predicting Monolayer Oxide Stability over Low-index Surfaces of TiO2 Polymorphs Using ab Initio Thermodynamics. (accepted) [Stampede, TACC] 262. Jonayat, A. S. M., A. Kramer, L. Bignardi, P. Lacovig, S. Lizzit, A. C. T. van Duin, M. Batzill, and M. J. Janik (2018), A first-principles study of stability of surface confined mixed metal oxides with corundum structure (Fe2O3, Cr2O3, V2O3), Physical Chemistry Chemical Physics, 20(10), 7073–7081, doi:10.1039/c8cp00154e. (published) [Stampede, TACC] 263. Kumar, G., L. Tibbitts, J. Newell, B. Panthi, A. Mukhopadhyay, R. M. Rioux, C. J. Pursell, M. Janik, and B. D. Chandler (2018), Evaluating differences in the active-site electronics of supported Au nanoparticle catalysts using Hammett and DFT studies, Nature Chemistry, 10(3), 268–274, doi:10.1038/nchem.2911. (published) [Stampede, TACC] 264. Kumar, G., T. Van Cleve, J. Park, A. van Duin, J. W. Medlin, and M. J. Janik (2018), Thermodynamics of Alkanethiol Self-Assembled Monolayer Assembly on Pd Surfaces, Langmuir, 34(22), 6346–6357, doi:10.1021/acs.langmuir.7b04351. (published) [Stampede, TACC] 265. Maheshwari, S., Rostamikia, G., Janik, M. 2018. Elementary kinetics of nitrogen electroreduction on Fe surfaces. (accepted) [Stampede, TACC] 266. McCrum, I. T., X. Chen, K. A. Schwarz, M. J. Janik, and M. T. M. Koper (2018), Effect of Step Density and Orientation on the Apparent pH Dependence of Hydrogen and Hydroxide Adsorption on Stepped Platinum Surfaces, The Journal of Physical Chemistry C, 122(29), 16756–16764, doi:10.1021/acs.jpcc.8b03660. (published) [Stampede, TACC] 267. McCrum, I. T., M. A. Hickner, and M. J. Janik (2018), Quaternary Ammonium Cation Specific Adsorption on Platinum Electrodes: A Combined Experimental and Density Functional Theory Study, Journal of The Electrochemical Society, 165(2), F114–F121, doi:10.1149/2.1351802jes. (published) [Stampede, TACC] 84. TG-CTS150062 268. Kanani, Y., S. Acharya, and F. Ames (2019), Large Eddy Simulation of the Laminar Heat Transfer Augmentation on the Pressure Side of a Turbine Vane Under Freestream Turbulence, Journal of Turbomachinery, 141(4), 041004, doi:10.1115/1.4041599. (published) [Comet, SDSC] 269. Kanani, Y., S. Acharya, and F. Ames (2018), LES Study of the Laminar Heat Transfer Augmentation on the Pressure Side of a Turbine Vane Under Freestream Turbulence, Volume 5C: Heat Transfer, doi:10.1115/gt2018-77135. (published) [Comet, SDSC]

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270. Kanani, Y., S. Acharya, and F. Ames (2018), Simulations of Slot Film-Cooling With Freestream Acceleration and Turbulence, Journal of Turbomachinery, 140(4), 041005, doi:10.1115/1.4038877. (published) [Comet, SDSC] 85. TG-CTS160008 271. Khare, K. 2018. Thermomechanics in Cross-linked Epoxy: Quantitative Comparison between Atomistic Simulation and Experiment. Symposium on Thermophysical Properties (Boulder, CO). (accepted) [Stampede2, TACC] 272. Khare, K., Phelan, F. 2017. Do atomistic simulations quantitatively capture molecular mechanisms underlying linear viscoelasticity in cross-linked epoxy networks?. American Chemical Society (Washington, DC). (accepted) [Stampede2, TACC] 273. Khare, K., Phelan Jr., F. 2017. Surfactant Chemistry and Counter-Ion Clouds in Bile Salt Stabilized SWCNTs. American Institute of Chemical Engineers National Meeting (Minneapolis, MN). (accepted) [Stampede2, TACC] 274. Khare, K., Phelan Jr., F. 2018. Quantitative Comparison of Atomistic Simulations with Experiment for Cross- linked Epoxy. Mach Conference 2018 (Annapolis, MD). (accepted) [Stampede2, TACC] 275. Khare, K., Phelan Jr., F. 2018. Time–Temperature Superposition in a Cross-linked Epoxy: Linking Dynamics from Atomistic Simulation with Mechanics from Experiment. (submitted) [Stampede2, TACC] 276. Khare, K., Phelan Jr., F. 2018. Atomistic Simulation of Volumetric and Dynamic Properties of Glycerol During Glass Transition. (in preparation) [Stampede2, TACC] 277. Khare, K., Phelan Jr., F. 2018. Time−Temperature Superposition for Integration of Atomistic Simulations with Experiment for Thermomechanical Properties of Cross-Linked Epoxy. American Institute of Chemical Engineers National Meeting (Pittsburg, PA). (accepted) [Stampede2, TACC] 278. Khare, K., Phelan Jr., F. 2018. Bridging the Vast Mismatch in the Time Scale of Atomistic Simulations and Experiment for Cross-linked Epoxy Using Time-Temperature Superposition. American Chemical Society Meeting (Boston, MA). (accepted) [Stampede2, TACC] 279. Khare, K., Phelan Jr., F. 2018. Probing Heterogeneities in Orientational Dynamics of Cross-linked Epoxy using Atomistic Simulations. (in preparation) [Stampede2, TACC] 280. Khare, K., Phelan Jr., F. 2018. Comparing Dynamics via Atomistic Simulations with Mechanics via Experiment: Utility of the Time-Temperature Superposition Principle. American Physical Society March Meeting (Los Angeles, CA). (accepted) [Stampede2, TACC] 281. Khare, K., Phelan Jr., F. 2018. Integration of Translational Dynamics from Atomistic Simulations with Mechanics from Experiment for a Cross-linked Epoxy. (in preparation) [Stampede2, TACC] 86. TG-CTS160024 282. Yuan, J. 2017. Effects of surface roughness topography in developed and transient channel flows . 10th International Symposium on Turbulence and Shear Flow Phenomena (TSFP10) (Chicago, IL). (published) 283. Yuan, J., and M. Aghaei Jouybari (2018), Topographical effects of roughness on turbulence statistics in roughness sublayer, Physical Review Fluids, 3(11), doi:10.1103/physrevfluids.3.114603. (published) 87. TG-CTS160041 284. Almithn, A., Hibbitts, D. 2018. Supra-monolayer coverages on small metal clusters and their effects on H2 chemisorption particle size estimates. DOI:10.1002/aic.16110 (Invalid?). (published) [Stampede2, TACC] 285. Almithn, A., and D. Hibbitts (2018), Effects of Catalyst Model and High Adsorbate Coverages in ab Initio Studies of Alkane Hydrogenolysis, ACS Catalysis, 8(7), 6375–6387, doi:10.1021/acscatal.8b01114. (published) [Stampede2, TACC] 286. Nystrom, S., A. Hoffman, and D. Hibbitts (2018), Tuning Brønsted Acid Strength by Altering Site Proximity in CHA Framework Zeolites, ACS Catalysis, 8(9), 7842–7860, doi:10.1021/acscatal.8b02049. (published) [Stampede2, TACC] 88. TG-CTS170021 287. Shen, Z., H. Ye, X. Yi, and Y. Li (2018), Membrane Wrapping Efficiency of Elastic Nanoparticles during Endocytosis: Size and Shape Matter, ACS Nano, 13(1), 215–228, doi:10.1021/acsnano.8b05340. (published) [Stampede2, TACC]

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288. Ye, H., Z. Shen, and Y. Li (2018), Interplay of deformability and adhesion on localization of elastic micro- particles in blood flow, Journal of Fluid Mechanics, 861, 55–87, doi:10.1017/jfm.2018.890. (published) [Stampede, Stampede2, TACC] 89. TG-CTS170030 289. Yan, Y., Chen, C., Yang, F. 2018. DNS on the Evolution of Vortices in the Upper Boundary layer in Transition. (published) 290. Yan, Y., Chen, C., Yang, F., Cotton, H. 2018. Investigation of Shock-Boundary Layer Interaction in a Ramp Flow with MVG Under Different Turbulent Inflows. (published) 90. TG-CTS170045 291. Chew, A., Van Lehn, R. 2018. Effects of core morphology on the structural asymmetry of alkanethiol monolayer-protected gold nanoparticles. (submitted) [Stampede2, TACC] 292. Dallin, B., Yeon, H., Ostwalt, A., Abbott, N., Van Lehn, R. 2018. Molecular order affects interfacial water structure and temperature-dependent hydrophobic interactions between nonpolar self-assembled monolayers. (submitted) [Comet, SDSC] 293. Patel, S., Van Lehn, R. 2018. Characterizing the molecular mechanisms for flipping charged peptide flanking loops across a lipid bilayer. (submitted) [Comet, SDSC] 294. Sheavly, J., Pedersen, J., Van Lehn, R. 2018. Curvature-driven adsorption of cationic nanoparticles to phase boundaries in multicomponent lipid bilayers. (submitted) [Stampede2, TACC] 91. TG-CTS170047 295. Nabil, M., Rattner, A. 2018. LES simulation of turbulent supercritical CO2 heat transfer in microchannels. 6th International Supercritical CO2 Power Cycles Symposium (Pittsburgh, PA). (published) [Comet] 296. Nabil, M., Rattner, A. 2018. Heat transfer performance of heated upward turbulent supercritical CO2 flow in a microchannel: a numerical study. 6th Micro and Nano Flows Conference (Atlanta, GA). (published) [Comet] 92. TG-CTS170051 297. Alshwairekh, A., Alghafis, A., Alwatban, A., Alqsair, U., Oztekin, A. 2018. The effects of membrane and channel corrugations in forward osmosis membrane modules - Numerical analyses. (submitted) [Bridges Regular, PSC] 298. Alshwairekh, A., Alghafis, A., Alwatban, A., Alqsair, U., Oztekin, A. 2019. The effect of embedded spacers on forward osmosis membrane systems performance - A CFD simulation. (in preparation) [Bridges Regular, PSC] 299. Altimemy, M., Daskiran, C., Attiya, B., Liu, I., Oztekin, A. 2018. Pressure Fluctuation Mitigation in a Francis Turbine With Water Injection – Computational Study. ASME 2018 International Mechanical Engineering Congress and Exposition (David L. Lawrence Convention Center, Pittsburgh, PA). (accepted) [Bridges Regular, PSC] 300. Altimemy, M., Daskiran, C., Attiya, B., Liu, I., Oztekin, A. 2018. Pressure Fluctuation Mitigation in a Francis Turbine with Water Injection – Computational Study. International Mechanical Engineering Congress and Exposition (Pittsburg, PA). (published) 301. Altimemy, M., Daskiran, C., Attiya, B., Liu, I., Oztekin, A. 2019. Francis turbine vibration reduction using large eddy simulation. (in preparation) [Bridges Regular, PSC] 302. Anqi, A., Usta, M., Krysko, R., Oztekin, A. 2019. Numerical Characterization of Vacuum Membrane Distillation Module Performance – Transient Analysis. (in preparation) [Bridges Regular, PSC] 303. Attiya, B., Daskiran, C., Altimemy, M., Liu, I., Oztekin, A. 2018. Large Eddy Simulations of Multiphase Flows Past a Finite Plate near a Free Surface. (submitted) [Bridges Regular, Comet, PSC] 304. Attiya, B., Daskiran, C., Altimemy, M., Liu, I., Oztekin, A. 2019. Large Eddy Simulations of Multiphase Flows Past a Finite Plate Near a Free Surface. (submitted) 305. Attiya, B., Liu, I., Altimemy, M., Daskiran, C., Oztekin, A. 2018. Investigation of Three-Dimensional Lagrangian Coherent Structures in Flow past Single and Arrays of Plate – Linear Energy Harvesting Applications. ASME 2018 International Mechanical Engineering Congress and Exposition (David L. Lawrence Convention Center, Pittsburgh, PA). (accepted) [Bridges Regular, Comet, PSC] 306. Attiya, B., Liu, I., Altimemy, M., Daskiran, C., Oztekin, A. 2018. Investigation of three dimensional lagrangian coherent structures in flow past single and arrays of plate – Linear Energy Harvesting Applications. International Mechanical Engineering Congress and Exposition (Pittsburg, PA). (accepted)

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307. Attiya, B., Liu, I., Altimemy, M., Daskiran, C., Oztekin, A. 2019. Vortex Identification in Turbulent Flows Past Plates using Lagrangian Method. (accepted) [Comet, PSC] 308. Daskiran, C., Attiya, B., Altimemy, M., Liu, I., Oztekin, A. 2018. Large Eddy Simulation of Ventilated Pump- Turbine for Wastewater Treatment. ASME 2018 International Mechanical Engineering Congress and Exposition (David L. Lawrence Convention Center, Pittsburgh, PA). (published) [Bridges Regular, PSC] 309. Daskiran, C., B. Attiya, M. Altimemy, I.-H. Liu, and A. Oztekin (2019), Oxygen dissolution via pump-turbine – Application to wastewater treatment, International Journal of Heat and Mass Transfer, 131, 1052–1063, doi:10.1016/j.ijheatmasstransfer.2018.11.130. (published) [Bridges Regular, PSC] 310. Daskiran, C., B. Attiya, J. Riglin, and A. Oztekin (2018), Large eddy simulations of ventilated micro hydrokinetic turbine at design and off-design operating conditions, Ocean Engineering, 169, 1–18, doi:10.1016/j.oceaneng.2018.09.008. (published) [Bridges Regular, PSC] 311. Dong, C., Cheng, X., Zhang, X., Oztekin, A., Webb III, E. 2018. A probabilistic coarse-grain monomer model for human glycoprotein von Willebrand Factor. (in preparation) [Bridges Regular, PSC, Pylon] 312. Dong, C., Lee, J., Kim, S., Lai, W., Webb, E., et al. 2018. Long-ranged Protein-glycan Interactions Stabilize von Willebrand Factor A2 Domain from Mechanical Unfolding. (accepted) [Bridges Large, Comet, PSC] 313. Kania, S., Morabito, M., Dong, C., Cheng, X., Zhang, X., et al. 2018. Computing hydrodynamic drag on coarse- grain models of complex polymers. (in preparation) [Bridges Regular, PSC, Pylon] 314. Luo, D., Xue, G., Oztekin, A., Cheng, X. 2018. Nanoparticle Focusing by Coupling Thermophoresis and Engineered Vortex in Microfluidic Device. (submitted) 315. Morabito, M., C. Dong, W. Wei, X. Cheng, X. F. Zhang, A. Oztekin, and E. Webb (2018), Internal Tensile Force and A2 Domain Unfolding of von Willebrand Factor Multimers in Shear Flow, Biophysical Journal, 115(10), 1860–1871, doi:10.1016/j.bpj.2018.09.001. (published) 316. Morabito, M., Usta, M., Webb III, E., Oztekin, A. 2018. Prediction of Sub-Monomer A2 Domain Dynamics of the von Willebrand Factor by Random Forest Algorithm and Coarse-Grained Molecular Dynamics Simulation. (submitted) [Bridges Regular, PSC] 317. Usta, M., Daskiran, C., Hakim, A., Cosman, S., Oztekin, A. 2019. Steady Three-Dimensional Axial Flows Past Hollow Fiber Membrane Arrays. (in preparation) [Bridges Regular, PSC] 318. Usta, M., Krysko, R., Anqi, A., Alshwairekh, A., Oztekin, A. 2018. The Effect of PTFE Membrane Properties on Vacuum Membrane Distillation Module Performance. ASME 2018 International Mechanical Engineering Congress and Exposition (David L. Lawrence Convention Center, Pittsburgh, PA). (accepted) [Bridges Regular, PSC] 319. Usta, M., Krysko, R., Oztekin, A. 2019. Characterization of Subgrid-Scale Model Uncertainties in Large-Eddy Simulations of Passive Scalar Transport by DNS simulations. (in preparation) [Bridges Regular, PSC] 320. Usta, M., M. Morabito, M. Alrehili, A. Hakim, and A. Oztekin (2018), Steady three-dimensional flows past hollow fiber membrane arrays – cross flow arrangement, Canadian Journal of Physics, 96(12), 1272–1287, doi:10.1139/cjp-2017-0914. (published) [Bridges Regular, PSC] 321. Usta, M., M. Morabito, A. Anqi, M. Alrehili, A. Hakim, and A. Oztekin (2018), Twisted hollow fiber membrane modules for reverse osmosis-driven desalination, Desalination, 441, 21–34, doi:10.1016/j.desal.2018.04.027. (published) 322. Usta, M., Tosyali, A. 2018. Characterization of Model-Based Uncertainties in Incompressible Turbulent Flows by Machine Learning. ASME 2018 International Mechanical Engineering Congress and Exposition (David L. Lawrence Convention Center, Pittsburgh, PA). (accepted) [Bridges Regular, PSC] 323. Wei, W., C. Dong, M. Morabito, X. Cheng, X. F. Zhang, E. B. Webb, and A. Oztekin (2018), Coarse-Grain Modeling of Shear-Induced Binding between von Willebrand Factor and Collagen, Biophysical Journal, 114(8), 1816–1829, doi:10.1016/j.bpj.2018.02.017. (published) 324. Xue, G., Cheng, X., Oztekin, A. 2019. Transient Simulations of Nanoparticle Focusing using Thermophoresis in Three-Dimensional Grooved Microfluidics Channel. (in preparation) [Bridges Regular, PSC] 325. Zhou, W., Shi, B., Webb III, E. 2018. Nano-scale particle size effect on the capillary forces during nano- suspension droplet wetting. (in preparation) [Bridges Regular, Comet, Data Oasis, PSC, Pylon, SDSC] 326. Zhou, W., Webb III, E. 2018. Exploring capillary forces during nano-suspension droplet wetting via molecular dynamics calculations. (in preparation) [Bridges Regular, Comet, Data Oasis, PSC, Pylon, SDSC] 93. TG-CTS170054 327. Rosen, A., Notestein, J., Snurr, R. 2019. Identifying Promising Metal-Organic Frameworks for Heterogeneous Catalysis via High-Throughput Periodic Density Functional Theory. DOI:10.1002/jcc.25787 (Invalid?). (accepted) [Stampede2, TACC]

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94. TG-DEB120016 328. Lan, Y., J. C. Morrison, R. Hershberg, and G. L. Rosen (2013), POGO-DB—a database of pairwise-comparisons of genomes and conserved orthologous genes, Nucleic Acids Research, 42(D1), D625–D632, doi:10.1093/nar/gkt1094. (published) [SDSC, Stampede, TACC, Training, Trestles] 95. TG-DMR070072N 329. Kang, Y., H. Peelaers, K. Krishnaswamy, and C. G. Van de Walle (2018), First-principles study of direct and indirect optical absorption in BaSnO3, Applied Physics Letters, 112(6), 062106, doi:10.1063/1.5013641. (published) 330. Peelaers, H., and C. G. Van de Walle (2017), Sub-band-gap absorption in Ga2O3, Applied Physics Letters, 111(18), 182104, doi:10.1063/1.5001323. (published) 331. Peelaers, H., J. B. Varley, J. S. Speck, and C. G. Van de Walle (2018), Structural and electronic properties of Ga2O3-Al2O3 alloys, Applied Physics Letters, 112(24), 242101, doi:10.1063/1.5036991. (published) 332. Shen, J.-X., D. Wickramaratne, and C. G. Van de Walle (2017), Band bowing and the direct-to-indirect crossover in random BAlN alloys, Physical Review Materials, 1(6), doi:10.1103/physrevmaterials.1.065001. (published) 333. Swift, M., Porter, Z., Wilson, S., Van de Walle, C. 2018. Electron doping in Sr3Ir2O7 : Collapse of band gap and magnetic order. DOI:10.1103/physrevb.98.081106 (Invalid?). (published) 334. Swift, M. W., C. G. Van de Walle, and M. P. A. Fisher (2018), Posner molecules: from atomic structure to nuclear spins, Physical Chemistry Chemical Physics, 20(18), 12373–12380, doi:10.1039/c7cp07720c. (published) 335. Varley, J., A. Janotti, C. G. Van de Walle, and J. L. Lyons (2018), First-principles calculations of optical transitions at native defects and impurities in ZnO, edited by F. H. Teherani, D. C. Look, and D. J. Rogers, Oxide-based Materials and Devices IX, doi:10.1117/12.2303687. (published) 336. Wang, W., H. Peelaers, J.-X. Shen, and C. G. Van de Walle (2018), Carrier-induced absorption as a mechanism for electrochromism in tungsten trioxide, MRS Communications, 8(03), 926–931, doi:10.1557/mrc.2018.115. (published) 337. Weston, L., Bjaalie, L., Krishnaswamy, K., Van de Walle, C. 2018. Origins of n-type doping difficulties in perovskite stannates. DOI:10.1103/physrevb.97.054112 (Invalid?). (published) 338. Weston, L., H. Tailor, K. Krishnaswamy, L. Bjaalie, and C. G. Van de Walle (2018), Accurate and efficient band- offset calculations from density functional theory, Computational Materials Science, 151, 174–180, doi:10.1016/j.commatsci.2018.05.002. (published) 339. Weston, L., D. Wickramaratne, M. Mackoit, A. Alkauskas, and C. G. Van de Walle (2018), Native point defects and impurities in hexagonal boron nitride, Physical Review B, 97(21), doi:10.1103/physrevb.97.214104. (published) 340. Zhang, L., A. Janotti, A. C. Meng, K. Tang, C. G. Van de Walle, and P. C. McIntyre (2018), Interfacial Cation- Defect Charge Dipoles in Stacked TiO2/Al2O3 Gate Dielectrics, ACS Applied Materials & Interfaces, 10(6), 5140–5146, doi:10.1021/acsami.7b19619. (published) 96. TG-DMR090026 341. Sakai, Y., J. R. Chelikowsky, and M. L. Cohen (2018), Simulating the effect of boron doping in superconducting carbon, Physical Review B, 97(5), doi:10.1103/physrevb.97.054501. (published) 342. Sakai, Y., J. R. Chelikowsky, and M. L. Cohen (2018), Magnetism in amorphous carbon, Physical Review Materials, 2(7), doi:10.1103/physrevmaterials.2.074403. (published) 97. TG-DMR110005 343. Ghadge, S. D., P. P. Patel, M. K. Datta, O. I. Velikokhatnyi, P. M. Shanthi, and P. N. Kumta (2018), First report of vertically aligned (Sn,Ir)O 2 :F solid solution nanotubes: Highly efficient and robust oxygen evolution electrocatalysts for proton exchange membrane based water electrolysis, Journal of Power Sources, 392, 139–149, doi:10.1016/j.jpowsour.2018.04.065. (published) [Comet, TACC] 344. Patel, P. P., O. I. Velikokhatnyi, S. D. Ghadge, P. J. Hanumantha, M. K. Datta, R. Kuruba, B. Gattu, P. M. Shanthi, and P. N. Kumta (2018), Electrochemically active and robust cobalt doped copper phosphosulfide electrocatalysts for hydrogen evolution reaction in electrolytic and photoelectrochemical water splitting, International Journal of Hydrogen Energy, 43(16), 7855–7871, doi:10.1016/j.ijhydene.2018.02.147. (published) [Comet, TACC]

