XSEDE: The Extreme Science and Engineering Discovery Environment
Interim Project Report 4: Report Year 2, Reporting Period 2 August 1, 2017 – October 31, 2017
RY2 IPR 4 Page i
XSEDE Senior Management Team (SMT) John Towns (NCSA) PI and Project Director Kelly Gaither (TACC) Co-PI and Campus Engagement and Enrichment Director Nancy Wilkins-Diehr Co-PI and Extended Collaborative Support Service Co-Director (SDSC) Sergiu Sanielevici (PSC) Extended Collaborative Support Service Interim 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 Karla Gendler (TACC) Senior Project Manager Emre Brooks (UT-San User Advisory Committee Chair (voting member of the SMT) Antonio) Dan Stanzione (TACC) XD Service Providers Forum Chair (voting member of the SMT)
RY2 IPR 4 Page ii
Table of Contents XSEDE: The Extreme Science and Engineering Discovery Environment ...... i Interim Project Report 4: ...... i Report Year 2, Reporting Period 2 ...... i XSEDE Senior Management Team (SMT) ...... ii List of Tables...... vi Reading this Report ...... viii 1. Executive Summary ...... 1 1.1. Strategic Goals ...... 1 1.2. Summary & Project Highlights ...... 4 2. Science and Engineering Highlights ...... 5 2.1. Computer Simulations Provide Eclipse Preview...... 5 2.2. Cosmos Code Helps Probe Space Oddities ...... 6 2.3. With Help from XSEDE, ArcticDEM Completes Presidential Order...... 7 2.4. Extracting Meaningful Data from Decomposing Bodies ...... 8 2.5. Shared High Value Research Resource: The CamCAN Human Lifespan Neuroimaging Dataset ...... 9 2.6. Tools of the 21st Century: HPC, Analytical Ultracentrifugation, and a New Detector ...... 10 2.7. XSEDE Resources Help Confirm LIGO Discovery ...... 11 3. Discussion of Strategic Goals and Key Performance Indicators ...... 13 3.1. Deepen and Extend Use ...... 13 3.1.1. Deepening Use to Existing Communities ...... 13 3.1.2. Extending Use to New Communities ...... 14 3.1.3. Prepare the Current and Next Generation ...... 14 3.1.4. Raising Awareness ...... 15 3.2. Advance the Ecosystem ...... 16 3.2.1. Create an Open and Evolving e-Infrastructure ...... 16 3.2.2. Enhance the Array of Technical Expertise and Support Services ...... 17 3.3. Sustain the Ecosystem...... 17 3.3.1. Provide Reliable, Efficient, and Secure Infrastructure ...... 18 3.3.2. Provide Excellent User Support ...... 18 3.3.3. Effective and Productive Virtual Organization...... 19 3.3.4. Innovative Virtual Organization ...... 19 4. Community Engagement & Enrichment (WBS 2.1) ...... 21 4.1. CEE Director’s Office (WBS 2.1.1) ...... 24
RY2 IPR 4 Page iii
4.2. Workforce Development (WBS 2.1.2) ...... 24 4.3. User Engagement (WBS 2.1.3) ...... 27 4.4. Broadening Participation (WBS 2.1.4) ...... 28 4.5. User Interfaces & Online Information (WBS 2.1.5) ...... 29 4.6. Campus Engagement (WBS 2.1.6) ...... 30 5. Extended Collaborative Support Service (WBS 2.2) ...... 32 5.1. ECSS Director’s Office (WBS 2.2.1) ...... 35 5.2. Extended Support for Research Teams (WBS 2.2.2) ...... 36 5.3. Novel & Innovative Projects (WBS 2.2.3) ...... 37 5.4. Extended Support for Community Codes (WBS 2.2.4) ...... 38 5.5. Extended Support for Science Gateways (WBS 2.2.5) ...... 39 5.6. Extended Support for Education, Outreach, & Training (WBS 2.2.6) ...... 