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345. Velikokhatnyi, O. I., and P. N. Kumta (2018), First principles study of the elastic properties of magnesium and iron based bio-resorbable alloys, Materials Science and Engineering: B, 230, 20–23, doi:10.1016/j.mseb.2017.12.024. (published) [Comet, TACC] 98. TG-DMR110007 346. Andrews, W., Voorhees, P., Thornton, K. 2019. Coarsening of Bicontinuous Microstructures via Surface Diffusion. (in preparation) 347. Andrews, W., Voorhees, P., Thornton, K. 2019. Phase Field Study of Coarsening in Two and Three Dimensions with Dissimilar Mobilities Between the Phases. (in preparation) 348. Chadwick, A., Thornton, K. 2018. Models of Electrode and Electrolyte Behavior at the Continuum Scale. PhD Dissertation. University of Michigan. (published) [Bridges Regular, PSC, Stampede2, TACC] 349. Chadwick, A., Thornton, K. 2019. Effect of Material Anisotropy on the Mechanical Response of Protective Layers for Lithium Metal Anodes. (in preparation) 350. Chan, V. W. L., N. Pisutha-Arnond, and K. Thornton (2017), Thermodynamic relationships for homogeneous crystalline and liquid phases in the phase-field crystal model, Computational Materials Science, 135, 205– 213, doi:10.1016/j.commatsci.2017.04.017. (published) [Stampede, TACC] 351. Choe, M., Andrews, W., Yu, H., Thornton, K. 2019. Predicting Transport Properties Through Planar Projections of Complex Grain Boundary Networks. (in preparation) 352. Choe, M., Yu, H., Amatucci, G., Chiang, Y., Thornton, K. 2019. Modeling Interfacial Transport in Complex Microstructures Using the Smoothed Boundary Method. (in preparation) 353. Choe, M., Yu, H., Thornton, K. 2019. Simulating Diffusional Impedance Spectroscopy of Single Particles. (in preparation) 354. Choe, M., Yu, H., Thornton, K. 2019. Simulating Diffusional Impedance Spectroscopy of Polycrystalline Microstructures. (in preparation) 355. Du, S., Park, C., Wang, S., Barnett, S., Thornton, K. 2019. Image Processing for Complex Three-dimensional Microstructure Reconstruction. (in preparation) 356. Montiel, D., Huang, G., Alster, E., Voorhees, P., Thornton, K. 2019. A Three-Dimensional Phase-Field Crystal Model for Two-Dimensional Materials. (in preparation) 357. Sun, Y., Andrews, W., Thornton, K., Voorhees, P. 2018. Self-Similarity and the Dynamics of Coarsening in Materials. (submitted) 358. Yu, H., Cronin, J., Adler, S., Barnett, S., Thornton, K. 2019. Simulating Diffusional Impedance Spectroscopy of Complex Solid Oxide Fuel Cell Cathode. (in preparation) 359. Yu, H., Orvananos, B., Cronin, S., Bazant, M., Barnett, S., et al. 2019. Smoothed Boundary Method Framework For Electrochemical Simulation of Li-ion Battery Cathode with Complex Microstructure: Part I Model, Formulation, and Parameterization. (in preparation) 360. Yu, H., Taha, D., Thompson, T., Taylor, N., Drews, A., et al. 2019. Deformation and Mechanical Stresses in Solid-State Composite Battery Cathodes. (in preparation) 361. Zhang, W. et al. (2018), Localized concentration reversal of lithium during intercalation into nanoparticles, Science Advances, 4(1), eaao2608, doi:10.1126/sciadv.aao2608. (published) 99. TG-DMR110009 362. Chen, X., D. Wu, L. Zou, Q. Yin, H. Zhang, D. N. Zakharov, E. A. Stach, and G. Zhou (2018), In situ atomic-scale observation of inhomogeneous oxide reduction, Chemical Communications, 54(53), 7342–7345, doi:10.1039/c8cc03822h. (published) [Comet, SDSC] 363. Li, C., P. Zhang, J. Wang, J. A. Boscoboinik, and G. Zhou (2018), Tuning the Deoxygenation of Bulk-Dissolved Oxygen in Copper, The Journal of Physical Chemistry C, 122(15), 8254–8261, doi:10.1021/acs.jpcc.7b12030. (published) [Comet, SDSC] 364. Zou, L. et al. (2018), Segregation induced order-disorder transition in Cu(Au) surface alloys, Acta Materialia, 154, 220–227, doi:10.1016/j.actamat.2018.05.040. (published) [Comet, SDSC] 365. Zou, L. et al. (2017), Dislocation nucleation facilitated by atomic segregation, Nature Materials, 17(1), 56–63, doi:10.1038/nmat5034. (published) [Comet, SDSC] 100. TG-DMR110090 366. He, M., Wu, C., Shugaev, M., Samolyuk, G., Zhigilei, L. 2018. Computational study of short pulse laser induced generation of crystal defects in Ni-based single-phase binary solid solution alloys. (submitted) [Stampede, Stampede2, TACC]

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101. TG-DMR110093 367. Ghazisaeed, S., J. Majzlan, J. Plášil, and B. Kiefer (2018), A simple method for the prediction of the orientation of H2O molecules in ionic crystals, Journal of Applied Crystallography, 51(4), 1116–1124, doi:10.1107/s1600576718008567. (published) [Stampede, TACC] 102. TG-DMR120041 368. Liu, J., E. Wang, Y. Zhao, X. Xu, J.-S. Moon, and M. P. Anantram (2018), Impact of doping on bonding energy hierarchy and melting of phase change materials, Journal of Applied Physics, 124(9), 094503, doi:10.1063/1.5039831. (published) [GaTech, IU, LSU, NICS, OSG, PSC, SDSC, Standford, TACC] 369. Liu, J., X. Xu, and M. P. Anantram (2014), Role of inelastic electron–phonon scattering in electron transport through ultra-scaled amorphous phase change material nanostructures, Journal of Computational Electronics, 13(3), 620–626, doi:10.1007/s10825-014-0579-7. (published) 370. Liu, J., X. Xu, L. Brush, and M. P. Anantram (2014), A multi-scale analysis of the crystallization of amorphous germanium telluride using ab initio simulations and classical crystallization theory, Journal of Applied Physics, 115(2), 023513, doi:10.1063/1.4861721. (published) 103. TG-DMR120073 371. Kolesov, G., E. Kaxiras, and E. Manousakis (2018), Density functional theory beyond the Born-Oppenheimer approximation: Accurate treatment of the ionic zero-point motion, Physical Review B, 98(19), doi:10.1103/physrevb.98.195112. (published) [Stampede, TACC] 104. TG-DMR130077 372. Trotochaud, L., A. R. Head, C. Büchner, Y. Yu, O. Karslıoğlu, R. Tsyshevsky, S. Holdren, B. Eichhorn, M. M. Kuklja, and H. Bluhm (2019), Room temperature decomposition of dimethyl methylphosphonate on cuprous oxide yields atomic phosphorus, Surface Science, 680, 75–87, doi:10.1016/j.susc.2018.10.003. (published) [Ranch, Stampede, Stampede2, TACC] 105. TG-DMR140008 373. Zhang, N., and M. Asle Zaeem (2017), Role of grain boundaries in determining strength and plastic deformation of yttria-stabilized tetragonal zirconia bicrystals, Journal of Materials Science, 53(8), 5706– 5718, doi:10.1007/s10853-017-1595-3. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede, TACC] 374. Zhang, N., Y. Hong, S. Yazdanparast, and M. Asle Zaeem (2018), Superior structural, elastic and electronic properties of 2D titanium nitride MXenes over carbide MXenes: a comprehensive first principles study, 2D Materials, 5(4), 045004, doi:10.1088/2053-1583/aacfb3. (published) [Comet, PSC, SDSC, Stampede, TACC] 106. TG-DMR140068 375. Jia, Q. et al. (2018), Roles of Mo Surface Dopants in Enhancing the ORR Performance of Octahedral PtNi Nanoparticles, Nano Letters, 18(2), 798–804, doi:10.1021/acs.nanolett.7b04007. (published) [Stampede, TACC] 376. Li, C., D. Raciti, T. Pu, L. Cao, C. He, C. Wang, and T. Mueller (2018), Improved Prediction of Nanoalloy Structures by the Explicit Inclusion of Adsorbates in Cluster Expansions, The Journal of Physical Chemistry C, 122(31), 18040–18047, doi:10.1021/acs.jpcc.8b03868. (published) [Stampede, TACC] 107. TG-DMR140129 377. Adorf, C. S., J. Antonaglia, J. Dshemuchadse, and S. C. Glotzer (2018), Inverse design of simple pair potentials for the self-assembly of complex structures, The Journal of Chemical Physics, 149(20), 204102, doi:10.1063/1.5063802. (published) [Bridges GPU, Comet, PSC, SDSC] 378. Dodd, P. M., P. F. Damasceno, and S. C. Glotzer (2018), Universal folding pathways of polyhedron nets, Proceedings of the National Academy of Sciences, 115(29), E6690–E6696, doi:10.1073/pnas.1722681115. (published) [Comet, SDSC]

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108. TG-DMR150006 379. Beams, R. et al. (2016), Characterization of Few-Layer 1T′ MoTe2 by Polarization-Resolved Second Harmonic Generation and Raman Scattering, ACS Nano, 10(10), 9626–9636, doi:10.1021/acsnano.6b05127. (published) [Stampede2, TACC] 380. Shinde, A., S. K. Suram, Q. Yan, L. Zhou, A. K. Singh, J. Yu, K. A. Persson, J. B. Neaton, and J. M. Gregoire (2017), Discovery of Manganese-Based Solar Fuel Photoanodes via Integration of Electronic Structure Calculations, Pourbaix Stability Modeling, and High-Throughput Experiments, ACS Energy Letters, 2(10), 2307–2312, doi:10.1021/acsenergylett.7b00607. (published) [Stampede, Stampede2, TACC] 381. Singh, A. 2016. Tandem Catalysts for CO2 Reduction. (in preparation) [Ranch, Stampede2, TACC] 382. Singh, A., Beams, R., Kalish, I., Davydov, A. 2018. Structure and Properties of High Mobility Mo1-xTe2+x phases. (in preparation) [Ranch, Stampede2, TACC] 383. Singh, A., Montoya, J., Gregoire, J., Persson, K. 2018. Robust and Synthesizable Photocatalysts for CO2 Reduction. Submitted. Nature Communications. (submitted) [Ranch, Stampede2, TACC] 384. Singh, A., Wang, Q. 2018. Substrate-Assisted Nanoscroll Formation in Transition Metal Dichalcogenides. (in preparation) [Ranch, Stampede2, TACC] 385. Singh, A. K., L. Zhou, A. Shinde, S. K. Suram, J. H. Montoya, D. Winston, J. M. Gregoire, and K. A. Persson (2017), Electrochemical Stability of Metastable Materials, Chemistry of Materials, 29(23), 10159–10167, doi:10.1021/acs.chemmater.7b03980. (published) [Stampede, Stampede2, TACC] 386. Torrissi, S., Singh, A., Montoya, J., Persson, K. 2018. 2D Materials Surface for Efficient Solar to Fuel COnversion. (in preparation) [Ranch, Stampede2, TACC] 109. TG-DMR150064 387. Pinge, S. D., D. Baskaran, and Y. L. Joo (2018), Evaluation of line-edge/line-width roughness of directed self- assembled PS-b-PMMA patterns using coarse-grained molecular dynamics simulation, edited by E. M. Panning and M. I. Sanchez, Novel Patterning Technologies 2018, doi:10.1117/12.2297485. (published) [Comet, SDSC] 110. TG-DMR150099 388. Harrington, S., Rice, A., Brown-Heft, T., Bonef, B., Sharan, A., et al. 2018. Growth, electrical, structural, and magnetic properties of half-Heusler CoTi1−xFexSb. DOI:10.1103/physrevmaterials.2.014406 (Invalid?). (published) [PSC, SDSC] 389. Kawasaki, J. K., A. Sharan, L. I. M. Johansson, M. Hjort, R. Timm, B. Thiagarajan, B. D. Schultz, A. Mikkelsen, A. Janotti, and C. J. Palmstrøm (2018), A simple electron counting model for half-Heusler surfaces, Science Advances, 4(6), eaar5832, doi:10.1126/sciadv.aar5832. (published) [PSC, SDSC] 390. Li, W., Sabino, F., Crasto de Lima, F., Wang, T., Miwa, R., et al. 2018. Large disparity between optical and fundamental band gaps in layered In2Se3. (accepted) [PSC, SDSC] 391. Wang, T., Li, W., Ni, C., Janotti, A. 2018. Band Gap and Band Offset of Ga2O3 and (AlxGa1−x)2O3 Alloys. DOI:10.1103/physrevapplied.10.011003 (Invalid?). (published) [PSC, SDSC] 111. TG-DMR150112 392. Motta, M., and S. Zhang (2017), Computation of Ground-State Properties in Molecular Systems: Back- Propagation with Auxiliary-Field Quantum Monte Carlo, Journal of Chemical Theory and Computation, 13(11), 5367–5378, doi:10.1021/acs.jctc.7b00730. (published) 393. Motta, M., and S. Zhang (2018), Ab initio computations of molecular systems by the auxiliary-field quantum Monte Carlo method, Wiley Interdisciplinary Reviews: Computational Molecular Science, 8(5), e1364, doi:10.1002/wcms.1364. (published) 394. Motta, M., and S. Zhang (2018), Communication: Calculation of interatomic forces and optimization of molecular geometry with auxiliary-field quantum Monte Carlo, The Journal of Chemical Physics, 148(18), 181101, doi:10.1063/1.5029508. (published) 395. Rosenberg, P., H. Shi, and S. Zhang (2017), Ultracold Atoms in a Square Lattice with Spin-Orbit Coupling: Charge Order, Superfluidity, and Topological Signatures, Physical Review Letters, 119(26), doi:10.1103/physrevlett.119.265301. (published) 396. Rosenberg, P., H. Shi, and S. Zhang (2017), Accurate computations of Rashba spin-orbit coupling in interacting systems: From the Fermi gas to real materials, Journal of Physics and Chemistry of Solids, doi:10.1016/j.jpcs.2017.12.026. (published)

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397. Vitali, E., Shi, H., Chiciak, A., Zhang, S. 2018. Metal-insulator transition in the ground-state of the three-band Hubbard model at half-filling. (submitted) 398. Vitali, E., Shi, H., Qin, M., Zhang, S. 2017. Visualizing the BEC-BCS crossover in a two-dimensional Fermi gas: Pairing gaps and dynamical response functions from ab initio computations. DOI:10.1103/physreva.96.061601 (Invalid?). (published) [Comet, SDSC] 399. Zheng, B.-X., C.-M. Chung, P. Corboz, G. Ehlers, M.-P. Qin, R. M. Noack, H. Shi, S. R. White, S. Zhang, and G. K.-L. Chan (2017), Stripe order in the underdoped region of the two-dimensional Hubbard model, Science, 358(6367), 1155–1160, doi:10.1126/science.aam7127. (published) 112. TG-DMR160007 400. Kim, D. H., H. E. Lee, B. K. You, S. B. Cho, R. Mishra, I.-S. Kang, and K. J. Lee (2018), Flexible Crossbar- Structured Phase Change Memory Array via Mo-Based Interfacial Physical Lift-Off, Advanced Functional Materials, 1806338, doi:10.1002/adfm.201806338. (published) [Comet, SDSC, Stampede2, TACC] 401. Majidi, L. et al. (2018), New Class of Electrocatalysts Based on 2D Transition Metal Dichalcogenides in Ionic Liquid, Advanced Materials, 31(4), 1804453, doi:10.1002/adma.201804453. (published) [Comet, SDSC, Stampede, Stampede2, TACC] 402. Morrell, M. V., X. He, G. Luo, A. S. Thind, T. A. White, J. A. Hachtel, A. Y. Borisevich, J.-C. Idrobo, R. Mishra, and Y. Xing (2018), Significantly Enhanced Emission Stability of CsPbBr3 Nanocrystals via Chemically Induced Fusion Growth for Optoelectronic Devices, ACS Applied Nano Materials, 1(11), 6091–6098, doi:10.1021/acsanm.8b01298. (published) [Comet, SDSC, Stampede, Stampede2, TACC] 403. Thind, A. S., G. Luo, J. A. Hachtel, M. V. Morrell, S. B. Cho, A. Y. Borisevich, J.-C. Idrobo, Y. Xing, and R. Mishra (2018), Atomic Structure and Electrical Activity of Grain Boundaries and Ruddlesden-Popper Faults in Cesium Lead Bromide Perovskite, Advanced Materials, 31(4), 1805047, doi:10.1002/adma.201805047. (published) [Comet, SDSC, Stampede, Stampede2, TACC] 113. TG-DMR160067, TG-DMR160122, TG-DMR160163 404. Feng, S., M. Chen, L. Giordano, M. Huang, W. Zhang, C. V. Amanchukwu, R. Anandakathir, Y. Shao-Horn, and J. A. Johnson (2017), Mapping a stable solvent structure landscape for aprotic Li–air battery organic electrolytes, Journal of Materials Chemistry A, 5(45), 23987–23998, doi:10.1039/c7ta08321a. (published) [Comet, SDSC] 405. Gauthier, M., P. Karayaylali, L. Giordano, S. Feng, S. F. Lux, F. Maglia, P. Lamp, and Y. Shao-Horn (2018), Probing Surface Chemistry Changes Using LiCoO2-only Electrodes in Li-Ion Batteries, Journal of The Electrochemical Society, 165(7), A1377–A1387, doi:10.1149/2.0431807jes. (published) [Comet, SDSC] 406. Han, B. et al. (2018), Iron-Based Perovskites for Catalyzing Oxygen Evolution Reaction, The Journal of Physical Chemistry C, 122(15), 8445–8454, doi:10.1021/acs.jpcc.8b01397. (published) [Comet, SDSC] 407. Huang, B., S. Muy, S. Feng, Y. Katayama, Y.-C. Lu, G. Chen, and Y. Shao-Horn (2018), Non-covalent interactions in electrochemical reactions and implications in clean energy applications, Physical Chemistry Chemical Physics, 20(23), 15680–15686, doi:10.1039/c8cp02512f. (published) [Comet, SDSC] 408. Huang, M., S. Feng, W. Zhang, L. Giordano, M. Chen, C. V. Amanchukwu, R. Anandakathir, Y. Shao-Horn, and J. A. Johnson (2018), Fluorinated Aryl Sulfonimide Tagged (FAST) salts: modular synthesis and structure– property relationships for battery applications, Energy & Environmental Science, 11(5), 1326–1334, doi:10.1039/c7ee03509h. (published) [Comet, SDSC] 409. Katayama, Y., L. Giordano, R. R. Rao, J. Hwang, H. Muroyama, T. Matsui, K. Eguchi, and Y. Shao-Horn (2018), Surface (Electro)chemistry of CO2 on Pt Surface: An in Situ Surface-Enhanced Infrared Absorption Spectroscopy Study, The Journal of Physical Chemistry C, 122(23), 12341–12349, doi:10.1021/acs.jpcc.8b03556. (published) [Comet, SDSC] 410. Krauskopf, T., S. Muy, S. P. Culver, S. Ohno, O. Delaire, Y. Shao-Horn, and W. G. Zeier (2018), Comparing the Descriptors for Investigating the Influence of Lattice Dynamics on Ionic Transport Using the Superionic Conductor Na3PS4–xSex, Journal of the American Chemical Society, 140(43), 14464–14473, doi:10.1021/jacs.8b09340. (published) [Comet, SDSC] 411. Muy, S., J. C. Bachman, H.-H. Chang, L. Giordano, F. Maglia, S. Lupart, P. Lamp, W. G. Zeier, and Y. Shao-Horn (2018), Lithium Conductivity and Meyer-Neldel Rule in Li3PO4–Li3VO4–Li4GeO4 Lithium Superionic Conductors, Chemistry of Materials, 30(16), 5573–5582, doi:10.1021/acs.chemmater.8b01504. (published) [Comet, SDSC] 412. Muy, S. et al. (2018), Tuning mobility and stability of lithium ion conductors based on lattice dynamics, Energy & Environmental Science, 11(4), 850–859, doi:10.1039/c7ee03364h. (published) [Comet, SDSC]

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413. Rao, R. R. et al. (2018), Surface Orientation Dependent Water Dissociation on Rutile Ruthenium Dioxide, The Journal of Physical Chemistry C, 122(31), 17802–17811, doi:10.1021/acs.jpcc.8b04284. (published) [Comet, SDSC] 414. Tatara, R., G. M. Leverick, S. Feng, S. Wan, S. Terada, K. Dokko, M. Watanabe, and Y. Shao-Horn (2018), Tuning NaO2 Cube Sizes by Controlling Na+ and Solvent Activity in Na–O2 Batteries, The Journal of Physical Chemistry C, 122(32), 18316–18328, doi:10.1021/acs.jpcc.8b05418. (published) [Comet, SDSC] 114. TG-DMR160106 415. Kou, T., Smart, T., Yao, B., Chen, I., Thota, D., et al. 2018. Theoretical and Experimental Insight into the Effect of Nitrogen Doping on Hydrogen Evolution Activity of Ni3S2 in Alkaline Medium. DOI:10.1002/aenm.201703538 (Invalid?). (published) [Stampede, TACC] 416. Peng, Y., B. Lu, F. Wu, F. Zhang, J. E. Lu, X. Kang, Y. Ping, and S. Chen (2018), Point of Anchor: Impacts on Interfacial Charge Transfer of Metal Oxide Nanoparticles, Journal of the American Chemical Society, 140(45), 15290–15299, doi:10.1021/jacs.8b08035. (published) [Bridges Regular, PSC, Stampede2, TACC] 417. Smart, T. J., A. C. Cardiel, F. Wu, K.-S. Choi, and Y. Ping (2018), Mechanistic insights of enhanced spin polaron conduction in CuO through atomic doping, npj Computational Materials, 4(1), doi:10.1038/s41524-018- 0118-3. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 418. Smart, T. J., F. Wu, M. Govoni, and Y. Ping (2018), Fundamental principles for calculating charged defect ionization energies in ultrathin two-dimensional materials, Physical Review Materials, 2(12), doi:10.1103/physrevmaterials.2.124002. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede, TACC] 419. Zhang, W., F. Wu, J. Li, D. Yan, J. Tao, Y. Ping, and M. Liu (2018), Unconventional Relation between Charge Transport and Photocurrent via Boosting Small Polaron Hopping for Photoelectrochemical Water Splitting, ACS Energy Letters, 3(9), 2232–2239, doi:10.1021/acsenergylett.8b01445. (published) [Stampede, TACC] 115. TG-DMR160106 420. Wu, F., and Y. Ping (2018), Combining Landau–Zener theory and kinetic Monte Carlo sampling for small polaron mobility of doped BiVO4 from first-principles, Journal of Materials Chemistry A, 6(41), 20025– 20036, doi:10.1039/c8ta07437b. (published) [Bridges Regular, Stampede] 116. TG-DMR160112 421. Amsler, M., Hegde, V., Jacobsen, S., Wolverton, C. 2018. Exploring the high-pressure materials genome. (published) [Bridges Regular, PSC] 117. TG-DMR160114 422. Cheng, T., A. Jaramillo-Botero, Q. An, D. V. Ilyin, S. Naserifar, and W. A. Goddard (2018), First principles-based multiscale atomistic methods for input into first principles nonequilibrium transport across interfaces, Proceedings of the National Academy of Sciences, 201800035, doi:10.1073/pnas.1800035115. (published) [ECSS, NIP, Science Gateways, Stampede, TACC, Training] 423. Ilyin, D. V., W. A. Goddard, J. J. Oppenheim, and T. Cheng (2018), First-principles–based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition, Proceedings of the National Academy of Sciences, 201701383, doi:10.1073/pnas.1701383115. (published) [Comet, ECSS, NIP, Science Gateways, SDSC, Stampede, TACC, Training] 424. Lum, Y., T. Cheng, W. A. Goddard, and J. W. Ager (2018), Electrochemical CO Reduction Builds Solvent Water into Oxygenate Products, Journal of the American Chemical Society, 140(30), 9337–9340, doi:10.1021/jacs.8b03986. (published) [Comet, ECSS, NIP, Science Gateways, SDSC, Stampede, TACC, Training] 425. Wang, Y., T. Cheng, J. Sun, Z. Liu, M. Frasconi, W. A. Goddard, and J. F. Stoddart (2018), Neighboring Component Effect in a Tri-stable [2]Rotaxane, Journal of the American Chemical Society, 140(42), 13827– 13834, doi:10.1021/jacs.8b08519. (published) [Comet, ECSS, NIP, Science Gateways, SDSC, Stampede, TACC, Training] 118. TG-DMR160162 426. Burgess, S., A. Vishnyakov, C. Tsovko, and A. V. Neimark (2018), Nanoparticle-Engendered Rupture of Lipid Membranes, The Journal of Physical Chemistry Letters, 9(17), 4872–4877, doi:10.1021/acs.jpclett.8b01696. (published) [Comet, SDSC]

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119. TG-DMR170070 427. Agarwal, A. et al. (2018), Anomalous isoelectronic chalcogen rejection in 2D anisotropic vdW TiS3(1−x)Se3x trichalcogenides, Nanoscale, 10(33), 15654–15660, doi:10.1039/c8nr04274h. (published) 428. Fan, L., S. Li, L. Liu, W. Zhang, L. Gao, Y. Fu, F. Chen, J. Li, H. L. Zhuang, and Y. Lu (2018), Enabling Stable Lithium Metal Anode via 3D Inorganic Skeleton with Superlithiophilic Interphase, Advanced Energy Materials, 8(33), 1802350, doi:10.1002/aenm.201802350. (published) 429. Fan, L., H. L. Zhuang, W. Zhang, Y. Fu, Z. Liao, and Y. Lu (2018), Stable Lithium Electrodeposition at Ultra-High Current Densities Enabled by 3D PMF/Li Composite Anode, Advanced Energy Materials, 8(15), 1703360, doi:10.1002/aenm.201703360. (published) 430. Islam, N., W. Huang, and H. L. Zhuang (2018), Machine learning for phase selection in multi-principal element alloys, Computational Materials Science, 150, 230–235, doi:10.1016/j.commatsci.2018.04.003. (published) 431. Lin, G., Wang, Y., Luo, X., Ma, J., Zhuang, H., et al. 2018. Magnetoelectric and Raman spectroscopic studies of monocrystalline MnCr2O4. DOI:10.1103/physrevb.97.064405 (Invalid?). (published) 432. Liu, L., I. Kankam, and H. L. Zhuang (2018), Ab initio playing of pentagonal puzzles, Electronic Structure, 1(1), 015004, doi:10.1088/2516-1075/aae303. (published) [Stampede] 433. Liu, L., Kankam, I., Zhuang, H. 2018. Ab initio playing of pentagonal puzzles.. (accepted) 434. Liu, L., I. Kankam, and H. L. Zhuang (2018), Can an element form a two-dimensional nanosheet of type 15 pentagons?, Computational Materials Science, 154, 37–40, doi:10.1016/j.commatsci.2018.07.031. (published) 435. Liu, L., and H. L. Zhuang (2018), Tunable phase transition in single-layer TiSe 2 via electric field, Journal of Solid State Chemistry, 262, 309–312, doi:10.1016/j.jssc.2018.03.034. (published) 436. Liu, L., Zhuang, H. 2018. PtP2: An example of exploring the hidden Cairo tessellation in the pyrite structure for discovering novel two-dimensional materials. (accepted) 437. Liu, L., and H. L. Zhuang (2018), High-throughput functionalization of single-layer electride Ca2N, Materials Research Express, 5(7), 076306, doi:10.1088/2053-1591/aad024. (published) 438. Liu, L., Zhuang, H. 2018. PtP2: An example of exploring the hidden Cairo tessellation in the pyrite structure for discovering novel two-dimensional materials. DOI:10.1103/physrevmaterials.2.114003 (Invalid?). (published) 439. Qian, J., L. Liu, J. Yang, S. Li, X. Wang, H. L. Zhuang, and Y. Lu (2018), Electrochemical surface passivation of LiCoO2 particles at ultrahigh voltage and its applications in lithium-based batteries, Nature Communications, 9(1), doi:10.1038/s41467-018-07296-6. (published) [Stampede] 440. Shen, Y. et al. (2018), Ultimate Control over Hydrogen Bond Formation and Reaction Rates for Scalable Synthesis of Highly Crystalline vdW MOF Nanosheets with Large Aspect Ratio, Advanced Materials, 30(52), 1802497, doi:10.1002/adma.201802497. (published) 441. Yang, S., B. Chen, Y. Qin, Y. Zhou, L. Liu, M. Durso, H. Zhuang, Y. Shen, and S. Tongay (2018), Highly crystalline synthesis of tellurene sheets on two-dimensional surfaces: Control over helical chain direction of tellurene, Physical Review Materials, 2(10), doi:10.1103/physrevmaterials.2.104002. (published) 442. Zhang, W., H. L. Zhuang, L. Fan, L. Gao, and Y. Lu (2018), A “cation-anion regulation” synergistic anode host for dendrite-free lithium metal batteries, Science Advances, 4(2), eaar4410, doi:10.1126/sciadv.aar4410. (published) 443. Zhou, J., H. L. Zhuang, and H. Wang (2017), Layered tetragonal zinc chalcogenides for energy-related applications: from photocatalysts for water splitting to cathode materials for Li-ion batteries, Nanoscale, 9(44), 17303–17311, doi:10.1039/c7nr04289b. (published) 120. TG-DMR170102 444. Liu, J., and S. T. Pantelides (2018), Mechanisms of Pyroelectricity in Three- and Two-Dimensional Materials, Physical Review Letters, 120(20), doi:10.1103/physrevlett.120.207602. (published) [Stampede2, TACC] 445. Liu, J., and S. T. Pantelides (2018), Anisotropic thermal expansion of group-IV monochalcogenide monolayers, Applied Physics Express, 11(10), 101301, doi:10.7567/apex.11.101301. (published) [Stampede2, TACC] 121. TG-DMR180038 446. Chang, Y.-W., M. S. Dimitriyev, A. Souslov, S. V. Nikolov, S. M. Marquez, A. Alexeev, P. M. Goldbart, and A. Fernández-Nieves (2018), Extreme thermodynamics with polymer gel tori: Harnessing thermodynamic instabilities to induce large-scale deformations, Physical Review E, 98(2), doi:10.1103/physreve.98.020501. (published) [Comet, Ranch, Stampede2, TACC]

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122. TG-DMR180059 447. Herran, J., S. Prophet, Y. Jin, S. Valloppilly, P. R. Kharel, D. J. Sellmyer, and P. V. Lukashev (2018), Structural and magnetic properties of bulk Mn2PtSn, Journal of Physics: Condensed Matter, 30(47), 475801, doi:10.1088/1361-648x/aae652. (published) 123. TG-DMR970008S 448. Kim, H., Seo, D., Bianchini, M., Clément, R., Kim, H., et al. 2018. A New Strategy for High-Voltage Cathodes for K-Ion Batteries: Stoichiometric KVPO4F. DOI:10.1002/aenm.201801591 (Invalid?). (published) 449. Kim, H., D.-H. Seo, A. Urban, J. Lee, D.-H. Kwon, S.-H. Bo, T. Shi, J. K. Papp, B. D. McCloskey, and G. Ceder (2018), Stoichiometric Layered Potassium Transition Metal Oxide for Rechargeable Potassium Batteries, Chemistry of Materials, 30(18), 6532–6539, doi:10.1021/acs.chemmater.8b03228. (published) 450. Lacivita, V., N. Artrith, and G. Ceder (2018), Structural and Compositional Factors That Control the Li-Ion Conductivity in LiPON Electrolytes, Chemistry of Materials, 30(20), 7077–7090, doi:10.1021/acs.chemmater.8b02812. (published) 451. Lacivita, V., A. S. Westover, A. Kercher, N. D. Phillip, G. Yang, G. Veith, G. Ceder, and N. J. Dudney (2018), Resolving the Amorphous Structure of Lithium Phosphorus Oxynitride (Lipon), Journal of the American Chemical Society, 140(35), 11029–11038, doi:10.1021/jacs.8b05192. (published) 124. TG-DMS160012 452. Alavi, A., Ruffalo, M., Parvangada, A., Huang, Z., Bar-Joseph, Z. 2018. scQuery: a web server for comparative analysis of single-cell RNA-seq data. (accepted) 453. Ma, C., M. Shao, and C. Kingsford (2018), SQUID: transcriptomic structural variation detection from RNA-seq, Genome Biology, 19(1), doi:10.1186/s13059-018-1421-5. (published) 454. Ruffalo, M., Thomas, R., Lee, A., Oesterreich, S., Bar-Joseph, Z. 2018. Network-guided prediction of aromatase inhibitor response in breast cancer. (accepted) 125. TG-DMS170019 455. Werther, C., Ferguson, M., Park, K., Kling, T., Chen, C., et al. 2018. Gender Effect on Face Recognition for a Large Longitudinal Database.. IEEE International Workshop on Information Forensics and Security 2018 (WIFS 2018) (Hong Kong). arXiv:1811.03680. (accepted) [Stampede, TACC] 126. TG-DMS170020, TG-DMS170025 456. Wang, K. (2019), Efficient counting of degree sequences, Discrete Mathematics, 342(3), 888–897, doi:10.1016/j.disc.2018.11.024. (published) [Bridges Large] 127. TG-DMS170021 457. Cope, L. 2018. Horizontal Shear Instabilities in the Stellar Regime. Report for the 2018 Geophysical Fluid Dynamics summer program.. (submitted) 128. TG-DMS170031 458. Sun, H., S. Zhou, L.-T. Cheng, and B. Li (2018), Numerical methods for solvent Stokes flow and solute-solvent interfacial dynamics of charged molecules, Journal of Computational Physics, 374, 533–549, doi:10.1016/j.jcp.2018.07.046. (published) 129. TG-DMS180009 459. Villa, U., Parno, M., Petra, N., Stadler, G., Marzouk, Y., et al. 2018. Inverse Problems: Systematic Integration of Data with Models under Uncertainty. Collection of jupyter notebook used in the hands-on computer sessions of the summer school. https://g2s3-2018.github.io/labs/index.html. (published) [IU, Jetstream, TACC, Training] 130. TG-DPP130002 460. Rudi, J., G. Stadler, and O. Ghattas (2017), Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity, SIAM Journal on Scientific Computing, 39(5), S272–S297, doi:10.1137/16m108450x. (published) [Lonestar, Stampede, TACC]

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461. V. Ratnaswamy, V., Rudi, J., Stadler, G., Gurnis, M. 2018. Simultaneous inference of plate boundary properties and mantle viscosity with an adjoint optimization: Large-scale two-dimensional models. (in preparation) [Stampede2, TACC] 131. TG-DPP150003, TG-DPP150005, TG-EAR140018 462. Lloyd, A. 2018. Seismic Tomography of Antarctica and the Southern Oceans: Regional and Continental Models from the Upper Mantle to the Transition Zone. PhD Dissertation - Specifically chapter 3 utilizes EXSEDE resource to produce the 1st generation elastic seismic model ANT-20.. ProQuest Dissertations and Theses. (published) [Stampede, Stampede2, TACC] 463. Lloyd, A., Wiens, D., Zhu, H., Tromp, J., Nyblade, A., et al. 2019. Transverse isotropic seismic structure of the Antarctic upper mantle and transition zone unearthed using adjoint tomography.. (in preparation) [Stampede, Stampede2, TACC] 132. TG-EAR130008 464. Liu, Y. Y., D. R. Maidment, D. G. Tarboton, X. Zheng, and S. Wang (2018), A CyberGIS Integration and Computation Framework for High-Resolution Continental-Scale Flood Inundation Mapping, JAWRA Journal of the American Water Resources Association, 54(4), 770–784, doi:10.1111/1752-1688.12660. (published) [ECSS, Stampede, TACC] 465. Survila, K., A. A. Yιldιrιm, T. Li, Y. Y. Liu, D. G. Tarboton, and S. Wang (2016), A Scalable High-performance Topographic Flow Direction Algorithm for Hydrological Information Analysis, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949571. (published) [ECSS, Stampede, TACC] 466. Yιldιrιm, A. A., D. Tarboton, Y. Liu, N. S. Sazib, and S. Wang (2016), Accelerating TauDEM for Extracting Hydrology Information from National-Scale High Resolution Topographic Dataset, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949582. (published) [ECSS, Stampede, TACC] 133. TG-EAR130011 467. Tao, K., S. P. Grand, and F. Niu (2018), Seismic Structure of the Upper Mantle Beneath Eastern Asia From Full Waveform Seismic Tomography, Geochemistry, Geophysics, Geosystems, 19(8), 2732–2763, doi:10.1029/2018gc007460. (published) [Lonestar, Stampede2, TACC] 134. TG-EAR160023, TG-EAR170019 468. Shi, J., Li, R., Xi, Y., Saad, Y., de Hoop, M. 2018. Computing Planetary Interior Normal Modes with a Highly Parallel Polynomial Filtering Eigensolver. International Conference for High Performance Computing, Networking, Storage and Analysis (SC18) (Dallas, TX). (accepted) [Comet, SDSC, Stampede2, TACC] 135. TG-EAR170004 469. Gardiner, J. D., K. A. Tomko, M.-J. Noh, and I. M. Howat (2018), Code Optimization and Stabilization for a High- Resolution Terrain Generation Application, Proceedings of the Practice and Experience on Advanced Research Computing - PEARC ’18, doi:10.1145/3219104.3229241. (published) 136. TG-EAR170016 470. Maffei, S., Calkins, M., Julien, K., Marti, P. 2018. Magnetic quenching of the inverse cascade in rapidly rotating convective turbulence. (published) [Stampede2] 137. TG-ECS160004 471. Eftekhar, M. A., Z. Sanjabi-Eznaveh, J. E. Antonio-Lopez, H. E. L. Aviles, S. Benis, M. Kolesik, A. Schülzgen, F. W. Wise, R. A. Correa, and D. N. Christodoulides (2018), Accelerating nonlinear interactions in tapered multimode fibers, Conference on Lasers and Electro-Optics, doi:10.1364/cleo_qels.2018.fth1m.3. (published) 472. Eftekhar, M. A., Z. Sanjabi-Eznaveh, J. E. Antonio-Lopez, F. W. Wise, D. N. Christodoulides, and R. Amezcua- Correa (2017), Instant and efficient second-harmonic generation and downconversion in unprepared graded-index multimode fibers, Optics Letters, 42(17), 3478, doi:10.1364/ol.42.003478. (published)

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138. TG-ENG150034 473. Austin, N., S. Zhao, J. R. McKone, R. Jin, and G. Mpourmpakis (2018), Elucidating the active sites for CO2 electroreduction on ligand-protected Au25 nanoclusters, Catalysis Science & Technology, 8(15), 3795–3805, doi:10.1039/c8cy01099d. (published) [Comet] 474. Dean, J., Y. Yang, N. Austin, G. Veser, and G. Mpourmpakis (2018), Design of Copper-Based Bimetallic Nanoparticles for Carbon Dioxide Adsorption and Activation, ChemSusChem, 11(7), 1169–1178, doi:10.1002/cssc.201702342. (published) [Comet] 475. Zhao, S., N. Austin, M. Li, Y. Song, S. D. House, S. Bernhard, J. C. Yang, G. Mpourmpakis, and R. Jin (2018), Influence of Atomic-Level Morphology on Catalysis: The Case of Sphere and Rod-Like Gold Nanoclusters for CO2 Electroreduction, ACS Catalysis, 8(6), 4996–5001, doi:10.1021/acscatal.8b00365. (published) [Comet] 139. TG-ENG170030 476. O'Dea, M. 2018. Development Of An Advanced Wind Turbine Actuator Line Method. PhD Dissertation. Oakland University. (submitted) [Comet, SDSC] 140. TG-GEO160006 477. Bennett, J. W., D. T. Jones, R. J. Hamers, and S. E. Mason (2018), First-Principles and Thermodynamics Study of Compositionally Tuned Complex Metal Oxides: Cation Release from the (001) Surface of Mn-Rich Lithium Nickel Manganese Cobalt Oxide, Inorganic Chemistry, 57(21), 13300–13311, doi:10.1021/acs.inorgchem.8b01855. (published) [Comet, SDSC] 141. TG-GEO170003 478. Bajgain, S. K., M. Mookherjee, R. Dasgupta, D. B. Ghosh, and B. B. Karki (2019), Nitrogen Content in the Earth’s Outer Core, Geophysical Research Letters, 46(1), 89–98, doi:10.1029/2018gl080555. (published) 142. TG-IBN140002 479. Sivagnanam, S., Yoshimoto, K., Carnevale, T., Majumdar, A. 2018. “The Neuroscience Gateway – Enabling Large Scale Modeling and Data Processing in Neuroscience. PEARC18 (Pittsburgh). (published) [Comet, Jetstream, Science Gateways, SDSC, Stampede, TACC] 480. Strande, S., Cai, H., Cooper, T., Flammer, K., Irving, C., et al. 2017. Comet - Tales from the Long Tail - Two Years In and 10,000 Users Later. PEARC17 (New Orleans). (published) [Comet, Science Gateways, SDSC] 143. TG-IBN140007 481. Hadjiabadi, D., Raikov, I., Milstein, A., Soltesz, I. 2018. Investigating hippocampal feature selectivity at scale. Poster. Natural/Artificial Intelligence Symposium, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA. (published) 482. Milstein, A., Bittner, K., Grienberger, C., Romani, S., Magee, J., et al. 2018. Non-Hebbian synaptic de- potentiation enables hippocampal place field translocation.. Poster. Natural/Artificial Intelligence Symposium, Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA. (published) 483. Milstein, A., Bittner, K., Grienberger, C., Soltesz, I., Romani, S., et al. 2018. Place field translocation by bidirectional behavioral time-scale synaptic plasticity.. Poster. CoSyNe Meeting, Denver, CO. (accepted) 484. Raikov, I., Milstein, A., Soltesz, I. 2017. Determinants of Sparse Activity in a Full-Scale Model of the Dentate Gyrus. Poster. Society for Neuroscience meeting, Washington DC. (published) 144. TG-IRI120015 485. Dalmia, S., Kim, S., Metze, F. 2018. Situation Informed End-to-End ASR for Noisy Environments. Proc. CHiME. (published) [Bridges GPU, PSC] 486. Dalmia, S., Li, X., Metze, F., Alan W Black, . 2018. Domain Robust Feature Extraction for Rapid Low Resource ASR Development. Proc. SLT. (published) [Bridges GPU, PSC] 487. Kim, S., Metze, F. 2018. Dialog-Context Aware End-to-End Speech Recognition. Proc. SLT. (published) [Bridges GPU, PSC] 488. Palaskar, S., Metze, F. 2018. Acoustic-to-Word Recognition with Sequence-to-Sequence Models. Proc. SLT. (published) [PSC] 489. Sanabria, R., Metze, F. 2018. Hierarchical Multi Task Learning With CTC. Proc. SLT. (published) [Bridges GPU, PSC]

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490. Tseng, S., Li, J., Wang, Y., Metze, F., Szurley, J., et al. 2018. Multiple Instance Deep Learning for Weakly Supervised Small-Footprint Audio Event Detection. Proc. INTERSPEECH. (published) [Comet, SDSC] 491. Wang, Y., Li, J., Metze, F. 2018. Comparing the Max and Noisy-Or Pooling Functions in Multiple Instance Learning for Weakly Supervised Sequence Learning Tasks. Proc. INTERSPEECH. (published) [Comet, SDSC] 145. TG-MCA03S007 492. Buddhiraju, S., P. Santhanam, and S. Fan (2018), Thermodynamic limits of energy harvesting from outgoing thermal radiation, Proceedings of the National Academy of Sciences, 115(16), E3609–E3615, doi:10.1073/pnas.1717595115. (published) [Comet, SDSC] 493. Cai, L. et al. (2018), Spectrally Selective Nanocomposite Textile for Outdoor Personal Cooling, Advanced Materials, 30(35), 1802152, doi:10.1002/adma.201802152. (published) [Comet, SDSC] 494. Cerjan, A., M. Xiao, L. Yuan, and S. Fan (2018), Effects of non-Hermitian perturbations on Weyl Hamiltonians with arbitrary topological charges, Physical Review B, 97(7), doi:10.1103/physrevb.97.075128. (published) [Comet, SDSC] 495. Chen, K., B. Zhao, and S. Fan (2018), MESH: A free electromagnetic solver for far-field and near-field radiative heat transfer for layered periodic structures, Computer Physics Communications, 231, 163–172, doi:10.1016/j.cpc.2018.04.032. (published) [Comet, SDSC] 496. Fan, S., and A. Raman (2018), Metamaterials for radiative sky cooling, National Science Review, 5(2), 132– 133, doi:10.1093/nsr/nwy012. (published) [Comet, SDSC] 497. Fan, S., Y. Shi, and Q. Lin (2018), Nonreciprocal Photonics Without Magneto-Optics, IEEE Antennas and Wireless Propagation Letters, 17(11), 1948–1952, doi:10.1109/lawp.2018.2856258. (published) [Comet, SDSC] 498. Guo, C., M. Xiao, M. Minkov, Y. Shi, and S. Fan (2018), Photonic crystal slab Laplace operator for image differentiation, Optica, 5(3), 251, doi:10.1364/optica.5.000251. (published) [Comet, SDSC] 499. Hughes, T. W., M. Minkov, Y. Shi, and S. Fan (2018), Training of photonic neural networks through in situ backpropagation and gradient measurement, Optica, 5(7), 864, doi:10.1364/optica.5.000864. (published) [Comet, SDSC] 500. Isabella, O., R. Vismara, D. N. P. Linssen, K. X. Wang, S. Fan, and M. Zeman (2018), Advanced light trapping scheme in decoupled front and rear textured thin-film silicon solar cells, Solar Energy, 162, 344–356, doi:10.1016/j.solener.2018.01.040. (published) [Comet, SDSC] 501. Karakasoglu, I., M. Xiao, and S. Fan (2018), Polarization control with dielectric helix metasurfaces and arrays, Optics Express, 26(17), 21664, doi:10.1364/oe.26.021664. (published) [Comet, SDSC] 502. Kim, S. J., J.-H. Kang, M. Mutlu, J. Park, W. Park, K. E. Goodson, R. Sinclair, S. Fan, P. G. Kik, and M. L. Brongersma (2018), Anti-Hermitian photodetector facilitating efficient subwavelength photon sorting, Nature Communications, 9(1), doi:10.1038/s41467-017-02496-y. (published) [Comet, SDSC] 503. Lin, Q., X.-Q. Sun, M. Xiao, S.-C. Zhang, and S. Fan (2018), A three-dimensional photonic topological insulator using a two-dimensional ring resonator lattice with a synthetic frequency dimension, Science Advances, 4(10), eaat2774, doi:10.1126/sciadv.aat2774. (published) [Comet, SDSC] 504. Liu, H., C. Guo, G. Vampa, J. L. Zhang, T. Sarmiento, M. Xiao, P. H. Bucksbaum, J. Vučković, S. Fan, and D. A. Reis (2018), Enhanced high-harmonic generation from an all-dielectric metasurface, Nature Physics, 14(10), 1006–1010, doi:10.1038/s41567-018-0233-6. (published) [Comet, SDSC] 505. Liu, S.-C., D. Zhao, X. Ge, C. Reuterskiold-Hedlund, M. Hammar, S. Fan, Z. Ma, and W. Zhou (2018), Size Scaling of Photonic Crystal Surface Emitting Lasers on Silicon Substrates, IEEE Photonics Journal, 10(3), 1–6, doi:10.1109/jphot.2018.2829900. (published) [Comet, SDSC] 506. Li, W., and S. Fan (2018), Nanophotonic control of thermal radiation for energy applications [Invited], Optics Express, 26(12), 15995, doi:10.1364/oe.26.015995. (published) [Comet, SDSC] 507. Li, W., Y. Shi, Z. Chen, and S. Fan (2018), Photonic thermal management of coloured objects, Nature Communications, 9(1), doi:10.1038/s41467-018-06535-0. (published) [Comet, SDSC] 508. Li, Y., K.-J. Zhu, Y.-G. Peng, W. Li, T. Yang, H.-X. Xu, H. Chen, X.-F. Zhu, S. Fan, and C.-W. Qiu (2018), Thermal meta-device in analogue of zero-index photonics, Nature Materials, 18(1), 48–54, doi:10.1038/s41563-018- 0239-6. (published) [Comet, SDSC] 509. Minkov, M., and S. Fan (2018), Unidirectional light transport in dynamically modulated waveguides, Physical Review Applied, 10(4), doi:10.1103/physrevapplied.10.044028. (published) [Comet, SDSC] 510. Ono, M., K. Chen, W. Li, and S. Fan (2018), Self-adaptive radiative cooling based on phase change materials, Optics Express, 26(18), A777, doi:10.1364/oe.26.00a777. (published) [Comet, SDSC]

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511. Peng, Y. et al. (2018), Nanoporous polyethylene microfibres for large-scale radiative cooling fabric, Nature Sustainability, 1(2), 105–112, doi:10.1038/s41893-018-0023-2. (published) [Comet, SDSC] 512. Qin, C., L. Yuan, B. Wang, S. Fan, and P. Lu (2018), Effective electric-field force for a photon in a synthetic frequency lattice created in a waveguide modulator, Physical Review A, 97(6), doi:10.1103/physreva.97.063838. (published) [Comet, SDSC] 513. Shi, Y., Q. Lin, M. Minkov, and S. Fan (2018), Nonreciprocal Optical Dissipation Based on Direction-Dependent Rabi Splitting, IEEE Journal of Selected Topics in Quantum Electronics, 24(6), 1–7, doi:10.1109/jstqe.2018.2814792. (published) [Comet, SDSC] 514. Shi, Y., W. Li, A. Raman, and S. Fan (2017), Optimization of Multilayer Optical Films with a Memetic Algorithm and Mixed Integer Programming, ACS Photonics, 5(3), 684–691, doi:10.1021/acsphotonics.7b01136. (published) [Comet, SDSC] 515. Song, A. Y., P. B. Catrysse, and S. Fan (2018), Broadband Control of Topological Nodes in Electromagnetic Fields, Physical Review Letters, 120(19), doi:10.1103/physrevlett.120.193903. (published) [Comet, SDSC] 516. Sun, X.-Q., M. Xiao, T. Bzdušek, S.-C. Zhang, and S. Fan (2018), Three-Dimensional Chiral Lattice Fermion in Floquet Systems, Physical Review Letters, 121(19), doi:10.1103/physrevlett.121.196401. (published) [Comet, SDSC] 517. Trivedi, R., K. Fischer, S. Xu, S. Fan, and J. Vuckovic (2018), Few-photon scattering and emission from low- dimensional quantum systems, Physical Review B, 98(14), doi:10.1103/physrevb.98.144112. (published) [Comet, SDSC] 518. Wang, J., Y. Shi, T. Hughes, Z. Zhao, and S. Fan (2018), Adjoint-based optimization of active nanophotonic devices, Optics Express, 26(3), 3236, doi:10.1364/oe.26.003236. (published) [Comet, SDSC] 519. Yi, S., M. Zhou, Z. Yu, P. Fan, N. Behdad, D. Lin, K. X. Wang, S. Fan, and M. Brongersma (2018), Subwavelength angle-sensing photodetectors inspired by directional hearing in small animals, Nature Nanotechnology, 13(12), 1143–1147, doi:10.1038/s41565-018-0278-9. (published) [Comet, SDSC] 520. Yuan, L., Q. Lin, M. Xiao, and S. Fan (2018), Synthetic dimension in photonics, Optica, 5(11), 1396, doi:10.1364/optica.5.001396. (published) [Comet, SDSC] 521. Yuan, L., M. Xiao, Q. Lin, and S. Fan (2018), Synthetic space with arbitrary dimensions in a few rings undergoing dynamic modulation, Physical Review B, 97(10), doi:10.1103/physrevb.97.104105. (published) [Comet, SDSC] 522. Zhang, M., C. Wang, Y. Hu, A. Shams-Ansari, T. Ren, S. Fan, and M. Lončar (2018), Electronically programmable photonic molecule, Nature Photonics, 13(1), 36–40, doi:10.1038/s41566-018-0317-y. (published) [Comet, SDSC] 523. Zhao, B., P. Santhanam, K. Chen, S. Buddhiraju, and S. Fan (2018), Near-Field Thermophotonic Systems for Low-Grade Waste-Heat Recovery, Nano Letters, 18(8), 5224–5230, doi:10.1021/acs.nanolett.8b02184. (published) [Comet, SDSC] 524. Zhao, N., S. Verweij, W. Shin, and S. Fan (2018), Accelerating convergence of an iterative solution of finite difference frequency domain problems via schur complement domain decomposition, Optics Express, 26(13), 16925, doi:10.1364/oe.26.016925. (published) [Comet, SDSC] 525. Zhao, Z., T. W. Hughes, S. Tan, H. Deng, N. Sapra, R. J. England, J. Vuckovic, J. S. Harris, R. L. Byer, and S. Fan (2018), Design of a tapered slot waveguide dielectric laser accelerator for sub-relativistic electrons, Optics Express, 26(18), 22801, doi:10.1364/oe.26.022801. (published) [Comet, SDSC] 526. Zhao, Z., Y. Shi, K. Chen, and S. Fan (2018), Relation between absorption and emission directivities for dipoles coupled with optical antennas, Physical Review A, 98(1), doi:10.1103/physreva.98.013845. (published) [Comet, SDSC] 527. Zhu, L., Y. Guo, and S. Fan (2018), Theory of many-body radiative heat transfer without the constraint of reciprocity, Physical Review B, 97(9), doi:10.1103/physrevb.97.094302. (published) [Comet, SDSC] 146. TG-MCA03S014, TG-MCA03T014 528. Dacie, S., T. Török, P. Démoulin, M. G. Linton, C. Downs, L. van Driel-Gesztelyi, D. M. Long, and J. E. Leake (2018), Sequential Eruptions Triggered by Flux Emergence: Observations and Modeling, The Astrophysical Journal, 862(2), 117, doi:10.3847/1538-4357/aacce3. (published) 529. Dacie, S., T. Török, P. Démoulin, M. G. Linton, C. Downs, L. van Driel-Gesztelyi, D. M. Long, and J. E. Leake (2018), Sequential Eruptions Triggered by Flux Emergence: Observations and Modeling, The Astrophysical Journal, 862(2), 117, doi:10.3847/1538-4357/aacce3. (published)

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530. Dacie, S., T. Török, P. Démoulin, M. G. Linton, C. Downs, L. van Driel-Gesztelyi, D. M. Long, and J. E. Leake (2018), Sequential Eruptions Triggered by Flux Emergence: Observations and Modeling, The Astrophysical Journal, 862(2), 117, doi:10.3847/1538-4357/aacce3. (published) 531. Dima, G. I., J. R. Kuhn, D. Mickey, and C. Downs (2018), Using a New Infrared Si x Coronal Emission Line for Discriminating between Magnetohydrodynamic Models of the Solar Corona During the 2006 Solar Eclipse, The Astrophysical Journal, 852(1), 23, doi:10.3847/1538-4357/aa9e87. (published) 532. Dima, G. I., J. R. Kuhn, D. Mickey, and C. Downs (2018), Using a New Infrared Si x Coronal Emission Line for Discriminating between Magnetohydrodynamic Models of the Solar Corona During the 2006 Solar Eclipse, The Astrophysical Journal, 852(1), 23, doi:10.3847/1538-4357/aa9e87. (published) 533. Froment, C., F. Auchère, Z. Mikić, G. Aulanier, K. Bocchialini, E. Buchlin, J. Solomon, and E. Soubrié (2018), On the Occurrence of Thermal Nonequilibrium in Coronal Loops, The Astrophysical Journal, 855(1), 52, doi:10.3847/1538-4357/aaaf1d. (published) 534. Froment, C., F. Auchère, Z. Mikić, G. Aulanier, K. Bocchialini, E. Buchlin, J. Solomon, and E. Soubrié (2018), On the Occurrence of Thermal Nonequilibrium in Coronal Loops, The Astrophysical Journal, 855(1), 52, doi:10.3847/1538-4357/aaaf1d. (published) 535. Green, L. M., T. Török, B. Vršnak, W. Manchester, and A. Veronig (2018), The Origin, Early Evolution and Predictability of Solar Eruptions, Space Science Reviews, 214(1), doi:10.1007/s11214-017-0462-5. (published) 536. Green, L. M., T. Török, B. Vršnak, W. Manchester, and A. Veronig (2018), The Origin, Early Evolution and Predictability of Solar Eruptions, Space Science Reviews, 214(1), doi:10.1007/s11214-017-0462-5. (published) 537. Hu, Q., M. G. Linton, B. E. Wood, P. Riley, and T. Nieves-Chinchilla (2017), The Grad–Shafranov Reconstruction of Toroidal Magnetic Flux Ropes: First Applications, Solar Physics, 292(11), doi:10.1007/s11207-017-1195-z. (published) 538. Jia, Y., Pesnell, W., Liu, W., Downs, C., Bryans, P. 2018. Interaction between cool material from Sun-grazing comets and the low corona. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.2-36-18. (published) 539. Jia, Y., Pesnell, W., Liu, W., Downs, C., Bryans, P. 2018. Interaction between cool material from Sun-grazing comets and the low corona. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.2-36-18. (published) 540. Jia, Y., Pesnell, W., Liu, W., Downs, C., Bryans, P. 2018. Interaction between cool material from Sun-grazing comets and the low corona. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.2-36-18. (published) 541. Lin, H., Gibson, S., Savage, S., Tomczyk, S., Downs, C., et al. 2018. A Space Coronal Magnetometry Mission. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.3-38-18. (published) 542. Lin, H., Gibson, S., Savage, S., Tomczyk, S., Downs, C., et al. 2018. A Space Coronal Magnetometry Mission. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.3-38-18. (published) 543. Lin, H., Gibson, S., Savage, S., Tomczyk, S., Downs, C., et al. 2018. A Space Coronal Magnetometry Mission. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.3-38-18. (published) 544. Linker, J. 2018. Inputs to Ambient Solar Wind Modeling: Plans for International Space Weather Action Teams. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. PSW.5-15-18. (published) 545. Linker, J. 2018. Inputs to Ambient Solar Wind Modeling: Plans for International Space Weather Action Teams. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. PSW.5-15-18. (published) 546. Linker, J. 2018. Inputs to Ambient Solar Wind Modeling: Plans for International Space Weather Action Teams. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. PSW.5-15-18. (published) 547. Linton, M., Torok, T., Leake, J., Schuck, P. 2018. The Role of Emerging Magnetic Flux in the Initiation of Solar Eruptions. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.3-26-18. (published) 548. Linton, M., Torok, T., Leake, J., Schuck, P. 2018. The Role of Emerging Magnetic Flux in the Initiation of Solar Eruptions. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.3-26-18. (published) 549. Linton, M., Torok, T., Leake, J., Schuck, P. 2018. The Role of Emerging Magnetic Flux in the Initiation of Solar Eruptions. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.3-26-18. (published) 550. Liu, W., M. Jin, C. Downs, L. Ofman, M. C. M. Cheung, and N. V. Nitta (2018), A Truly Global Extreme Ultraviolet Wave from the SOL2017-09-10 X8.2+ Solar Flare-Coronal Mass Ejection, The Astrophysical Journal, 864(2), L24, doi:10.3847/2041-8213/aad77b. (published)

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551. Liu, W., M. Jin, C. Downs, L. Ofman, M. C. M. Cheung, and N. V. Nitta (2018), A Truly Global Extreme Ultraviolet Wave from the SOL2017-09-10 X8.2+ Solar Flare-Coronal Mass Ejection, The Astrophysical Journal, 864(2), L24, doi:10.3847/2041-8213/aad77b. (published) 552. Liu, W., M. Jin, C. Downs, L. Ofman, M. C. M. Cheung, and N. V. Nitta (2018), A Truly Global Extreme Ultraviolet Wave from the SOL2017-09-10 X8.2+ Solar Flare-Coronal Mass Ejection, The Astrophysical Journal, 864(2), L24, doi:10.3847/2041-8213/aad77b. (published) 553. Liu, W., Ofman, L., Nitta, N., Cheung, M., Downs, C., et al. 2018. The Best and Last of Solar Cycle 24 - The Global EUV Wave from the X8 Flare-CME Eruption on 2017-Sept-10: SDO/AIA Observations and Data-constrained Simulations. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.1-19-18. (published) 554. Liu, W., Ofman, L., Nitta, N., Cheung, M., Downs, C., et al. 2018. 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Boubrahimi (2018), Coronal Mass Ejection Data Clustering and Visualization of Decision Trees, The Astrophysical Journal Supplement Series, 236(1), 14, doi:10.3847/1538- 4365/aab76f. (published) 558. Ma, R., R. A. Angryk, P. Riley, and S. F. Boubrahimi (2018), Coronal Mass Ejection Data Clustering and Visualization of Decision Trees, The Astrophysical Journal Supplement Series, 236(1), 14, doi:10.3847/1538- 4365/aab76f. (published) 559. Mikić, Z. et al. (2018), Predicting the corona for the 21 August 2017 total solar eclipse, Nature Astronomy, 2(11), 913–921, doi:10.1038/s41550-018-0562-5. (published) 560. Mikić, Z. et al. (2018), Predicting the corona for the 21 August 2017 total solar eclipse, Nature Astronomy, 2(11), 913–921, doi:10.1038/s41550-018-0562-5. (published) 561. Mikić, Z. et al. (2018), Predicting the corona for the 21 August 2017 total solar eclipse, Nature Astronomy, 2(11), 913–921, doi:10.1038/s41550-018-0562-5. (published) 562. Owens, M. J., M. Lockwood, P. Riley, and J. Linker (2017), Sunward Strahl: A Method to Unambiguously Determine Open Solar Flux from In Situ Spacecraft Measurements Using Suprathermal Electron Data, Journal of Geophysical Research: Space Physics, 122(11), 10,980–10,989, doi:10.1002/2017ja024631. (published) 563. Owens, M. J., and P. Riley (2017), Probabilistic Solar Wind Forecasting Using Large Ensembles of Near-Sun Conditions With a Simple One-Dimensional “Upwind” Scheme, Space Weather, 15(11), 1461–1474, doi:10.1002/2017sw001679. (published) 564. Owens, M. J., P. Riley, and T. Horbury (2017), The Role of Empirical Space-Weather Models (in a World of Physics-Based Numerical Simulations), Proceedings of the International Astronomical Union, 13(S335), 254– 257, doi:10.1017/s1743921317007128. (published) 565. Owens, M. J., P. Riley, and T. Horbury (2017), The Role of Empirical Space-Weather Models (in a World of Physics-Based Numerical Simulations), Proceedings of the International Astronomical Union, 13(S335), 254– 257, doi:10.1017/s1743921317007128. (published) 566. Owens, M. J., P. Riley, and T. Horbury (2017), The Role of Empirical Space-Weather Models (in a World of Physics-Based Numerical Simulations), Proceedings of the International Astronomical Union, 13(S335), 254– 257, doi:10.1017/s1743921317007128. (published) 567. Raymond, J. C., C. Downs, M. M. Knight, K. Battams, S. Giordano, and R. Rosati (2018), Comet C/2011 W3 (Lovejoy) between 2 and 10 Solar Radii: Physical Parameters of the Comet and the Corona, The Astrophysical Journal, 858(1), 19, doi:10.3847/1538-4357/aabade. (published) 568. Raymond, J. C., C. Downs, M. M. Knight, K. Battams, S. Giordano, and R. Rosati (2018), Comet C/2011 W3 (Lovejoy) between 2 and 10 Solar Radii: Physical Parameters of the Comet and the Corona, The Astrophysical Journal, 858(1), 19, doi:10.3847/1538-4357/aabade. (published) 569. Raymond, J. C., C. Downs, M. M. Knight, K. Battams, S. Giordano, and R. Rosati (2018), Comet C/2011 W3 (Lovejoy) between 2 and 10 Solar Radii: Physical Parameters of the Comet and the Corona, The Astrophysical Journal, 858(1), 19, doi:10.3847/1538-4357/aabade. (published) 570. Reginald, N., O. St. Cyr, J. Davila, L. Rastaetter, and T. Török (2018), Evaluating Uncertainties in Coronal Electron Temperature and Radial Speed Measurements Using a Simulation of the Bastille Day Eruption, Solar Physics, 293(5), doi:10.1007/s11207-018-1301-x. (published)

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571. Reginald, N., O. St. Cyr, J. Davila, L. Rastaetter, and T. Török (2018), Evaluating Uncertainties in Coronal Electron Temperature and Radial Speed Measurements Using a Simulation of the Bastille Day Eruption, Solar Physics, 293(5), doi:10.1007/s11207-018-1301-x. (published) 572. Reginald, N., O. St. Cyr, J. Davila, L. Rastaetter, and T. Török (2018), Evaluating Uncertainties in Coronal Electron Temperature and Radial Speed Measurements Using a Simulation of the Bastille Day Eruption, Solar Physics, 293(5), doi:10.1007/s11207-018-1301-x. (published) 573. Riley, P., D. Baker, Y. D. Liu, P. Verronen, H. Singer, and M. Güdel (2017), Extreme Space Weather Events: From Cradle to Grave, Space Science Reviews, 214(1), doi:10.1007/s11214-017-0456-3. (published) 574. Riley, P., D. Baker, Y. D. Liu, P. Verronen, H. Singer, and M. Güdel (2017), Extreme Space Weather Events: From Cradle to Grave, Space Science Reviews, 214(1), doi:10.1007/s11214-017-0456-3. (published) 575. Riley, P., M. Ben-Nun, J. A. Linker, M. J. Owens, and T. S. Horbury (2017), Forecasting the properties of the solar wind using simple pattern recognition, Space Weather, 15(3), 526–540, doi:10.1002/2016sw001589. (published) 576. Riley, P., Lionello, R., Linker, J., Owens, M. 2018. The State of the Solar Wind, Magnetosphere, and Ionosphere During the Maunder Minimum. (published) 577. Riley, P., Lionello, R., Linker, J., Owens, M. 2018. The State of the Solar Wind, Magnetosphere, and Ionosphere During the Maunder Minimum. (published) 578. Riley, P., Lionello, R., Linker, J., Owens, M. 2018. The State of the Solar Wind, Magnetosphere, and Ionosphere During the Maunder Minimum. (published) 579. Riley, P., and J. J. Love (2017), Extreme geomagnetic storms: Probabilistic forecasts and their uncertainties, Space Weather, 15(1), 53–64, doi:10.1002/2016sw001470. (published) 580. Schwadron, N. A. et al. (2017), Particle Radiation Sources, Propagation and Interactions in Deep Space, at Earth, the Moon, Mars, and Beyond: Examples of Radiation Interactions and Effects, Space Science Reviews, 212(3-4), 1069–1106, doi:10.1007/s11214-017-0381-5. (published) 581. Schwadron, N., Mays, M., Linker, J., Spence, H., Townsend, L., et al. 2018. Coordinated Observations and Modeling of Accelerated Particles at the Sun and in the Inner Heliosphere. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.1-21-18. (published) 582. Schwadron, N., Mays, M., Linker, J., Spence, H., Townsend, L., et al. 2018. Coordinated Observations and Modeling of Accelerated Particles at the Sun and in the Inner Heliosphere. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.1-21-18. (published) 583. Schwadron, N., Mays, M., Linker, J., Spence, H., Townsend, L., et al. 2018. Coordinated Observations and Modeling of Accelerated Particles at the Sun and in the Inner Heliosphere. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.1-21-18. (published) 584. Shen, C., J. C. Raymond, Z. Mikić, J. A. Linker, K. K. Reeves, and N. A. Murphy (2017), Time-dependent Ionization in a Steady Flow in an MHD Model of the Solar Corona and Wind, The Astrophysical Journal, 850(1), 26, doi:10.3847/1538-4357/aa93f3. (published) 585. Titov, V. S., C. Downs, Z. Mikić, T. Török, J. A. Linker, and R. M. Caplan (2018), Regularized Biot–Savart Laws for Modeling Magnetic Flux Ropes, The Astrophysical Journal, 852(2), L21, doi:10.3847/2041-8213/aaa3da. (published) 586. Titov, V. S., C. Downs, Z. Mikić, T. Török, J. A. Linker, and R. M. Caplan (2018), Regularized Biot–Savart Laws for Modeling Magnetic Flux Ropes, The Astrophysical Journal, 852(2), L21, doi:10.3847/2041-8213/aaa3da. (published) 587. Titov, V., Linker, J., Mikic, Z., Downs, C., Torok, T., et al. 2018. Generalizing the RBSL-method for Flux Ropes with Various Current Profiles and Nonzero External Axial Field. 42nd COSPAR Scientific Assembly. Held 14- 22 July 2018. 42. E2.1-15-18. (published) 588. Titov, V., Linker, J., Mikic, Z., Downs, C., Torok, T., et al. 2018. Generalizing the RBSL-method for Flux Ropes with Various Current Profiles and Nonzero External Axial Field. 42nd COSPAR Scientific Assembly. Held 14- 22 July 2018. 42. E2.1-15-18. (published) 589. Titov, V., Linker, J., Mikic, Z., Downs, C., Torok, T., et al. 2018. Generalizing the RBSL-method for Flux Ropes with Various Current Profiles and Nonzero External Axial Field. 42nd COSPAR Scientific Assembly. Held 14- 22 July 2018. 42. E2.1-15-18. (published) 590. Torok, T. 2018. 3D MHD Modeling of Prominence Formation and Eruption. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.1-16-18. (published) 591. Torok, T. 2018. 3D MHD Modeling of Prominence Formation and Eruption. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.1-16-18. (published)

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592. Torok, T. 2018. 3D MHD Modeling of Prominence Formation and Eruption. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. E2.1-16-18. (published) 593. Török, T., C. Downs, J. A. Linker, R. Lionello, V. S. Titov, Z. Mikić, P. Riley, R. M. Caplan, and J. Wijaya (2018), Sun-to-Earth MHD Simulation of the 2000 July 14 “Bastille Day” Eruption, The Astrophysical Journal, 856(1), 75, doi:10.3847/1538-4357/aab36d. (published) 594. Török, T., C. Downs, J. A. Linker, R. Lionello, V. S. Titov, Z. Mikić, P. Riley, R. M. Caplan, and J. Wijaya (2018), Sun-to-Earth MHD Simulation of the 2000 July 14 “Bastille Day” Eruption, The Astrophysical Journal, 856(1), 75, doi:10.3847/1538-4357/aab36d. (published) 595. Torok, T., Linker, J., Mikic, Z., Riley, P., Titov, V., et al. 2018. Using MHD Simulations for Space-Weather Forecasting: Where do we Stand?. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.3-28-18. (published) 596. Torok, T., Linker, J., Mikic, Z., Riley, P., Titov, V., et al. 2018. Using MHD Simulations for Space-Weather Forecasting: Where do we Stand?. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.3-28-18. (published) 597. Torok, T., Linker, J., Mikic, Z., Riley, P., Titov, V., et al. 2018. Using MHD Simulations for Space-Weather Forecasting: Where do we Stand?. 42nd COSPAR Scientific Assembly. Held 14-22 July 2018. 42. D2.3-28-18. (published) 598. Winebarger, A. R., R. Lionello, C. Downs, Z. Mikić, and J. Linker (2018), Identifying Observables That Can Differentiate Between Impulsive and Footpoint Heating: Time Lags and Intensity Ratios, The Astrophysical Journal, 865(2), 111, doi:10.3847/1538-4357/aad9fb. (published) 599. Winebarger, A. R., R. Lionello, C. Downs, Z. Mikić, and J. Linker (2018), Identifying Observables That Can Differentiate Between Impulsive and Footpoint Heating: Time Lags and Intensity Ratios, The Astrophysical Journal, 865(2), 111, doi:10.3847/1538-4357/aad9fb. (published) 600. Winebarger, A. R., R. Lionello, C. Downs, Z. Mikić, and J. Linker (2018), Identifying Observables That Can Differentiate Between Impulsive and Footpoint Heating: Time Lags and Intensity Ratios, The Astrophysical Journal, 865(2), 111, doi:10.3847/1538-4357/aad9fb. (published) 601. Yeates, A. R. et al. (2018), Global Non-Potential Magnetic Models of the Solar Corona During the March 2015 Eclipse, Space Science Reviews, 214(5), doi:10.1007/s11214-018-0534-1. (published) 602. Yeates, A. R. et al. (2018), Global Non-Potential Magnetic Models of the Solar Corona During the March 2015 Eclipse, Space Science Reviews, 214(5), doi:10.1007/s11214-018-0534-1. (published) 603. Yeates, A. R. et al. (2018), Global Non-Potential Magnetic Models of the Solar Corona During the March 2015 Eclipse, Space Science Reviews, 214(5), doi:10.1007/s11214-018-0534-1. (published) 147. TG-MCA05S027 604. Arsiccio, A., J. McCarty, R. Pisano, and J.-E. Shea (2018), Effect of Surfactants on Surface-Induced Denaturation of Proteins: Evidence of an Orientation-Dependent Mechanism, The Journal of Physical Chemistry B, 122(49), 11390–11399, doi:10.1021/acs.jpcb.8b07368. (published) [Stampede, TACC] 605. Bennett, W. F. D., J.-E. Shea, and D. P. Tieleman (2018), Phospholipid Chain Interactions with Cholesterol Drive Domain Formation in Lipid Membranes, Biophysical Journal, 114(11), 2595–2605, doi:10.1016/j.bpj.2018.04.022. (published) [Stampede, TACC] 606. Do, T. D., J. W. Checco, M. Tro, J.-E. Shea, M. T. Bowers, and J. V. Sweedler (2018), Conformational investigation of the structure–activity relationship of GdFFD and its analogues on an achatin-like neuropeptide receptor of Aplysia californica involved in the feeding circuit, Physical Chemistry Chemical Physics, 20(34), 22047–22057, doi:10.1039/c8cp03661f. (published) [Stampede, TACC] 148. TG-MCA05S028 607. Winogradoff, D., Aksimentiev, A. 2019. Molecular Mechanism of Spontaneous Nucleosome Unraveling. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 149. TG-MCA08X012 608. Sarangapani, P., Chu, Y., Wang, K., Valencia, D., Charles, J., et al. 2018. Nonequilibrium Green’s function method: Transport and band tail predictions in transition metal dichalcogenides. International Conference on Simulation of Semiconductor Processes and Devices (SISPAD) 2018 (Austin, USA). (accepted)

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150. TG-MCA08X012 609. Sarangapani, P., Chu, Y., Wang, K., Valencia, D., Charles, J., et al. 2018. Nonequilibrium Green's function method: Transport and band tail predictions in transition metal dichalcogenides. International Conference on Simulation of Semiconductor Processes and Devices (Austin). (accepted) 151. TG-MCA08X016 610. Korre, L. 2018. Dynamics of weakly non-Boussinesq convection, convective overshooting and magnetic field cofinement in a spherical shell. Ph.D. thesis. (published) 611. Korre, L., Garaud, P., Brummell, N. 2018. Convective overshooting and penetration in a Boussinesq shell. (submitted) [Ranch, Stampede, Stampede2, TACC] 612. Manek, B., N. Brummell, and D. Lee (2018), The Rise of a Magnetic Flux Tube in a Background Field: Solar Helicity Selection Rules, The Astrophysical Journal, 859(2), L27, doi:10.3847/2041-8213/aac723. (published) [Ranch, Stampede, Stampede2, TACC] 613. Wood, T. S., and N. H. Brummell (2018), A Self-consistent Model of the Solar Tachocline, The Astrophysical Journal, 853(2), 97, doi:10.3847/1538-4357/aaa6d5. (published) [Ranch, Stampede, TACC] 152. TG-MCA93S002 614. Basak, S., Bazavov, A., Bernard, C., DeTar, C., Levkova, L., et al. 2018. Lattice computation of the electromagnetic contributions to kaon and pion masses. (submitted) [Ranch, Stampede] 615. Bazavov, A., Bernard, C., Brambilla, N., Brown, N., DeTar, C., et al. 2018. B- and D-meson leptonic decay constants and quark masses from four-flavor lattice QCD. (accepted) [Ranch, Stampede] 616. Bazavov, A. et al. (2018), Up-, down-, strange-, charm-, and bottom-quark masses from four-flavor lattice QCD, Physical Review D, 98(5), doi:10.1103/physrevd.98.054517. (published) [Ranch, Stampede, TACC] 617. Bazavov, A., Bernard, C., Brown, N., DeTar, C., El Khadra, A., et al. 2018. B- and D-meson leptonic decay constants from four-flavor lattice QCD. (submitted) [Ranch, Stampede, TACC] 618. Bazavov, A., Bernard, C., DeTar, C., Du, D., El-Khadra, A., et al. 2018. . DOI: from Kℓ3 decay and four-flavor lattice QCD}, (Invalid?). (journal={Physical Review D}) [t] 619. Bernard, C., and D. Toussaint (2018), Effects of nonequilibrated topological charge distributions on pseudoscalar meson masses and decay constants, Physical Review D, 97(7), doi:10.1103/physrevd.97.074502. (published) [Ranch, TACC] 620. Chakraborty, B. et al. (2018), Strong-Isospin-Breaking Correction to the Muon Anomalous Magnetic Moment from Lattice QCD at the Physical Point, Physical Review Letters, 120(15), doi:10.1103/physrevlett.120.152001. (published) [Ranch, Stampede, TACC] 621. DeTar, C., S. Gottlieb, R. Li, and D. Toussaint (2018), MILC Code Performance on High End CPU and GPU Supercomputer Clusters, edited by M. Della Morte, P. Fritzsch, E. Gámiz Sánchez, and C. Pena Ruano, EPJ Web of Conferences, 175, 02009, doi:10.1051/epjconf/201817502009. (published) [Ranch, Stampede, TACC] 622. Liu, Y. et al. (2018), Bs → Kℓv form factors with 2+1 flavors, edited by M. Della Morte, P. Fritzsch, E. Gámiz Sánchez, and C. Pena Ruano, EPJ Web of Conferences, 175, 13008, doi:10.1051/epjconf/201817513008. (published) [Ranch, Stampede] 153. TG-MCA94P017 623. Aydin, F., N. Courtemanche, T. D. Pollard, and G. A. Voth (2018), Gating mechanisms during actin filament elongation by formins, eLife, 7, doi:10.7554/elife.37342. (published) 624. Bidone, T. C., A. Polley, A. E. P. Durumeric, T. Driscoll, D. Iwamoto, D. Calderwood, M. A. Schwartz, and G. A. Voth (2017), New Insights into the Conformational Activation of Full-Length Integrin, , doi:10.1101/203661. (published) 625. Harker, A. J., H. H. Katkar, T. C. Bidone, F. Aydin, G. A. Voth, D. A. Applewhite, and D. R. Kovar (2018), Ena/VASP processive elongation is modulated by avidity on actin filaments bundled by the filopodia crosslinker fascin, , doi:10.1101/386961. (published) 626. Katkar, H. H., A. Davtyan, A. E. P. Durumeric, G. M. Hocky, A. C. Schramm, E. M. De La Cruz, and G. A. Voth (2018), Insights into the Cooperative Nature of ATP Hydrolysis in Actin Filaments, Biophysical Journal, 115(8), 1589–1602, doi:10.1016/j.bpj.2018.08.034. (published) 627. Madsen, J. J., J. M. A. Grime, J. S. Rossman, and G. A. Voth (2018), Entropic forces drive clustering and spatial localization of influenza A M2 during viral budding, Proceedings of the National Academy of Sciences, 115(37), E8595–E8603, doi:10.1073/pnas.1805443115. (published)

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628. Mayes, H. B., S. Lee, A. D. White, G. A. Voth, and J. M. J. Swanson (2018), Multiscale Kinetic Modeling Reveals an Ensemble of Cl–/H+ Exchange Pathways in ClC-ec1 Antiporter, Journal of the American Chemical Society, 140(5), 1793–1804, doi:10.1021/jacs.7b11463. (published) 629. Oakes, P. W., T. C. Bidone, Y. Beckham, A. V. Skeeters, G. R. Ramirez-San Juan, S. P. Winter, G. A. Voth, and M. L. Gardel (2018), Lamellipodium is a myosin-independent mechanosensor, Proceedings of the National Academy of Sciences, 115(11), 2646–2651, doi:10.1073/pnas.1715869115. (published) 630. Prévost, C., M. E. Sharp, N. Kory, Q. Lin, G. A. Voth, R. V. Farese, and T. C. Walther (2018), Mechanism and Determinants of Amphipathic Helix-Containing Protein Targeting to Lipid Droplets, Developmental Cell, 44(1), 73–86.e4, doi:10.1016/j.devcel.2017.12.011. (published) 631. Sun, R., Y. Han, J. M. J. Swanson, J. S. Tan, J. P. Rose, and G. A. Voth (2018), Molecular transport through membranes: Accurate permeability coefficients from multidimensional potentials of mean force and local diffusion constants, The Journal of Chemical Physics, 149(7), 072310, doi:10.1063/1.5027004. (published) 154. TG-MCA95C003 632. Hawley, J., Krolik, J. 2018. Sound Speed Dependence of Alignment in Accretion Disks Subjected to Lense– Thirring Torques. (published) [Stampede, TACC] 633. Kinch, B., Schnittman, J., Kallman, T., Krolik, J. 2018. Predicting Stellar-Mass Black Hole X-ray Spectra From Simulations. (submitted) [TACC] 155. TG-MCA99S015 634. Kogut, J., Sinclair, D. 2018. Complex Langevin Simulations of QCD at Finite Density. Progress Report. Lattice 2018 (MIchigan State Univeristy, East Lansing, MI). EPJ Web Conf 175, 07031 (2018). (published) 156. TG-MCB070039N 635. English, L. R., A. Tischer, A. K. Demeler, B. Demeler, and S. T. Whitten (2018), Sequence Reversal Prevents Chain Collapse and Yields Heat-Sensitive Intrinsic Disorder, Biophysical Journal, 115(2), 328–340, doi:10.1016/j.bpj.2018.06.006. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 636. Gorbet, G. E., S. Mohapatra, and B. Demeler (2018), Multi-speed sedimentation velocity implementation in UltraScan-III, European Biophysics Journal, 47(7), 825–835, doi:10.1007/s00249-018-1297-z. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 637. Johnson, C. N., G. E. Gorbet, H. Ramsower, J. Urquidi, L. Brancaleon, and B. Demeler (2018), Multi-wavelength analytical ultracentrifugation of human serum albumin complexed with porphyrin, European Biophysics Journal, 47(7), 789–797, doi:10.1007/s00249-018-1301-7. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 638. Kim, H., E. Brookes, W. Cao, and B. Demeler (2018), Two-dimensional grid optimization for sedimentation velocity analysis in the analytical ultracentrifuge, European Biophysics Journal, 47(7), 837–844, doi:10.1007/s00249-018-1309-z. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 639. Salveson, P. J., S. Haerianardakani, A. Thuy-Boun, S. Yoo, A. G. Kreutzer, B. Demeler, and J. S. Nowick (2018), Repurposing Triphenylmethane Dyes to Bind to Trimers Derived from Aβ, Journal of the American Chemical Society, 140(37), 11745–11754, doi:10.1021/jacs.8b06568. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 640. Salveson, P. J., S. Haerianardakani, A. Thuy-Boun, S. Yoo, A. G. Kreutzer, B. Demeler, and J. S. Nowick (2018), Correction to “Repurposing Triphenylmethane Dyes To Bind to Trimers Derived from Aβ,” Journal of the American Chemical Society, 140(45), 15546–15546, doi:10.1021/jacs.8b11195. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 641. Sterritt, O. W., E. J. M. Lang, S. A. Kessans, T. M. Ryan, B. Demeler, G. B. Jameson, and E. J. Parker (2018), Structural and functional characterisation of the entry point to pyocyanin biosynthesis in Pseudomonas aeruginosa defines a new 3-deoxy-d-arabino-heptulosonate 7-phosphate synthase subclass, Bioscience Reports, 38(5), BSR20181605, doi:10.1042/bsr20181605. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 642. Wang, Z. et al. (2018), Functionality of Redox-Active Cysteines Is Required for Restriction of Retroviral Replication by SAMHD1, Cell Reports, 24(4), 815–823, doi:10.1016/j.celrep.2018.06.090. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC]

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643. Williams, T. L., G. E. Gorbet, and B. Demeler (2018), Multi-speed sedimentation velocity simulations with UltraScan-III, European Biophysics Journal, 47(7), 815–823, doi:10.1007/s00249-018-1308-0. (published) [Comet, Gordon, IU, Jetstream, Lonestar, Science Gateways, SDSC, Stampede, Stampede2, TACC] 157. TG-MCB070073N 644. Gardner, J. M., and C. F. Abrams (2018), Lipid flip-flop vs. lateral diffusion in the relaxation of hemifusion diaphragms, Biochimica et Biophysica Acta (BBA) - Biomembranes, 1860(7), 1452–1459, doi:10.1016/j.bbamem.2018.04.007. (published) [Stampede2, TACC] 645. Gossert, S. T., B. Parajuli, I. Chaiken, and C. F. Abrams (2018), Roles of conserved tryptophans in trimerization of HIV-1 membrane-proximal external regions: Implications for virucidal design via alchemical free-energy molecular simulations, Proteins: Structure, Function, and Bioinformatics, 86(7), 707–711, doi:10.1002/prot.25504. (published) [Stampede2, TACC] 646. Srikanth, A., E. Kinaci, J. Vergara, G. Palmese, and C. F. Abrams (2018), The effect of alkyl chain length on mechanical properties of fatty-acid-functionalized amidoamine-epoxy systems, Computational Materials Science, 150, 70–76, doi:10.1016/j.commatsci.2018.03.073. (published) [Stampede2, TACC] 158. TG-MCB100147 647. Gilbert, D. G. (2018), Genes of the Pig, Sus scrofa, reconstructed with EvidentialGene, , doi:10.1101/412130. (published) [IU, SDSC] 159. TG-MCB110014 648. Bao, H., D. Das, N. A. Courtney, Y. Jiang, J. S. Briguglio, X. Lou, D. Roston, Q. Cui, B. Chanda, and E. R. Chapman (2018), Dynamics and number of trans-SNARE complexes determine nascent fusion pore properties, Nature, 554(7691), 260–263, doi:10.1038/nature25481. (published) [Stampede, TACC] 649. Cai, X., K. Haider, J. Lu, S. Radic, C. Y. Son, Q. Cui, and M. R. Gunner (2018), Network analysis of a proposed exit pathway for protons to the P-side of cytochrome c oxidase, Biochimica et Biophysica Acta (BBA) - Bioenergetics, 1859(10), 997–1005, doi:10.1016/j.bbabio.2018.05.010. (published) [Comet, LSU, Stampede, Stampede2, TACC] 650. Condon, S. G. F. et al. (2017), The FtsLB subcomplex of the bacterial divisome is a tetramer with an uninterrupted FtsL helix linking the transmembrane and periplasmic regions, Journal of Biological Chemistry, 293(5), 1623–1641, doi:10.1074/jbc.ra117.000426. (published) [Comet, SDSC, Stampede, SuperMIC, TACC] 651. Doǧangün, M., P. E. Ohno, D. Liang, A. C. McGeachy, A. G. Bé, N. Dalchand, T. Li, Q. Cui, and F. M. Geiger (2018), Hydrogen-Bond Networks near Supported Lipid Bilayers from Vibrational Sum Frequency Generation Experiments and Atomistic Simulations, The Journal of Physical Chemistry B, 122(18), 4870–4879, doi:10.1021/acs.jpcb.8b02138. (published) [Comet, LSU, SDSC, Stampede, Stampede2, Standford, SuperMIC, TACC, XStream] 652. Liang, D., J. Hong, D. Fang, J. W. Bennett, S. E. Mason, R. J. Hamers, and Q. Cui (2018), Analysis of the conformational properties of amine ligands at the gold/water interface with QM, MM and QM/MM simulations, Physical Chemistry Chemical Physics, 20(5), 3349–3362, doi:10.1039/c7cp06709g. (published) [Comet, SDSC, Stampede, SuperMIC, TACC] 653. McGeachy, A. C., E. R. Caudill, D. Liang, Q. Cui, J. A. Pedersen, and F. M. Geiger (2018), Counting charges on membrane-bound peptides, Chemical Science, 9(18), 4285–4298, doi:10.1039/c8sc00804c. (published) [Comet, LSU, SDSC, Stampede, Stampede2, Standford, SuperMIC, TACC, XStream] 654. Nomura, Y., D. Roston, E. J. Montemayor, Q. Cui, and S. E. Butcher (2018), Structural and mechanistic basis for preferential deadenylation of U6 snRNA by Usb1, Nucleic Acids Research, 46(21), 11488–11501, doi:10.1093/nar/gky812. (published) [Comet, LSU, SDSC, Stampede, Stampede2, SuperMIC, TACC] 655. Santos, T. M. A., M. G. Lammers, M. Zhou, I. L. Sparks, M. Rajendran, D. Fang, C. L. Y. De Jesus, G. F. R. Carneiro, Q. Cui, and D. B. Weibel (2017), Small Molecule Chelators Reveal That Iron Starvation Inhibits Late Stages of Bacterial Cytokinesis, ACS Chemical Biology, 13(1), 235–246, doi:10.1021/acschembio.7b00560. (published) [SDSC, Stampede, SuperMIC, TACC] 656. Son, C. Y., J. G. McDaniel, Q. Cui, and A. Yethiraj (2018), Conformational and Dynamic Properties of Poly(ethylene oxide) in BMIM+BF4–: A Microsecond Computer Simulation Study Using ab Initio Force Fields, Macromolecules, 51(14), 5336–5345, doi:10.1021/acs.macromol.8b01002. (published) [Comet, LSU, SDSC, Stampede, Stampede2, Standford, SuperMIC, TACC, XStream]

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657. Vujović, M., M. Huynh, S. Steiner, P. Garcia‐Fernandez, M. Elstner, Q. Cui, and M. Gruden (2018), Exploring the applicability of density functional tight binding to transition metal ions. Parameterization for nickel with the spin‐polarized DFTB3 model, Journal of Computational Chemistry, 40(2), 400–413, doi:10.1002/jcc.25614. (published) [LSU, SDSC, Stampede, SuperMIC, TACC] 658. Zheng, Y., and Q. Cui (2018), Multiple Pathways and Time Scales for Conformational Transitions in apo- Adenylate Kinase, Journal of Chemical Theory and Computation, 14(3), 1716–1726, doi:10.1021/acs.jctc.7b01064. (published) [Comet, LSU, SDSC, Stampede, Stampede2, Standford, SuperMIC, TACC, XStream] 160. TG-MCB110144 659. Guo, Z., and J. T. Kindt (2018), Partitioning of Size-Mismatched Impurities to Grain Boundaries in 2d Solid Hard-Sphere Monolayers, Langmuir, 34(43), 12947–12956, doi:10.1021/acs.langmuir.8b02633. (published) [Comet, SDSC] 660. Zhang, X., J. G. Arce Nunez, and J. T. Kindt (2019), Derivation of micelle size-dependent free energies of aggregation for octyl phosphocholine from molecular dynamics simulation, Fluid Phase Equilibria, 485, 83– 93, doi:10.1016/j.fluid.2018.12.001. (published) [Comet, SDSC] 161. TG-MCB130108, TG-MCB140287 661. Raguimova, O. N., N. Smolin, E. Bovo, S. Bhayani, J. M. Autry, A. V. Zima, and S. L. Robia (2018), Redistribution of SERCA calcium pump conformers during intracellular calcium signaling, Journal of Biological Chemistry, 293(28), 10843–10856, doi:10.1074/jbc.ra118.002472. (published) 162. TG-MCB130177 662. Paraskevakos, I., A. Luckow, M. Khoshlessan, G. Chantzialexiou, T. E. Cheatham, O. Beckstein, G. C. Fox, and S. Jha (2018), Task-parallel Analysis of Molecular Dynamics Trajectories, Proceedings of the 47th International Conference on Parallel Processing - ICPP 2018, doi:10.1145/3225058.3225128. (published) [Comet, SDSC, TACC, Wrangler] 663. Seyler, S. L., A. Kumar, M. F. Thorpe, and O. Beckstein (2015), Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways, edited by E. Tajkhorshid, PLOS Computational Biology, 11(10), e1004568, doi:10.1371/journal.pcbi.1004568. (published) [Stampede, TACC] 163. TG-MCB140174 664. Mu, X., Q. Wang, L.-P. Wang, S. D. Fried, J.-P. Piquemal, K. N. Dalby, and P. Ren (2014), Modeling Organochlorine Compounds and the σ-Hole Effect Using a Polarizable Multipole Force Field, The Journal of Physical Chemistry B, 118(24), 6456–6465, doi:10.1021/jp411671a. (published) 665. Wang, L.-P., T. Head-Gordon, J. W. Ponder, P. Ren, J. D. Chodera, P. K. Eastman, T. J. Martinez, and V. S. Pande (2013), Systematic Improvement of a Classical Molecular Model of Water, The Journal of Physical Chemistry B, 117(34), 9956–9972, doi:10.1021/jp403802c. (published) 164. TG-MCB140228 666. Marino, S., Hult, C., Wolberg, P., Linderman, J., Kirschner, D. 2019. The role of dimensionality in understanding granuloma formation. (submitted) [Comet, SDSC, Stampede, TACC] 165. TG-MCB150019, XSEDE_ALLOCATION_REQUEST 667. Bhattacharya, A. 2018. How do hydrophobic surfaces modulate the conformational equilibria of protein backbones?. ACS Annual Meeting (Boston, MA). 256. (published) 166. TG-MCB150059 668. Johnson, M. E. (2018), Modeling the Self-Assembly of Protein Complexes through a Rigid-Body Rotational Reaction–Diffusion Algorithm, The Journal of Physical Chemistry B, 122(49), 11771–11783, doi:10.1021/acs.jpcb.8b08339. (published) [LSU, SuperMIC] 669. Yogurtcu, O. N., and M. E. Johnson (2018), Cytosolic proteins can exploit membrane localization to trigger functional assembly, edited by P. M. Kasson, PLOS Computational Biology, 14(3), e1006031, doi:10.1371/journal.pcbi.1006031. (published) [Comet, LSU, Stampede, SuperMIC, TACC]

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167. TG-MCB150142 670. Pantelopulos, G. A., and J. E. Straub (2018), Regimes of Complex Lipid Bilayer Phases Induced by Cholesterol Concentration in MD Simulation, Biophysical Journal, 115(11), 2167–2178, doi:10.1016/j.bpj.2018.10.011. (published) 168. TG-MCB160005 671. Alred, E. J., I. Lodangco, J. Gallaher, and U. H. E. Hansmann (2018), Mutations Alter RNA-Mediated Conversion of Human Prions, ACS Omega, 3(4), 3936–3944, doi:10.1021/acsomega.7b02007. (published) 672. Bernhardt, N. A., and U. H. E. Hansmann (2018), Multifunnel Landscape of the Fold-Switching Protein RfaH- CTD, The Journal of Physical Chemistry B, 122(5), 1600–1607, doi:10.1021/acs.jpcb.7b11352. (published) 673. Xi, W., and U. H. E. Hansmann (2018), Conversion between parallel and antiparallel β-sheets in wild-type and Iowa mutant Aβ40 fibrils, The Journal of Chemical Physics, 148(4), 045103, doi:10.1063/1.5016166. (published) 674. Xi, W., E. K. Vanderford, and U. H. E. Hansmann (2018), Out-of-Register Aβ42 Assemblies as Models for Neurotoxic Oligomers and Fibrils, Journal of Chemical Theory and Computation, 14(2), 1099–1110, doi:10.1021/acs.jctc.7b01106. (published) 169. TG-MCB160013 675. Niesen, M. J. M., A. Müller-Lucks, R. Hedman, G. von Heijne, and T. F. Miller (2018), Forces on Nascent Polypeptides during Membrane Insertion and Translocation via the Sec Translocon, Biophysical Journal, 115(10), 1885–1894, doi:10.1016/j.bpj.2018.10.002. (published) [Bridges Regular, PSC, Pylon] 170. TG-MCB160059 676. Ricci, C. G., J. S. Chen, Y. Miao, M. Jinek, J. A. Doudna, J. A. McCammon, and G. Palermo (2018), Molecular mechanism of off-target effects in CRISPR-Cas9, , doi:10.1101/421537. (published) [Comet, SDSC] 677. Palermo, G., J. S. Chen, C. G. Ricci, I. Rivalta, M. Jinek, V. S. Batista, J. A. Doudna, and J. A. McCammon (2018), Key role of the REC lobe during CRISPR–Cas9 activation by “sensing”, “regulating”, and “locking” the catalytic HNH domain, Quarterly Reviews of Biophysics, 51, doi:10.1017/s0033583518000070. (published) [Comet, SDSC] 171. TG-MCB160167, TG-MCB170057 678. Bowerman, S., J. E. Curtis, J. Clayton, E. H. Brookes, and J. Weresczczynski (2018), BEES: Bayesian Ensemble Estimation from SAS, a SASSIE-web Module, , doi:10.1101/400168. (published) [IU, Jetstream, TACC] 172. TG-MCB160183 679. Mohammadiarani, H., V. S. Shaw, R. R. Neubig, and H. Vashisth (2018), Interpreting Hydrogen–Deuterium Exchange Events in Proteins Using Atomistic Simulations: Case Studies on Regulators of G-Protein Signaling Proteins, The Journal of Physical Chemistry B, 122(40), 9314–9323, doi:10.1021/acs.jpcb.8b07494. (published) [Comet, SDSC] 173. TG-MCB170003 680. Walgenbach, D. G., A. J. Gregory, and J. C. Klein (2018), Unique methionine-aromatic interactions govern the calmodulin redox sensor, Biochemical and Biophysical Research Communications, 505(1), 236–241, doi:10.1016/j.bbrc.2018.09.052. (published) [Stampede] 174. TG-MCB170036 681. Benler, S., Cobian-Guemes, A., McNair, K., Hung, S., Levi, K., et al. 2018. A diversity-generating retroelement encoded by a globally ubiquitous Bacteroides phage. (accepted) 175. TG-MCB170052 682. Athreya, N. B. M., A. Sarathy, and J.-P. Leburton (2018), Large Scale Parallel DNA Detection by Two- Dimensional Solid-State Multipore Systems, ACS Sensors, 3(5), 1032–1039, doi:10.1021/acssensors.8b00192. (published)

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683. Sarathy, A., N. B. Athreya, L. R. Varshney, and J.-P. Leburton (2018), Classification of Epigenetic Biomarkers with Atomically Thin Nanopores, The Journal of Physical Chemistry Letters, 9(19), 5718–5725, doi:10.1021/acs.jpclett.8b02200. (published) 176. TG-MCB170057 684. Brookes, E., and M. Rocco (2018), Recent advances in the UltraScan SOlution MOdeller (US-SOMO) hydrodynamic and small-angle scattering data analysis and simulation suite, European Biophysics Journal, 47(7), 855–864, doi:10.1007/s00249-018-1296-0. (published) [IU, Jetstream, TACC] 177. TG-MCB170059 685. Martin, T., Brinkley, G., Whitten, D., Chi, E., Evans, D. 2019. Molecular Dynamics of Oligo-p-Phenylene Ethynylenes and Amyloid-Beta Aggregates: Potential Sensing Mechanism. (in preparation) 178. TG-MCB170069 686. Shi, L., X. Meng, E. Tseng, M. Mascagni, and Z. Wang (2018), SpaRC: scalable sequence clustering using Apache Spark, edited by I. Birol, Bioinformatics, doi:10.1093/bioinformatics/bty733. (published) 179. TG-MCB170096 687. Quinn, C. M., M. Wang, M. P. Fritz, B. Runge, J. Ahn, C. Xu, J. R. Perilla, A. M. Gronenborn, and T. Polenova (2018), Dynamic regulation of HIV-1 capsid interaction with the restriction factor TRIM5α identified by magic-angle spinning NMR and molecular dynamics simulations, Proceedings of the National Academy of Sciences, 115(45), 11519–11524, doi:10.1073/pnas.1800796115. (published) [Bridges Large, Bridges Regular, PSC, Pylon] 180. TG-MCB170129 688. Miao, Y., A. Bhattarai, A. T. N. Nguyen, A. Christopoulos, and L. T. May (2018), Structural Basis for Binding of Allosteric Drug Leads in the Adenosine A1 Receptor, Scientific Reports, 8(1), doi:10.1038/s41598-018- 35266-x. (published) [Comet, SDSC] 181. TG-MCB180007 689. Hong, L., B. P. Vani, E. H. Thiede, M. J. Rust, and A. R. Dinner (2018), Molecular dynamics simulations of nucleotide release from the circadian clock protein KaiC reveal atomic-resolution functional insights, Proceedings of the National Academy of Sciences, 201812555, doi:10.1073/pnas.1812555115. (published) [Bridges Regular, Comet, Data Oasis, PSC, Pylon, SDSC] 182. TG-MPS180001 690. Alekseenko, A., Grandilli, A., Wood, A. 2018. An ultra-sparse approximation of kinetic solutions to spatially homogeneous flows of non-continuum gas. (submitted) [Bridges Regular, Comet, Globus Online, PSC, SDSC, Stampede, Stampede2, TACC] 183. TG-MSS090046 691. Cheng, G., Yin, S., Chang, T., Richter, G., Gao, H., et al. 2017. Anomalous tensile detwinning in twinned nanowires. (published) 692. Chen, W., Zhou, H., Liu, Z., Ketkaew, J., Li, N., et al. 2017. Processing effects on fracture toughness of metallic glasses. (published) 693. Chen, W., Zhou, H., Liu, Z., Ketkaew, J., Shao, L., et al. 2018. Test sample geometry for fracture toughness measurements of bulk metallic glasses. (published) 694. Kim, W., Steele, A., Zhu, W., Csatary, E., Fricke, N., et al. 2018. Discovery and Optimization of nTZDpa as an Antibiotic Effective Against Bacterial Persisters. (published) 695. Kim, W., Zhu, W., Hendricks, G., Van Tyne, D., Steele, A., et al. 2018. A new class of synthetic retinoid antibiotics effective against bacterial persisters. (published) 696. Li, J., Ni, B., Zhang, T., Gao, H. 2018. Phase field crystal modeling of grain boundary structures and growth in polycrystalline graphene. (published) 697. Pan, Q., Zhou, H., Lu, Q., Gao, H., Lu, L. 2017. History-independent cyclic response of nanotwinned metals. (published)

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698. Yi, X., Zou, G., Gao, H. 2018. Mechanics of cellular packing of nanorods with finite and non-uniform diameters. (published) 699. Zou, G., Yi, X., Zhu, W., Gao, H. 2018. Packing of flexible 2D materials in vesicles. (published) 700. Zou, G., Yi, X., Zhu, W., Gao, H. 2018. Packing of flexible nanofibers in vesicles. (published) 184. TG-MSS140006 701. Bae, S., O. Galant, C. E. Diesendruck, and M. N. Silberstein (2018), The Effect of Intrachain Cross-Linking on the Thermomechanical Behavior of Bulk Polymers Assembled Solely from Single Chain Polymer Nanoparticles, Macromolecules, 51(18), 7160–7168, doi:10.1021/acs.macromol.8b01027. (published) [Stampede, TACC] 185. TG-MSS170004 702. Sui, J., J. Chen, X. Zhang, G. Nie, and T. Zhang (2018), Symplectic Analysis of Wrinkles in Elastic Layers With Graded Stiffnesses, Journal of Applied Mechanics, 86(1), 011008, doi:10.1115/1.4041620. (published) [Comet, SDSC] 186. TG-NCR130002 703. Bayatpour, M., S. Chakraborty, H. Subramoni, X. Lu, and D. K. (DK) Panda (2017), Scalable reduction collectives with data partitioning-based multi-leader design, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC ’17, doi:10.1145/3126908.3126954. (published) 704. Chakraborty, S., H. Subramoni, and D. K. Panda (2017), Contention-Aware Kernel-Assisted MPI Collectives for Multi-/Many-Core Systems, 2017 IEEE International Conference on Cluster Computing (CLUSTER), doi:10.1109/cluster.2017.106. (published) [Gordon, SDSC, Stampede, Stampede2, TACC] 705. Gugnani, S., Lu, X., Panda, D. 2018. Analyzing, Modeling, and Provisioning QoS for NVMe SSDs. 11th IEEE/ACM International Conference on Utility and Cloud Computing (Zurich, Switzerland). (accepted) 706. Hashmi, J. M., S. Chakraborty, M. Bayatpour, H. Subramoni, and D. K. Panda (2018), Designing Efficient Shared Address Space Reduction Collectives for Multi-/Many-cores, 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), doi:10.1109/ipdps.2018.00111. (published) [Gordon, SDSC, Stampede2, TACC] 707. Ruhela, A., H. Subramoni, S. Chakraborty, M. Bayatpour, P. Kousha, and D. K. Panda (2018), Efficient Asynchronous Communication Progress for MPI without Dedicated Resources, Proceedings of the 25th European MPI Users’ Group Meeting on - EuroMPI’18, doi:10.1145/3236367.3236376. (published) [Stampede, Stampede2, TACC] 708. Shi, H., X. Lu, D. Shankar, and D. K. (DK) Panda (2018), High-Performance Multi-Rail Erasure Coding Library over Modern Data Center Architectures, Proceedings of the ACM Symposium on Cloud Computing - SoCC ’18, doi:10.1145/3267809.3275472. (published) 709. Zhang, J., Lu, X., Panda, D. 2017. Designing Locality and NUMA Aware MPI Runtime for Nested Virtualization based HPC Cloud with SR-IOV Enabled InfiniBand.. 13th ACM SIGPLAN/SIGOPS International Conference on Virtual Execution Environments (VEE '17) (Xi'an, China). (published) 187. TG-NCR130002 710. Awan, A., Chu, C., Subramoni, H., Lu, X., Panda, D. 2018. OC-DNN: Exploiting Advanced Unified Memory Capabilities in CUDA 9 and Volta GPUs for Out-of-Core DNN Training. 25th IEEE International Conference on High Performance Computing, Data, and Analytics (Bengaluru, India). (accepted) 711. Awan, A., Chu, C., Subramoni, H., Panda, D. 2018. Optimized Broadcast for Deep Learning Workloads on Dense-GPU InfiniBand Clusters: MPI or NCCL?. The EuroMPI 2018 Conference (Barcelona, Spain). (published) 712. Chu, C.-H., X. Lu, A. A. Awan, H. Subramoni, B. H. Elton, and D. K. Panda (2018), Exploiting Hardware Multicast and GPUDirect RDMA for Efficient Broadcast, IEEE Transactions on Parallel and Distributed Systems, 1–1, doi:10.1109/tpds.2018.2867222. (published) [Bridges GPU, Comet, OSG, PSC, SDSC, Stampede2, TACC] 188. TG-OCE130026 713. Cessi, P. (2018), The Effect of Northern Hemisphere Winds on the Meridional Overturning Circulation and Stratification, Journal of Physical Oceanography, 48(10), 2495–2506, doi:10.1175/jpo-d-18-0085.1. (published)

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714. Ferreira, D. et al. (2018), Atlantic-Pacific Asymmetry in Deep Water Formation, Annual Review of Earth and Planetary Sciences, 46(1), 327–352, doi:10.1146/annurev-earth-082517-010045. (published) 715. Jones, C. S., and P. Cessi (2018), Components of Upper-Ocean Salt Transport by the Gyres and the Meridional Overturning Circulation, Journal of Physical Oceanography, 48(10), 2445–2456, doi:10.1175/jpo-d-18- 0005.1. (published) 189. TG-OCE170017 716. Bednarsek, N., Feely, R., Howes, E., Hunt, B., Kessouri, F., et al. 2018. Synthesis of thresholds of ocean acidification effects on pelagic gastropods. (in preparation) 717. Howard, E., Penn, J., Frenzel, H., Kessouri, F., McWilliams, J., et al. 2018. Fish habitat compression by temperature dependent hypoxia in the California Current System.. (in preparation) 718. Kessouri, F., McWilliams, J., Bianchi, D., Renault, L., Deutsch, C., et al. 2018. Effects of submesoscale eddies on nutrients cycles in the California Current System. (in preparation) 719. Kessouri, F., McWilliams, J., Renault, L., Bianchi, D., Frenzel, H., et al. 2018. Wind eddy interaction effects on plankton productivity in the California Current System. (in preparation) 720. Kessouri, F., Sutula, M., Sengupta, A., Bianchi, D., McKune, K., et al. 2018. Terrestrial and atmospheric forcings for Southern California Bight ocean modeling. (in preparation) 721. Renault, L., McWilliams, J., Jousse, A., Deutsch, C., Frenzel, H., et al. 2018. Revisiting the California Current System using uncoupled oceanic and atmospheric models. (in preparation) 722. Yang, A., Bianchi, D., Babbin, A., Deutsch, C. 2018. Drivers of N2O outgassing in the Peruvian Upwelling System. (in preparation) 190. TG-PHY060011N 723. Hill, R. J., P. Kammel, W. J. Marciano, and A. Sirlin (2018), Nucleon axial radius and muonic hydrogen—a new analysis and review, Reports on Progress in Physics, 81(9), 096301, doi:10.1088/1361-6633/aac190. (published) 191. TG-PHY060027N 724. Abbott, B. P. (2017), Observation of Gravitational Waves from a Binary Black Hole Merger, Centennial of General Relativity, 291–311, doi:10.1142/9789814699662_0011. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 725. Abbott, B. P. et al. (2017), Estimating the Contribution of Dynamical Ejecta in the Kilonova Associated with GW170817, The Astrophysical Journal, 850(2), L39, doi:10.3847/2041-8213/aa9478. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 726. Abbott, B. P. et al. (2017), On the Progenitor of Binary Neutron Star Merger GW170817, The Astrophysical Journal, 850(2), L40, doi:10.3847/2041-8213/aa93fc. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 727. Abbott, B. P. et al. (2017), GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral, Physical Review Letters, 119(16), doi:10.1103/physrevlett.119.161101. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 728. Abbott, B. P. et al. (2017), Gravitational Waves and Gamma-Rays from a Binary Neutron Star Merger: GW170817 and GRB 170817A, The Astrophysical Journal, 848(2), L13, doi:10.3847/2041-8213/aa920c. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 729. Abbott, B. P. et al. (2017), A gravitational-wave standard siren measurement of the Hubble constant, Nature, doi:10.1038/nature24471. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 730. Abbott, B. P. et al. (2017), Search for Post-merger Gravitational Waves from the Remnant of the Binary Neutron Star Merger GW170817, The Astrophysical Journal, 851(1), L16, doi:10.3847/2041-8213/aa9a35. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 731. Abbott, B. P. et al. (2017), GW170608: Observation of a 19 Solar-mass Binary Black Hole Coalescence, The Astrophysical Journal, 851(2), L35, doi:10.3847/2041-8213/aa9f0c. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 732. Abbott, B. P. et al. (2018), Constraints on cosmic strings using data from the first Advanced LIGO observing run, Physical Review D, 97(10), doi:10.1103/physrevd.97.102002. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC]

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733. Abbott, B. P. et al. (2018), GW170817: Implications for the Stochastic Gravitational-Wave Background from Compact Binary Coalescences, Physical Review Letters, 120(9), doi:10.1103/physrevlett.120.091101. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 734. Abbott, B. P. et al. (2018), Search for Tensor, Vector, and Scalar Polarizations in the Stochastic Gravitational- Wave Background, Physical Review Letters, 120(20), doi:10.1103/physrevlett.120.201102. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 735. Healy, J., and C. O. Lousto (2018), Hangup effect in unequal mass binary black hole mergers and further studies of their gravitational radiation and remnant properties, Physical Review D, 97(8), doi:10.1103/physrevd.97.084002. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 736. Healy, J., C. O. Lousto, I. Ruchlin, and Y. Zlochower (2018), Evolutions of unequal mass, highly spinning black hole binaries, Physical Review D, 97(10), doi:10.1103/physrevd.97.104026. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede2, TACC] 192. TG-PHY090003 737. Barack, L., Cardoso, V., Nissanke, S., Sotiriou, T., Sperhake, U., et al. 2018. Black holes, gravitational waves and fundamental physics: a roadmap. (submitted) [Comet, Data Oasis, Stampede] 738. Cook, W., Wang, D., Sperhake, U. 2018. Orbiting black-hole binaries and apparent horizons in higher dimensions. (submitted) [Comet, Data Oasis, SDSC, Stampede, TACC] 739. Sperhake, U., Berti, E., Cardoso, V., Cook, W., Pretorius, F., et al. 2018. The high-energy collision of black holes in higher dimensions. (in preparation) [Comet, Data Oasis, Ranch, SDSC, Stampede, TACC] 193. TG-PHY100016 740. Palenzuela, C., S. L. Liebling, D. Neilsen, L. Lehner, O. L. Caballero, E. O’Connor, and M. Anderson (2015), Effects of the microphysical equation of state in the mergers of magnetized neutron stars with neutrino cooling, Physical Review D, 92(4), doi:10.1103/physrevd.92.044045. (published) 194. TG-PHY100053 741. East, W. E. (2018), Massive Boson Superradiant Instability of Black Holes: Nonlinear Growth, Saturation, and Gravitational Radiation, Physical Review Letters, 121(13), doi:10.1103/physrevlett.121.131104. (published) [Comet, SDSC, Stampede, TACC] 742. Yang, H., W. E. East, V. Paschalidis, F. Pretorius, and R. F. P. Mendes (2018), Evolution of highly eccentric binary neutron stars including tidal effects, Physical Review D, 98(4), doi:10.1103/physrevd.98.044007. (published) [Comet, SDSC, Stampede, TACC] 195. TG-PHY110016 743. Walker, J., S. Boldyrev, and N. F. Loureiro (2018), Influence of tearing instability on magnetohydrodynamic turbulence, Physical Review E, 98(3), doi:10.1103/physreve.98.033209. (published) [Stampede2, TACC] 196. TG-PHY130027 744. Fraser, A. E., P. W. Terry, E. G. Zweibel, and M. J. Pueschel (2017), Coupling of damped and growing modes in unstable shear flow, Physics of Plasmas, 24(6), 062304, doi:10.1063/1.4985322. (published) 745. Williams, Z. R., M. J. Pueschel, P. W. Terry, and T. Hauff (2017), Turbulence, transport, and zonal flows in the Madison symmetric torus reversed-field pinch, Physics of Plasmas, 24(12), 122309, doi:10.1063/1.5000252. (published) 197. TG-PHY140041 746. Werner, G. R., A. A. Philippov, and D. A. Uzdensky (2018), Particle acceleration in relativistic magnetic reconnection with strong inverse-Compton cooling in pair plasmas, Monthly Notices of the Royal Astronomical Society: Letters, 482(1), L60–L64, doi:10.1093/mnrasl/sly157. (published) [Stampede2, TACC] 198. TG-PHY160053 747. George, D., and E. A. Huerta (2018), Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data, Physics Letters B, 778, 64–70, doi:10.1016/j.physletb.2017.12.053. (published) [Comet, SDSC, Stampede, TACC]

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748. George, D., H. Shen, and E. A. Huerta (2018), Classification and unsupervised clustering of LIGO data with Deep Transfer Learning, Physical Review D, 97(10), doi:10.1103/physrevd.97.101501. (published) [Comet, SDSC, Stampede, TACC] 749. Huerta, E. A. et al. (2018), Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers, Physical Review D, 97(2), doi:10.1103/physrevd.97.024031. (published) [Comet, SDSC, Stampede, TACC] 199. TG-PHY170023 750. Jin, W., Schiros, T., Lin, Y., Ma, J., Lou, R., et al. 2018. Phase transition and electronic structure evolution of MoTe2 induced by W substitution. DOI:10.1103/physrevb.98.144114 (Invalid?). (published) [Comet, SDSC] 200. TG-PHY170027, TG-PHY170049 751. Liang, E., Gao, L., Lu, Y., Follet, R., Sio, H., et al. 2018. Mega-Gauss Plasma Jet Creation Using a Ring of Laser Beams. (submitted) [Stampede2, TACC] 752. Lu, Y., Tzeferacos, P., Liang, E., Follett, R., Gao, L., et al. 2018. Numerical Simulation of magnetized jet creation using a hollow ring of laser beams. (submitted) [Stampede2, TACC] 201. TG-PHY170042 753. Xu, B., T. Feng, Z. Li, S. T. Pantelides, and Y. Wu (2018), Constructing Highly Porous Thermoelectric Monoliths with High-Performance and Improved Portability from Solution-Synthesized Shape-Controlled Nanocrystals, Nano Letters, 18(6), 4034–4039, doi:10.1021/acs.nanolett.8b01691. (published) [Bridges Regular, Comet, PSC, SDSC] 754. Xu, B., Feng, T., Li, Z., Zhou, L., Pantelides, S., et al. 2018. Creating Zipper-Like van der Waals Gap Discontinuity in Low-Temperature-Processed Nanostructured PbBi2n Te1+3n : Enhanced Phonon Scattering and Improved Thermoelectric Performance. DOI:10.1002/anie.201805890 (Invalid?). (published) [Bridges Regular, Comet, PSC, SDSC] 202. TG-SES140024 755. Lungeanu, A., Park, P., DeChurch, L., Contractor, N. 2018. Deep Space Collaboration: Impact of Latency and Social Networks on Collaborative Work. . 104th Annual Convention National Communication Association (NCA) (Salt Lake City, UT). (accepted) 756. Lungeanu, A., Park, P., DeChurch, L., Contractor, N. 2018. Deep Space Collaboration: Impact of Latency and Social Networks on Collaborative Work. XXXVIII Sunbelt Conference (Utrecht, The Netherlands). (accepted) 757. Lungeanu, A., Park, P., DeChurch, L., Contractor, N. 2018. Deep Space Collaboration: Impact of Latency and Social Networks on Collaborative Work. . 104th Annual Convention National Communication Association (NCA) (Salt Lake City, UT). (accepted) 758. Park, P., Lungeanu, A., DeChurch, L., Contractor, N. 2018. CREST: Crew Recommender for Effectively Switching Tasks. 2018 NASA Human Research Program Investigators' Workshop (Galveston, TX). (accepted) 203. TG-SES170012 759. Mohiuddin, S., Salam, S., Khan, L., Brandt, P., D'Orazio, V. 2017. Re-PAIR: Recommend political actors in real- time from news websites. IEEE International Conference on Big Data (Big Data) (Boston, MA). 1333-1340. (published) [IU, Jetstream, Science Gateways] 204. TG-SES170012 760. Gunasekaran, A., Imani, M., Khan, L., Grant, C., Brandt, P., et al. 2018. SPERG: Scalable Political Event Report Geoparsing in Big Data. IEEE International Conference on Big Data (BigData 2018) (Seattle, WA). (published) [IU, Jetstream, Science Gateways] 205. XSEDE_ALLOCATION_REQUEST 761. Alavi, A., M. Ruffalo, A. Parvangada, Z. Huang, and Z. Bar-Joseph (2018), A web server for comparative analysis of single-cell RNA-seq data, Nature Communications, 9(1), doi:10.1038/s41467-018-07165-2. (published) 762. Ameen, T. A., H. Ilatikhameneh, J. Z. Huang, M. Povolotskyi, R. Rahman, and G. Klimeck (2017), Combination of Equilibrium and Nonequilibrium Carrier Statistics Into an Atomistic Quantum Transport Model for

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Tunneling Heterojunctions, IEEE Transactions on Electron Devices, 64(6), 2512–2518, doi:10.1109/ted.2017.2690626. (published) 763. Charles, J., Kais, S., Kubis, T. 2018. Modeling Molecules in Interacting Environments. ACS National Meeting and Exposition 2018 (New Orleans, USA). (accepted) 764. Haschke, S., M. Mader, S. Schlicht, A. M. Roberts, A. M. Angeles-Boza, J. A. C. Barth, and J. Bachmann (2018), Direct oxygen isotope effect identifies the rate-determining step of electrocatalytic OER at an oxidic surface, Nature Communications, 9(1), doi:10.1038/s41467-018-07031-1. (published) 765. Karra, V. 2017. Modeling the formation of collagen-mimetic triple helices and microfibrils. DOI:10.7282/t3959mqw (Invalid?). (published) 766. Libardo, M. D. J. et al. (2018), Phagosomal Copper-Promoted Oxidative Attack on Intracellular Mycobacterium tuberculosis, ACS Infectious Diseases, 4(11), 1623–1634, doi:10.1021/acsinfecdis.8b00171. (published) 767. Liu, K., El-Gohary, N. 2016. Ontology-based sequence labelling for automated information extraction for supporting bridge data analytics. 2016 International Conference on Sustainable Design, Engineering, and Construction (Tempe, AZ). (published) 768. Liu, K., El-Gohary, N. 2016. Semantic modeling of bridge deterioration knowledge for supporting big bridge data analytics. 2016 ASCE Construction Research Congres (Puerto Rico). (published) 769. Liu, K., El-Gohary, N. 2017. Ontology-based semi- supervised conditional random fields for automated information extraction from bridge inspection reports. (published) 770. Liu, K., El-Gohary, N. 2017. Similarity-based dependency parsing for extracting dependency relations from bridge inspection reports. 2017 ASCE International Workshop on Computing in Civil Engineering (Seattle, WA). (published) 771. Liu, K., El-Gohary, N. 2017. Ontology-based data integration for supporting big bridge data analytics. 2017 Canadian Society for Civil Engineering (CSCE) Annual Conference (Vancouver, BC, Canada). (published) 772. Liu, K., El-Gohary, N. 2018. Learning from class- imbalanced bridge and weather data for supporting bridge deterioration prediction. 35th International Council for Research and Innovation in Building Construction (Chicago, IL). (published) 773. Liu, K., El-Gohary, N. 2018. Unsupervised named entity normalization for supporting information fusion for big bridge data analytics. 25th International Workshop on Intelligent Computing in Engineering (Lausanne, Switzerland). (published) 774. Liu, K., El-Gohary, N. 2018. Feature discretization and selection methods for supporting bridge deterioration prediction. 2018 ASCE Construction Research Congress (New Orleans, LA). (published) 775. Liu, P., El-Gohary, N. 2018. Automatic annotation of web images for domain-specific crack classification. 35th International Council for Research and Innovation in Building Construction (Chicago, IL). (published) 776. Luo, S.-X., J. S. Cannon, B. L. H. Taylor, K. M. Engle, K. N. Houk, and R. H. Grubbs (2016), Z-Selective Cross- Metathesis and Homodimerization of 3E-1,3-Dienes: Reaction Optimization, Computational Analysis, and Synthetic Applications, Journal of the American Chemical Society, 138(42), 14039–14046, doi:10.1021/jacs.6b08387. (published) 777. Maeda, K., T. Colonius, A. D. Maxwell, W. Kreider, and M. R. Bailey (2018), Modeling and numerical simulation of the bubble cloud dynamics in an ultrasound field for burst wave lithotripsy, The Journal of the Acoustical Society of America, 144(3), 1780–1780, doi:10.1121/1.5067866. (published) 778. Maeda, K., A. D. Maxwell, T. Colonius, W. Kreider, and M. R. Bailey (2018), Energy shielding by cavitation bubble clouds in burst wave lithotripsy, The Journal of the Acoustical Society of America, 144(5), 2952– 2961, doi:10.1121/1.5079641. (published) 779. Makwana, K., H. Li, F. Guo, and X. Li (2017), Dissipation and particle energization in moderate to low beta turbulent plasma via PIC simulations, Journal of Physics: Conference Series, 837, 012004, doi:10.1088/1742- 6596/837/1/012004. (published) 780. Marras, A. E., Z. Shi, M. G. Lindell, R. A. Patton, C.-M. Huang, L. Zhou, H.-J. Su, G. Arya, and C. E. Castro (2018), Cation-Activated Avidity for Rapid Reconfiguration of DNA Nanodevices, ACS Nano, 12(9), 9484–9494, doi:10.1021/acsnano.8b04817. (published) 781. Pan, L., and S. G. Aller (2018), Allosteric Role of Substrate Occupancy Toward the Alignment of P- glycoprotein Nucleotide Binding Domains, Scientific Reports, 8(1), doi:10.1038/s41598-018-32815-2. (published) 782. Pan, L., and J. P. Segrest (2016), Computational studies of plasma lipoprotein lipids, Biochimica et Biophysica Acta (BBA) - Biomembranes, 1858(10), 2401–2420, doi:10.1016/j.bbamem.2016.03.010. (published)

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783. Pongkitiwanichakul, P., K. D. Makwana, and D. Ruffolo (2018), Driving reconnection in sheared magnetic configurations with forced fluctuations, Physics of Plasmas, 25(2), 022114, doi:10.1063/1.5014026. (published) 784. Schneider, T. W., M. T. Hren, M. Z. Ertem, and A. M. Angeles-Boza (2018), [RuII(tpy)(bpy)Cl]+-Catalyzed reduction of carbon dioxide. Mechanistic insights by carbon-13 kinetic isotope effects, Chemical Communications, 54(61), 8518–8521, doi:10.1039/c8cc03009j. (published) 785. Unnikrishnan, U., J. C. Oefelein, and V. Yang (2018), Direct Numerical Simulation of a Turbulent Reacting Liquid-Oxygen/Methane Mixing Layer at Supercritical Pressure, 2018 Joint Propulsion Conference, doi:10.2514/6.2018-4564. (published) [GaTech] 786. Venkatesh, I., Mehra, V., Wang, Z., Califf, B., Blackmore, M. 2018. Developmental chromatin restriction of pro- growth gene networks acts as an epigenetic barrier to axon regeneration in cortical neurons. DOI:10.1002/dneu.22605 (Invalid?). (published) 787. Wang, C., G. L. Rosner, and R. B. S. Roden (2018), A Bayesian design for phase I cancer therapeutic vaccine trials, Statistics in Medicine, doi:10.1002/sim.8021. (published) 788. Wang, X., H. Huo, U. Unnikrishnan, and V. Yang (2018), A systematic approach to high-fidelity modeling and efficient simulation of supercritical fluid mixing and combustion, Combustion and Flame, 195, 203–215, doi:10.1016/j.combustflame.2018.04.030. (published) [GaTech] 789. Wang, Z., V. Mehra, M. T. Simpson, B. Maunze, A. Chakraborty, L. Holan, E. Eastwood, M. G. Blackmore, and I. Venkatesh (2018), KLF6 and STAT3 co-occupy regulatory DNA and functionally synergize to promote axon growth in CNS neurons, Scientific Reports, 8(1), doi:10.1038/s41598-018-31101-5. (published)

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13. Collaborations XSEDE considers collaboration within the research community to be essential to the advancement of all fields of research. It is XSEDE’s policy and practice to encourage and engage in collaborations, where applicable. In addition to the collaborations with Service Providers via the SP Forum (§14), XSEDE collaborates with NSF awardees and other domestic and international projects. The following list represents the active collaborations with NSF awardees:

NSF Proposal Title PI/Contact Award Amount Abstract David Mainstreaming Volunteer Computing 1105572 $548,546.00 Anderson SI2-SSI: SciDaaS -- Scientific data management as a service Ian Foster 1148484 $2,398,999.00 for small/medium labs Collaborative Research: Integrated HPC Systems Usage and Performance of Resources Monitoring and Modeling Abani Patra 1203560 $458,561.00 (SUPReMM- SUNY Buffalo) Collaborative Research: Integrated HPC Systems Usage and Performance of Resources Monitoring and Modeling Abani Patra 1203604 $457,919.00 (SUPReMM- UT-Austin) Center for Trustworthy Scientific Cyberinfrastructure Von Welch 1234408 $4,518,845.00 (CTSC) Latin America-US Institute 2013: Methods in Computational Kevin Franklin 1242216 $99,986.00 Discovery for Multidimensional Problem Solving EAGER proposal: Toward a Distributed Knowledge Environment for Research into Cyberinfrastructure: Data, Nicholas 1348461 $204,935.00 Tools, Measures, and Models for Multidimensional Berente Innovation Network Analysis Multiscale Software for Quantum Simulations in Materials Jerzy Bernholc 1339844 $500,000.00 Design, Nano Science and Technology MRI: Acquisition of SuperMIC—A Heterogeneous Computing Environment to Enable Transformation of Seung-Jong 1338051 $3,924,181.00 Computational Research and Education in the State of Park Louisiana Open Gateway Computing Environments Science Gateways Marlon Pierce 1339774 $2,500,000.00 Platform as a Service (OGCE SciGaP) Sustaining Globus Toolkit for the NSF Community (Sustain- Steven Tuecke 1339873 $1,200,000.00 GT) CC-NIE Integration: Developing Applications with Kathy L. 1341005 $650,000.00 Networking Capabilities via End-to-End SDN (DANCES) Benninger A Large-Scale, Community-Driven Experimental Dr. Kate 1419141 $10,049,765.00 Environment for Cloud Research Keahey MRI: Acquisition of a National CyberGIS Facility for Computing- and Data-Intensive Geospatial Research and Shaowen Wang 1429699 $1,787,335.00 Education

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Acquisition of an Extreme GPU cluster for Interdisciplinary Todd Martinez 1429830 $3,500,000.00 Research The Centrality of Advanced Digitally-Enabled Science: Donna Cox 1445176 $1,499,535.00 CADENS CloudLab: Flexible Scientific Infrastructure to Support Fundamental Advances in Cloud Architectures and Robert Ricci 1419199 $9,999,999.00 Applications Dr. Kerk F. Kee, RUI: CAREER Organizational Capacity and Capacity Building Almadena Y. 1453864 $519,753.00 for Cyberinfrastructure Diffusion Chtchelkanova Fostering Successful Innovative Large-Scale, Distributed Science and Engineering Projects through Integrated Nicolas Berente 1551609 $100,000.00 Collaboration EarthCube RCN: Collaborative Research: Research Coordination Network for High Performance Distributed Allen Pope 1541620 $299,977.00 Computing in the Polar Sciences MRI Collaborative Consortium: Acquisition of a Shared Supercomputer by the Rocky Mountain Advanced Thomas Hauser 1532236 $2,730,000.00 Computing Consortium BD Hubs: Midwest: SEEDCorn: Sustainable Enabling Environment for Data Collaboration that you are proposing in response to the NSF Big Data Regional Innovation Hubs Edward Seidel 1550320 $1,499,999.00 (BD Hubs): Accelerating the Big Data Innovation Ecosystem (NSF 15-562) solicitation Secure Data Architecture: Shared Intelligence Platform for Protecting our National Cyberinfrastructure" that you are Alexander 1547249 $499,206.00 proposing in response to the NSF Cybersecurity Innovation Withers for Cyberinfrastructure (NSF 15-549) solicitation CILogon 2.0 project that you are proposing in response to the NSF Cybersecurity Innovation for Cyberinfrastructure James Basney 1547268 $499,973.00 (NSF 15-549) solicitation DIBBs: Merging Science and Cyberinfrastructure Pathways: Bertram 1541450 $4,986,951.00 The Whole Tale Ludaescher Associated Universities, Inc. (AUI) and the National Radio Philip J. Puxley 1519126 $1.00 Astronomy Observatory (NRAO) SI2-SSE: Multiscale Software for Quantum Simulations of J. Bernholc 1615114 $29,232.00 Nanostructured Materials and Devices Collaborative Research: SI2-SSI: Adding Volunteer David 1550601 $259,999.00 Computing to the Research Cyberinfrastructure Anderson Molecular Sciences Software Institute (MolSSI) that you are Thomas proposing in response to the NSF Scientific Software 1547580 $5,880,491.00 Crawford Innovation Institutes (S2I2, NSF 15-553) solicitation Science Gateways Software Institute for NSF Scientific Nancy Wilkins- Software Innovation Institutes (S2I2, NSF 15-553) 1547611 $6,599,000.00 Diehr solicitation CC* Compute: BioBurst in response to the Campus Cyberinfrastructure (CC*) Program solicitation (NSF 16- Ron Hawkins 1659104 $494,066.00 567)

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CC* Networking Infrastructure: Building HPRNet (High- Farzad Performance Research Network) for advancement of data 1659255 $499,745.00 Mashayek intensive research and collaboration Cybertraining: CIP–Professional Training for Dirk Colbry 1730137 $498,330.00 CyberAmbassadors SI2-SSI: Pegasus: Automating compute and data intensive Ewa Deelman 1664162 $2,500,000.00 science Renata Quantum Mechanical Modeling of Major Mantle Materials 0635990 $805,227.00 Wentacovitch MRI: Acquisition of the Lawrence Supercomputer to Doug 1626516 $504,911.00 Advance Multidisciplinary Research in South Dakota Jennewein Collaborative Research: CyberTraining: CIU: Hour of Cyberinfrastructure: Developing Cyber Literacy for Eric Shook 1829708 $373,990.00 Geographic Information Science CC* NPEO: A Sustainable Center for Engagement and Jennifer M. 1826994 $1,166,667.00 Networks Schopf Collaborative Research: Building the Community for the Alex Szalay 1747493 $165,185.00 Open Storage Network CICI: CSRC: Research Security Operations Center Von Welch 1840034 $4,933,641.00 (ResearchSOC) CC* NPEO: Towards the National Research Platform Larry Smarr 1826967 $2,500,000.00 Collaborative Research: ABI Sustaining: The National Center Thomas Doak 1759906 $313,673.00 for Genome Analysis SI2-SSI Collaborative Research: SCALE-MS-Scalable Peter Kasson 1835780 $763,289.00 Adaptive Large Ensembles of Molecular Simulations A Workshop to Jumpstart High-Performance Computing in Finance/ BIGDATA: IA: Collaborative Research: Mao Ye 1838183 $422,288.00 Understanding the Financial Market Ecosystem The following list represents other active formal domestic and international collaborations:

Project Collaboration Summary Domestic Collaborations Marshall University - Campus Indiana University CRI staff visited Marshall University for server build and Bridging Site (CRI) XSEDE software toolkit installation Southern Illinois University - Indiana University CRI staff visited Southern Illinois University for server Campus Bridging Site (CRI) build and XSEDE software toolkit installation Bentley University - Campus Indiana University CRI staff visited Bentley University for server build and Bridging Site (CRI) XSEDE software toolkit installation University Texas El Paso - Campus Indiana University CRI staff visited University Texas El Paso for server build Bridging Site (CRI) and XSEDE software toolkit installation Brandeis University - Campus Indiana University CRI staff visited Brandeis University for server build and Bridging Site (CRI) XSEDE software toolkit installation Incorporated Research - Indiana University partnering with IRIS to use the Jetstream system to Institutions for Seismology (IRIS) disseminate data to the research community CyVerse Indiana University partnered with CyVerse projects to deploy and operate the Jetstream system as part of the XSEDE ecosystem

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National Center for Genome Indiana University partnered with the National Center for Genome Analysis Analysis Support (NCGAS) Support to use the Jetstream system for creation of virtual machine images for research and analysis of genome data South Dakota State University - Indiana University CRI staff visited SDSU for cluster build and XSEDE Campus Bridging Site software toolkit installation Slippery Rock University – Indiana University CRI staff visited Slippery Rock University for server build Campus Bridging Site and XSEDE software toolkit installation Use of XRAS by NCAR/CISL Agreement to use XRAS for managing the NCAR allocations Use of XRAS by NCAR/EOL Agreement to use XRAS to manage EOL Lower Atmosphere Observing Facilities (LAOF) allocations Use of XRAS by Blue Waters Agreement to use XRAS for managing the Blue Waters allocations Use of Information Services by Agreement to use Information Services to enable resource discovery in the UIUC UIUC Research Portal (https://researchit.illinois.edu) Delivering Kepler to XSEDE Users XSEDE Providing Capability Integration Assistance to make Kepler available to XSEDE users Open Storage Network CI XCI is participating in OSN software working group to facilitate documenting integration collaboration requirements and Use Cases, support the engineering process, and provide other useful XSEDE services Institute for Research on Agreement to use network science methods and administrative data Innovation & Science (IRIS) maintained by the Institute for Research on Innovation & Science to develop preliminary models that examine the scientific effects of XSEDE usage by researchers on more than 30 U.S. university campuses Doane University - Campus Indiana University CRI staff visited Doane University for cluster build and Bridging Site XSEDE software toolkit installation UltraScan Science Gateway - CRI staff worked with UltraScan to create a virtual cluster in Jetstream Virtual Cluster 3-D Quantitative Phenotyping CRI staff worked with A. Murat Maga in order to create a virtual cluster in Gateway - Virtual Cluster Jetstream for the biological sciences XSEDE Web SSO RACD is helping XSEDE federated services providers (XDMoD, CI-Tutor, and Cornell Virtual Workshop) to integrate with the XSEDE Web SSO capability International Collaborations International HPC Summer School Partnership with PRACE (EU), RIKEN AICS (Japan), and Compute Canada (Canada) to familiarize the best students of the respective continents or countries in computational sciences with a strong bond to supercomputing with all major state-of-the-art aspects related to HPC for a broad range of scientific disciplines, catalyze the formation of networks, provide mentoring through faculty members and supercomputing experts from renowned HPC centers, and to facilitate international exchange and open further career options. Open Grid Forum (www.ogf.org) JP Navarro is GLUE working group chair. XSEDE leverages and influences infrastructure information management thru this collaboration. HPC Development and Summer Craig Stewart entered into a formal MOU of Collaboration with TU-Dresden Exchange Program for HPC visiting staff Membership in the RDA Indiana University entered into a formal membership agreement with RDA to organization explore open data standards as part of the international scope of the organization

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International grid computing Indiana University participates as a contributing partner to summer teaching research and education in the African Grid School organization International collaboration of XSEDE entered into an MOU jointly with PRACE and RIKEN committing to regional research infrastructures opening lines of communications and seeking more areas of collaboration. (XSEDE, PRACE, and RIKEN) This occurred in May 2017. XRAS Canada Exploring possible use of XRAS by Compute Canada (still preliminary) Exploring possible collaboration on re-design of Federated Accounting Accounting Service Service WISE (Wise Information Security Support trusted global framework where security experts can share for Collaborating e- information on topics such as risk management, experiences about Infrastructures) certification processes and threat intelligence To establish common policies and guidelines that help establish interoperable, global trust relations between providers of e-Infrastructures and cyber-infrastructures, identity providers, and other qualified relying IGTF/TAGPMA parties. International Identity Federation XCI-302: Participate in REFEDS Assurance Framework Pilot Engagement Group for Infrastructures (AEGIS) XCI-256: Participate in AARC Engagement Group for Infrastructures (AEGIS) Understanding ROI on university-owned advanced cyberinfrastructure ROI on academic advanced systems, including cloud systems with Technische Universitat Darmstadt, cyberinfrastructure systems RTWH Aachen University, & Technishe Universitat Dresden

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14. Service Provider Forum Report Service Providers (SPs) are independently funded projects and/or organizations that provide cyberinfrastructure (CI) services to the science and engineering community. There is a rich diversity of SPs in the US academic community, spanning centers that are funded by NSF to operate large-scale resources for the national research community to universities that provide resources and services to their campus researchers. The Service Provider Forum (SPF) is intended to facilitate this ecosystem of Service Providers, thereby advancing the science and engineering researchers that rely on these cyberinfrastructure services. The SPF has two primary elements of its charter: 1) An open forum for discussion of topics of interest to the SP community 2) A formal communication channel between the SPF members and the XSEDE project The SPF conducts its business primarily through biweekly conference calls on Thursdays at 4PM Eastern Time. Agendas are distributed in advance of the meetings and minutes are maintained on the XSEDE SP wiki (https://confluence.xsede.org/display/XT/XSEDE+Federation). NSF Program Officers are invited and occasionally participate. Many people from the XSEDE program routinely participate in SPF meetings to facilitate direct interaction with the XSEDE program. For example, regular updates are provided by John Towns (XSEDE PI), Victor Hazlewood (XSEDE SP Coordinator), and other XSEDE management and area leads. Additional contributors from XSEDE and other organizations are frequently invited to brief the SPF on XSEDE topics or seek the Forum’s input in the development of program plans and activities. This report is the quarterly summary of the SPF’s activities covering the period of November 1, 2018 – January 31, 2019. SP Forum administrative and membership activities • Biweekly SPF meetings • SP Forum participation at the XSEDE Senior Management Team calls • Ongoing clean-up of membership, getting current contacts for SPs, etc. • Election of new leadership for calendar year 2019: o Chair: Ruth Marinshaw, Stanford University o Vice Chair: Mary Thomas, SDSC/UCSD o L2 Chair: Thomas Doak, NCGAS/Indiana University o L3 Chair: Chet Langin, Southern Illinois University o XAB representatives: David Hancock, Indiana University and Jonathan Anderson, UC Boulder • SPF membership: o The Forum welcomed new L3 members Johns Hopkins University and the University of Delaware. o NCGAS moved from being an L3 member to L2 o Stanford moved from being an L2 to an L3 member o There were also discussions of proposed changes to the process for requesting membership and a redefinition of L2 Service Providers. The full membership of the SP Forum is maintained on the XSEDE website, https://www.xsede.org/ecosystem/service-providers. Technical and programmatic discussions Noteworthy SPF activities from this reporting period include:

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• Regular attendance and updates by John Towns on the XSEDE projects. • Regular attendance and updates by Victor Hazlewood XSEDE Operations topics. • SP Forum participation in the XAB meetings on October 10 and December 12. • Discussions about changing process of requesting membership and redefinition of L2 Service Provider requirements. • Discussion and initial comment gathering around the NSF’s invitation for the SP Forum to provide its input around the XSEDE program, as the NSF considers what the follow-on program might look like. This activity continues into the following quarter • Discussion and review of open NSF cyberinfrastructure-related solicitations • Discussion of the need for a webpage with boilerplate information to support those whose proposal submissions might include provision of resources through XSEDE • Discussions of current status and potential longer-term impact of the government shutdown • Explanation of the purpose and role of the XAB • XSEDE and SP Forum communication and outreach during SC’18 • Participation in XAB quarterly meeting • Participation in XSEDE Senior Management Team meetings • Technical presentations at the SP Forum meetings: o Dan Stanzione: Frontera Leadership Class Computing Facility o Dana Brunson: Update on the Campus Champions

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15. UAC XSEDE User Advisory Committee Report Reporting period: November 1, 2018 through January 31, 2019 The XSEDE User Advisory Committee (UAC) represents the “user's voice” to XSEDE management, presenting recommendations regarding emerging needs and services and act as a sounding board for plans and suggested developments. It meets two or three times a year via conference call. The UAC makes suggestions to improve XSEDE operations and helps identify areas where more support expertise is required. It reviews findings of XSEDE's Performance Evaluation Plan and Self Assessment, and its User Survey. Subsets of the UAC advise XSEDE's Extended Collaborative Support Service (ECSS) management on where to dedicate support effort on community codes. One member of the UAC serves on the User Requirements Evaluation and Prioritization Working Group (UREP). The chair of the UAC (elected by its members) participates in regular XSEDE senior management meetings (currently bi-weekly). There was one regular meeting of the UAC during this reporting period which took place on 1 November 2018. Seven UAC members were present, including Richard Braatz, Tom Cheatham, Diego Donzis, Balint Joo, Mark Miller, Deidre Shoemaker and Chair Emre Brookes. XSEDE staff present included Lizanne DeStefano, Sergiu Sanielevici, John Towns, and Julie Wernert. The first topic was led by John Towns who provided an update on the XSEDE project. He discussed the experience of the project’s first midterm review. Deidre Shoemaker noted that she felt she was unaware of all the services offered by XSEDE and “.. suspect I'm like a lot of other PIs out there that I'm too removed now from the actual day-to-day use of XSEDE, so what I really probably should be doing is getting my team together that sit there every day and make sure they're aware of stuff and talk to them about what works well and what doesn't.” In response, John Towns stated, “It certainly would be extremely useful if that could be passed to us because then we can use that to drive improvements in what we do deliver.” UAC member Diego Donzis noted, “we always are looking for faster machines, bigger machines more powerful and so on” and had concerns about the future availability of resources. John Town agreed with this concern and noted, “the lack of resources is a real problem ... when we go through the quarterly allocations process we typically see anywhere between three and five times the number of available resources in the requests … makes it really hard to make progress with your research team because you simply don't have the resources necessary to get it done.” There was insufficient available time in the meeting to continue this discussion and it was suggested that a UAC meeting dedicated to discussions with John Towns could be useful for the members. The second topic was presented by Lizanne DeStefano and Julie Wernert and focused on results of the 2018 XSEDE User Satisfaction Survey. The results were quite impressive, as one member noted, “the lowest score was 3.7! … I think that's quite amazing …. ” Discussion ensued about viewing the data by allocation size, as Diego Donzis noted: “People that use very large allocations may have different views about many topics than people that use smaller allocations”. Lizanne DeStefano responded that such data was not presented but was in their overall report. After additional discussions on the user survey results and a reiteration by John Towns of his offer to meet with the committee for further discussions, the meeting was closed. In mid-December, feedback was requested from UAC members on the proposed 2019 XSEDE User Satisfaction Survey. Multiple members provided written commentary which was forwarded to Julie Wernert. We subsequently held a brief ad-hoc meeting for members interested in

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discussing this topic on 7 January with members Richard Braatz, Diedre Shoemaker, chair Emre Brookes, and XSEDE staff member Julie Wernert. There were no other actions of the UAC during this reporting period.

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16. XMS Summary Executive Summary Jetstream is now partially reporting their data and a pipeline to this data has been opened to XMS; further work is required to acquire a complete set of metrics and to validate the existing data. The ability of XMS to achieve this is dependent on data feeds from the Jetstream providers, which to date are not complete. We are currently working on collecting energy/power measurements across a wide portion of the CCR HPC environment, including GPU (graphical processing unit), IPMI (intelligent platform management interface), PDU (power distribution unit) per socket and overall PDU. The next step is to transfer this data to the XDMoD database and begin analysis of the data. The storage realm in XDMoD is in beta release, with several HPC centers actively testing it. It is intended to help computer centers track storage in a fully integrated fashion with other XDMoD metrics. XMS has also launched a beta version of Cloud metrics to track cloud usage. It will be tested simultaneously with federated XDMoD using data from the DIBBS Aristotle program. XMS is starting integration of the open source ColdFront allocation management tool with XDMoD. XMS is also starting integration of Ohio Supercomputing Center’s Open OnDemand with XDMoD. XMS FINDINGS: 1. Partial Jetstream reporting has been established. 2. Node based energy/power measurements are being acquired on the CCR cluster nodes and will form the basis for subsequent inclusion of energy metrics in Open XDMoD. 3. Initial releases of Storage and Cloud realms are complete. XMS RECOMMENDATIONS: In order for Jetstream metrics to be fully reported, IU and TACC must provide additional data feeds to XMS. While discussions with both groups are on-going, progress has been slow.

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17. XAB Executive Summaries Executive Summary of XSEDE Advisory Board Meeting, April 17-18, 2018 Meeting Date: April 17-18, 2018 Meeting Place: Aloft Hotel, Rosemont, IL Preface: This was an extended face-to-face meeting. Included was a discussion of recommendations from the January NSF panel review, a discussion of points to consider for the upcoming June NSF panel review, and presentations from each L2 area on their PY8 plan highlights. Agenda, meeting notes, and slides are available for XAB members at https://confluence.xsede.org/display/XT/XAB+2018+April+Face-to-Face+Meeting Summary of meeting comments and XAB suggestions January Panel Review Recommendations: Communicating Successes: The Board discussed ways that XSEDE can better communicate successes. It was noted that communicating scientific results to researchers is different than communicating to the general public and that images are needed. Significant results have come from XSEDE but communicating about this work requires tuning the scientific message so that it is well understood. It was suggested that we start by making simple changes in communicating to a broader audience. One recommendation was to target long-term users and highlight the impact of their research and what came out of their XSEDE allocation. We should think broadly about the success of the entire endeavor and not get trapped into marketing. Share details about the number of researchers whose science has been enabled by XSEDE and try to estimate what that would equate to in dollars, while knowing that is nearly impossible to do. We should also expand our communications about successes beyond science. It was noted that some work is being done on the ROI for XSEDE that could inform this. While ROI and cost avoidance is important to NSF, they fundamentally want to know about science impact. Considering issues beyond lifetime of the current award The Board discussed whether XSEDE has influence over the direction the project might take at the end of the XSEDE 2.0 award. Longitudinal Studies John shared that NSF would like to see more longitudinal assessments, but there is a concern about where the project would shift budget to allow for this. It was noted that NSF is looking for outcomes and specific statements. We could pick a narrow area to analyze and explain the time invested to demonstrate the high cost, but we have to be careful to not make inferences that are not there. The group discussed what we could do with a minimum investment to measure outcomes. It was noted that XSEDE has been instrumental in changing the social dynamics of how the research community interacts together, and that is important to demonstrate. John suggested that we might reach out to those for whom we have written letters of collaboration to ask about outcomes, and we could also ask them to give a presentation at a meeting and share with us how we could improve. Supporting Other Agencies

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The Board discussed that NSF and XSEDE have a cooperative agreement, and, with that, they are serving other agencies and is a joint responsibility. We should show Congress that we can work together. It was suggested that the XAB create a recommendation statement from the XAB to NSF regarding this. It was questioned whether we should add USGS to the list. June Panel Review Points of Interest Benefits other organizations derive from XSEDE and their recognition of same The Board discussed that with XSEDE's existence and social structure it has built, other agencies’ staff, science, technical aspects, etc. are better able to serve their communities. XSEDE has also helped in the educational aspects such as computational science and other topics. Issues beyond lifetime of award The Board discussed that in considering issues beyond the lifetime of the current award, we shouldn’t limit the scope – because it would not be in the best interest of the community. XSEDE has cooperative agreement to advance science in the community, and what we’ve learned has been valuable in defining the future so we should use this leadership role and vision and be reflective on what we’ve learned to help shape the future even if it doesn’t involve XSEDE. It was noted that the project is designed to evolve. Issues to consider beyond scope of the award might include workforce development, security, distributed/virtual organization challenges that will persist beyond the award, etc. Review frequency It was noted that XSEDE devotes a great deal of administrative overhead to reporting to NSF. John questioned if there is something we could provide that would help NSF provide the necessary information to congress. The Board suggested that we should lead this conversation by asking how we can help the NSF promote this program, share ownership, and work together to get a more effective review process. Current value against current investment It was noted that our Program Officer is our advocate, but he is under pressure to justify the large dollar amount, and he also comes from a contract environment as opposed to a cooperative agreement partnership. It was suggested that XSEDE should define our metrics and see if NSF pushes back. While the project has devoted a great deal of work to metrics, NSF wants something “better.” Ultimately, they want outcomes and impact measures. Our report should include a dialogue reflecting on how we use the metrics to define or respond. NSF also wants an analysis of the data, and we should focus on outcomes and stay at a high level. We are better positioned to talk more about this now than we were at the semi-annual review in January. John noted that it would be helpful to have a comment from the XAB supporting this notion. The Board suggested that the report shouldn’t be more than 30 pages, should note major accomplishments, should provide a reflection on what was learned, and should offer major themes going forward. John noted that the IPRs report on data, while the annual report is more of a summary. Transition Planning The Board discussed that in terms of transition planning, XSEDE should step back and think about insights gained by meeting the needs of the community. We should outline what the imperatives are for the community to continue to go forward and high-impact opportunities or investments that have yielded success.

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NSCI and Possible XSEDE Role John has provided information on how XSEDE could support NSCI. The Board suggested that XSEDE make the case that we are well positioned to support any of those, show how XSEDE has helped NSF meet these needs, and demonstrate the value XSEDE is contributing to these national goals. National Strategic Computing Initiative PY8 Plan Highlights The meeting then shifted to each L2 area sharing highlights of their PY8 plans. Slide decks and specific responses to those can be found through the link provided in the preface. High-level comments across all presentations included: • State why you decided to do certain things. • Be clear that a lot of work has been done to broaden SP community. • Try to limit acronyms when speaking, and define them clearly in any written material. • Distill to several key themes. Decide on high-level points you want to make and share core values. • Explain what we’re doing from the vantage point of the user. • The core tenet of XSEDE was leveraging partners and organizations to accomplish the glue that would tie together the science. Presentations should be framed that way and acknowledge others’ engagement. Without those partner connections, XSEDE couldn’t do what it does. Executive Summary of XSEDE Advisory Board Meeting, June 20, 2018 Meeting Date: June 20, 2018 Meeting Place: Teleconference via Skype Preface: The main topic of this call was a debrief following the June NSF Panel Review. Agenda and meeting notes are available for XAB members at https://confluence.xsede.org/display/XT/XAB+2018+June+Call. Summary of meeting comments and XAB suggestions Recap of June NSF Panel Review: John shared that the NSF review went very well despite the fact that review panelists were provided and prepared only a couple days prior to the review and many were new to the program. Highlights from the review included discussion of long-term science impacts such as SCEC, Campus Champions celebrating their 10-year anniversary, and giving back to the community through applications and supporting software infrastructure that benefit the broader community. Each L2 area provided a presentation with highlights from their area. The team received five general questions from panel, which were easy to address, and three additional questions were received the next morning for which the team finalized and submitted responses while the panel began writing their report. John had a final debrief with the remaining panelists. They were complimentary to the project about its vision and maturity. The marketing plan was received well, and they provided suggestions for improved communications. The team is currently awaiting the report from the panel and will share it with the XAB. The Advisory Board members shared thoughts in response to John’s recap. They felt the panel was looking at how XSEDE could tell its story better by sharing what has been learned and how the project continues to grow in response. The fact that the panel didn’t poke holes in the presentation is a sign that XSEDE is doing a good job at something that is brand new and has

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maturity. It was noted that the team did re-think its general formula and provided more context and dug deeper into one example (External Relations), rather than providing a survey of many. This approach worked well. The team noted there was a renewed emphasis on impact—why people care—instead of having the numbers as the main focus. It was suggested that focusing on quantitative data on demographics in the future would be beneficial as the panel wants to learn more about what XSEDE is doing and how it benefits the community. The team noted that all XSEDE is doing to promote diversity within the XSEDE workforce was a theme and that the panel was complimentary about the leadership transition for ECSS and noted that a transition such as this going smoothly in a complicated organization says a lot about the quality of the management and teamwork. John closed by shared that Bob approving spending through PY8, and although he didn't state it, it is likely we will not have a mid-year review as we did last year. Executive Summary of XSEDE Advisory Board Meeting, August 8, 2018 Meeting Date: August 8, 2018 Meeting Place: Teleconference via Skype Preface: The main topic of this call was a discussion about the availability of resources beyond 2020 and how that plays into the post-XSEDE 2.0 draft transition plan. Agenda and meeting notes are available for XAB members at https://confluence.xsede.org/display/XT/XAB+2018+August+Call. Summary of meeting comments and XAB suggestions John gave a debrief on slides that were presented at PEARC18 by Manish Parashar and Sushil Prasad from NSF and noted that there will be a remarkable drop in resources by 2020. While Manish and Sushil did not give a definitive answer, it seems likely that a new Track 2 solicitation will be coming out soon. NSF is in engaging the community and gathering requirements, and a lot of people are providing input. Board members noted that a Track 2 solicitation will need to be out by the end of FY19, as the current Blue Waters system ends before that, so anything other than general discussions XSEDE would be ill-informed at present. It was noted that a general discussion with NSF would be useful and that XSEDE should be providing information to NSF since we control the connection to the community and understand what the community needs to advance science. We should consider how we can communicate the absolute imperatives for hardware and service availability since XSEDE must adapt and transform over time while hardware systems come and go. It was shared that the issue of on-boarding new resources was discussed during the NSF annual review, and there is an expectation that XSEDE should predict and develop expertise and training content for the community despite the fact that we don’t know what is coming. XSEDE is connected to the community of scientists and service providers and could have a conversation with them to determine where their science is going and impose on those ideas the set of services that we’ve learned are essential. We can then share with NSF what services XSEDE will need to have in place when new technologies come along to allow research to continue. The Board discussed that there is only a loose connection between what the NSF is calling “innovative systems” and what the community needs as a resource. It would be helpful if the solicitations were driven by the science rather than the win, which drives us into proposing technologies that may or may not be useful over the long term. It was also noted that scientists don't always know how to use a new system, and XSEDE has provided this service of

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transitioning the science to the new technology. XSEDE has demonstrated leadership for almost a decade and should continue to demonstrate that leadership, taking less defensive position and more of a leadership position. The Board discussed the expectation that XSEDE will deliver a draft transition plan for XSEDE3 and we should use this as an opportunity to lay all of our cards on the table as a set of what is needed to advance science and what serves the community best. It was noted that there is much uncertainty about what such a follow-on solicitation might entail and what budget will be available, but if we provide thoughtful documents to NSF, this will be invaluable when it comes time for them to execute a plan. It was noted that the future of XSEDE is separate from the future of high-end resources deployed at resources providers, but XSEDE can't allocate to machines in 2021 as there will only be 2 at that time. It was recommended that if Kelvin Droegemeier is appointed that XSEDE should send a letter about efficiencies and engagement (not funding). It was noted that training for new resources is difficult if you don't know what you're training for. XSEDE should provide NSF with input on what is going on in community to support the planning process, broadening the scope of focus from systems to science, and be ready to respond. It was suggested that a letter from the XAB to NSF providing input from the community about this problem and some ideas about solutions would valuable. Important information that XSEDE can provide to NSF includes information about GPU usage, those not using parallel processing, how the community is using or not using hardware, disruptive technologies that are coming, and new things on the horizon (e.g. we could tap into data that Dave Hart provided). Executive Summary of XSEDE Advisory Board Meeting, October 10, 2018 Meeting Date: October 10, 2018 Meeting Place: Webinar via Skype Preface: This Webinar was an extended meeting with several presentations from the team. Included was a broad update from John, a deep dive into evaluation (staff and user surveys), and highlights of activity from the Program Office, RAS, ECSS, and Operations. Agenda, meeting notes, and slides are available for XAB members at https://confluence.xsede.org/display/XT/XAB+2018+Oct+Webinar. Summary of meeting comments and XAB suggestions By agenda item: Project Level Update from John: John shared that based on strong reviews and reports provided to date, NSF has forward-funded XSEDE through February 2021 (the project end-date is August 2021). We will continue to require spending authority annually, however. The Program Officer has informed us that we will not be required to have a mid-year review this year and that the use of KPIs has been an effective way to community project status and progress. XSEDE held a strategic planning session on October 2 with a goal of assessing alignment of our stakeholders (stakeholders defined broadly as user community, partners (SPs), staff, funding agencies, etc.) with strategic goals. During this meeting we identified stakeholder priorities, priority areas for the draft transition plan, and priorities beyond XSEDE 2. Stakeholder priorities include access to research computing, infrastructure services supporting the ecosystem, workforce development, Campus Champions and campus engagement, and advanced user support. We also discussed how to

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define the role of XSEDE in addressing gaps and how XSEDE is involved, which include providing leadership, collaborating with the larger community, and thinking in strategic terms. John shared some scenarios for what might happen beyond XSEDE 2.0. Envisioned options include: • Fund single awardee for similar services at ~$75M/5 years (labor cost). If this scenario were to play out XSEDE should determine what the most critical services are that we provide and what could be eliminated • Fund multiple awardees (i.e., RAS might be expanded to provide support for other allocations processes). As resources are retired, this could impact whether there would be anything like XSEDE at all. Our post-XSEDE 2.0 transition plan will point out key things that need to be continued, but there is no certainty about what NSF will do with that information. Decisions from NSF will come in the next 4-6 months, but our transition plan will not get to them until later. Should we deliver earlier? Services XSEDE provides could be leveraged, but the challenge is in how we put together a partnership. Finally, John shared that he has been invited by Manish Parashar to brainstorm with him and others about what should happen next, which is very encouraging. Evaluation Updates: The Evaluation team shared results from both the Staff Climate Survey and the User Survey. Staff climate study: • Trend of increasing scores from 2013 baseline scores • Highest gains in wiki & website, communication tools, decision making • Leadership/management training was a common request • Board members discussed that it is important for XSEDE to share with NSF and the community in general the elements of what helps a virtual organization be successful. Lizanne noted that NSF invited them to speak at the large facilities workshop about this. We need to find ways to incorporate this information into NSF planning processes, particularly when they're thinking about what the next thing looks like. User survey: • Awareness is slightly down from 2017. 25% of users are new to XSEDE every year, so this is common. We always need to work to get the word out about our services. • Awareness highest among center non-research staff, center research staff, high school users, HPC centers, MIS, EPSCOR state institutions. • Satisfaction remains high (4.28 on 5-point scale) and is highest among high school users, government lab, faculty/PI users, minority-serving and teaching focused institutions. • Importance of XSEDE resources to your research remains high. • Main recommendations are that users want more and faster. • Board members discussed that the numbers are extraordinary, especially since XSEDE doesn't have control over the resources themselves. Lizanne noted that a large percentage of people who use XSEDE training materials don't have an allocation, and Service Providers don't have to provide training. The meeting then shifted to the L2 areas providing updates. In the Program Office, External Relations is preparing to deploy the Discover More marketing campaign in an effort to tell the XSEDE story more consistently, show XSEDE as an approachable project no matter who you are, and show the more human and thoughtful elements of XSEDE. The campaign will launch at SC18 with updated booth graphics, in-booth events, and

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coordination with SP booths, and it will be followed by a digital implementation following SC18. Board members discussed the strategic objective that these activities are trying to address. Hannah noted that the ER team tracks leads from the booth, how many of them we have conversations with, and who picks up materials. The External Relations team is also in the process of having a market analysis done by the Illinois Business Council (IBC), a student group at the Univ. of Illinois. IBC will conduct a market analysis for XRAS and SSO Hub services. They will provide a midpoint presentation on October 22, and SMT members will be invited to join that. RAS shared that their team met to look at goals and plans for the next 12 months. Topics included ways to improve administrative aspects of XRAS, revisiting accounting and updating with modern technology, reviewing tasks completed and accomplishments, using XRAS to help allocate aircraft at UCAR/NCAR, and the huge demand vs. available resources and the fact that the success rate is down to about 65%. Dave explained the major reasons people are rejected are due to some sort of technicality (they failed to do something so can easily take them out) and poorly justified requests. If a proposal is rejected the user can reapply in 3 months. A large number of proposals handled by ECSS staff have a higher success rate. Plenary & parallel session level rejections are higher, and it is more likely that a new user will be rejected than a renewal. Dave noted that the number of requests received has jumped considerably from 100+ to 200+ and shared a 2016 Chart of who gets rejected at what percent in various disciplines. He also noted that after rejection 20% never submit again, 20% apply again and get rejected again, and 60% later get a research or startup allocation. Many reviewers look at 10+ requests per quarter (volunteer work). RAS continues to push to rotate out reviewers to bring in new members, which has helped as new reviewers that come in tend to be more willing to consider requests. We do not make a deliberate effort to ensure a particular percentage goes to each field but instead look at the merit of the request regardless of field. There is a perceived competition between biologists & physicists and materials research has increased dramatically. Once the panel makes the recommendation, the reconciliation process factors in the source & amount of funding. There is a formulaic process that brings recommendations in line with available resources. RAS doesn't track gender or other demographics, but the evaluation team looks at gender. Allocations tend to follow funding from agencies. The board noted that it would be good to have this data and be able to report on it. N.B. XCI and CEE did not present updates at this meeting. ECSS ECSS shared that Phil is picking up Ralph's previous duties and Bob is picking up Nancy's previous duties. There have been over 700 cumulative requests since 2010 and service has been very successful with a 4.47 satisfaction rating and 4.03 impact rating on the User Survey. ECSS is doing adaptive reviews which are internal reviews by ECSS staff to reduce the number of proposals that have to be reviewed at quarterly allocations review meetings. PY8 goals include: improve project pipeline, prioritize staff training, science gateway user counting, shared experiences with others providing ECSS-like services, lightweight consulting, and modernization of training collaboration. Approximately 15% of users get this service, and at each XRAC meeting ECSS gets 7-10 project recommendations. Also, a fair number of start-ups get ECSS services. The Board discussed that lighter/medium weight consulting would be helpful and not everything needs a year, though Bob noted that if work is completed early, they wrap it up early. Sergiu noted that NIP is not confined to doing projects with project plans, and they also do as-needed mentoring engagements, especially in data-centric applications. The Board noted that ECSS has

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defined a new mode of knowledge transfer that compliments activities at universities and has done so in a way that is more agile than universities are equipped to do. The team should take credit for this as it speaks to the NSF strategic plan. There are cases where people use XSEDE platforms to gain knowledge that they use on their campus. If they learn something their campus can use, we count that as a success. The Board discussed that HPC, CS, and data science are often intertwined and wondered if XSEDE can communicate better what it meant by these since they are so intertwined. We should look at definitions and make them what we want them to be. John clarified that CS is curiosity driven/publish papers. “Computational science” is an application of that to research problems. HPC can be involved in both. There is merit in us having our own definition of these things. The Board suggested that XSEDE be careful in its use of jargon and noted that the glossary might be expanded to a project glossary that includes jargon used in presentations and reports. We should make sure such jargon is in the glossary to ensure everyone is on the same page with definitions. Operations The Operations team shared their PY8 goals to develop the transition plan for post XSEDE 2.0 and continue excellent help desk ticket resolution. Ops has seen trends in tickets with 4,200 average tickets per quarter (25% are addressed by the Ops center and closed), Single Sign-On Hub as the third most heavily used resource XSEDE provides (logins for ~7,000 pages from about 600-700 people, which means most people open ~10 windows), and Duo usage which was started in Oct. 2016 as optional but then became required. The Board discussed that more explanation about hybrid cloud would be helpful in future presentation materials. They also noted that it would be helpful to show ticket number information compared to number of active users. Finally, they noted that this is a complimentary technical side of the virtual community, and connections should be made there. The Board agreed that all presentations today have great information about XSEDE’s success and suggest that John start with a more far-looking presentation when he goes to NSF. XSEDE has developed an implementation of the vision of the original proposal and found a way to help navigate the challenges of integrating centers in a way that they still have competition but not to a point that it is detrimental to science. John noted that in terms of a transition plan, we plan to talk more about the needs of the community than how to continue XSEDE, as that is the most important thing. The Board noted that there is a fundamental question about services and the need to support operations of those. There are three levels that could be discussed: 1. To what extent an XSEDE follow-on will support that set of resources? 2. What role XSEDE plays to educate the community and onboard them to using HPC? 3. How to we best use the various other things XSEDE does to benefit the community that doesn’t just involve using systems? The XAB reminded us to look at the context of XSEDE partners and consider if the balance of investment between XSEDE & Service Providers is the right one. There is a fair amount of investment into XSEDE that directly benefits SPs. The SP operational budgets are lean so they lean on XSEDE. John noted that we have captured much of that in Craig Stewart's ROI work as it looks at what the investment would have to be locally for each SP without XSEDE and notes that you would spend a lot more than what is being spent today. The message was: we need to continue to make the point that XSEDE allows the NSF investment to multiply.

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Executive Summary of XSEDE Advisory Board Meeting, December 12, 2018 Meeting Date: December 12, 2018 Meeting Place: Teleconference via Skype Preface: We welcomed four new members during this meeting, and John provided a summary of his visit at NSF in November. Agenda and meeting notes are available for XAB members at https://confluence.xsede.org/display/XT/XAB+2018+Dec+Call. Summary of meeting comments and XAB suggestions The meeting opened with welcoming the four new XAB members: Lisa Arafune, Ken Bloom, Rudi Eigenmann, and Ani Ural. Staff will plan an orientation call for new members any anyone else interested in January. Staff also shared the schedule for the 2019 XAB meetings which have all been sent out as Outlook calendar invitations. Update from John on his visit to NSF: John was invited by NSF’s OAC Director, Manish Parashar, to discuss what a follow-on solicitation post XSEDE 2.0 might entail. The last time John had been invited was 2011, and this is a positive step and recognition of his leadership as well as the success of XSEDE. His presentation to NSF included: • Questions including: Is there an NSF vision for what is desired? Is this a step in a long-term exit strategy for NSF? What are the budgetary constraints? What strategic impacts are most important to NSF? They had thought about some of these things but didn't have many answers. • John then offered various suggestions: o Go Big (expanded scope working to reduce duplication across the foundation) o Go Really Big (single MREFC-level award; treat CI as a facility) o Go Big and Wide (multiple awards that address various service areas) o Go Home (fund ramp-down with Service Providers collectively managing the ecosystem. This could encourage the creation of a commercial spin-off.) o Status Quo (fund a similar project at a similar budget) o Keep the trains running (fund something like XSEDE at a significantly reduced budget) John noted that whatever follows XSEDE 2 needs to satisfy the needs of the community and avoid disruption. His personal interests are in building infrastructure in support of research. Board members discussed that ideally they should avoid a model that requires renewing every couple of months because there is no time left to do any work. They also discussed that Open Science Grid had similar experience with coming to the end of their funding and had to figure out a new model with smaller grants to different domain areas being used to fund a joint project. It was noted that a conversation with Frank Würthwein would be valuable, and John has already talked with him. John anticipates being invited back, so members should share any alternatives or suggestions with him. John spoke with program officers from elsewhere at NSF in the afternoon and provided information about XSEDE and why they should be interested. He presented a pie chart that shows to whom we deliver allocated resources by their funding agency, and noted that XSEDE is mandated to support researchers funded by any federal agency. NSF funded PIs received about 41% of delivered resources, while DOE received 14%, NIH received 14%, DOD received 5%, NASA received 4%, DOC received 3%, which shows XSEDE is supporting a broad range of researchers. The distribution of consumption by NSF-supported research projects includes: math & physical sciences 48%/$12.8M/3000 node cluster; bio 24%;

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eng 15%; geo 8%. NSF projects consumed ~7.9B core hours. XSEDE Staff is working to deliver to each directorate a report for last fiscal year with a complete list of funded PIs with allocations, dollar value of awards, dollar values of cycles consumed, and resource dedicated to them. This will allow them to connect funded PIs to services provided by XSEDE. While this data is all available to the public, it is hard to extract from XDMoD, so this will package the data in a way that is easier for directorates to view. It is unclear how they might use the information, but we would still want to enlighten them as to what is actually being done by their funded PIs and engage in a discussion with OAC to protect what they are already getting from XSEDE. We are trying to prompt conversations that will help directorates and help define a better program going forward. Board members noted that we should try to provide guidance to NSF as NSF is community driven, and we are part of the community. John noted that NSF feels they have addressed the need for gathering input via community workshops to help inform the future direction with RFI workshops, etc. We have worked hard to align our activities with the NSF strategic plan that covers 2018-2022, but until last year there were just two other documents that guided us which have expired. It is unclear if there are going to be follow-on documents that help inform community. The Board suggested that XSEDE keep communications open with the Department of Energy Advisory Board.

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