40 6. XSEDE Cyberinfrastructure Integration (WBS 2.3) ...... 42 6.1. XCI Director’s Office (WBS 2.3.1) ...... 43 6.2. Requirements Analysis & Capability Delivery (WBS 2.3.2) ...... 44 6.3. XSEDE Cyberinfrastructure Resource Integration (WBS 2.3.3) ...... 45 7. XSEDE Operations (WBS 2.4) ...... 47 7.1. Operations Director’s Office (WBS 2.4.1) ...... 48 7.2. Cybersecurity (WBS 2.4.2) ...... 48 7.3. Data Transfer Services (WBS 2.4.3) ...... 49 7.4. XSEDE Operations Center (WBS 2.4.4) ...... 49 7.5. System Operations Support (WBS 2.4.5) ...... 50 8. Resource Allocation Service (WBS 2.5) ...... 51 8.1. RAS Director’s Office (WBS 2.5.1) ...... 52 8.2. XSEDE Allocations Process & Policies (WBS 2.5.2) ...... 52 8.3. Allocations, Accounting, & Account Management CI (WBS 2.5.3) ...... 53 9. Program Office (WBS 2.6) ...... 55 9.1. Project Office (WBS 2.6.1) ...... 57 9.2. External Relations (WBS 2.6.2) ...... 57 9.3. Project Management, Reporting, & Risk Management (WBS 2.6.3) ...... 58 9.4. Business Operations (WBS 2.6.4) ...... 59 9.5. Strategy, Planning, Policy, Evaluation & Organizational Improvement (WBS 2.6.5)...... 60 10. Financial Information...... 62 11. Project Improvement Fund ...... 66 12. Appendices ...... 67
RY2 IPR 4 Page iv
12.1. Glossary and List of Acronyms ...... 67 12.2. Metrics ...... 70 12.2.1. SP Resource and Service Usage Metrics ...... 70 12.2.2. Other Metrics...... 81 12.3. Scientific Impact Metrics (SIM) and Publications Listing ...... 92 12.3.1. Summary Impact Metrics ...... 92 12.3.2. Historical Trend ...... 92 12.3.3. Publications Listing ...... 93 13. Collaborations ...... 145 14. Service Provider Forum Report ...... 148 15. UAC ...... 149 16. XMS Summary ...... 151 16.1. Executive Summary ...... 151 16.2. XMS Findings ...... 151 16.3. XMS Recommendations ...... 151
RY2 IPR 4 Page v
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 to (existing communities)...... 13 Table 3-2: KPIs for the sub-goal of extend use (new communities)...... 14 Table 3-3: KPIs for the sub-goal of preparing the current and next generation...... 15 Table 3-4: KPIs for the sub-goal of raise awareness of the value of advanced digital research services...... 15 Table 3-5: KPIs for the sub-goal of create an open and evolving e-infrastructure...... 16 Table 3-6: KPIs for the sub-goal of enhance the array of technical expertise and support services...... 17 Table 3-7: KPIs for the sub-goal of provide reliable, efficient, and secure infrastructure...... 18 Table 3-8: KPIs for the sub-goal of provide excellent user support...... 18 Table 3-9: KPIs for the sub-goal of operate an effective and productive virtual organization...... 19 Table 3-10: KPIs for the sub-goal of operate an innovative organization...... 20 Table 4-1: Area Metrics for Community Engagement & Enrichment...... 21 Table 4-2: Area Metrics for Workforce Development...... 25 Table 4-3: Area Metrics for User Engagement...... 27 Table 4-4: Area Metrics for Broadening Participation...... 28 Table 4-5: Area Metrics for User Interfaces & Online Information...... 29 Table 4-6: Area Metrics for Campus Engagement...... 31 Table 5-1: Area Metrics for Extended Collaborative Support Service...... 32 Table 5-2: Area Metrics for Extended Support for Research Teams...... 36 Table 5-3: Area Metrics for Novel & Innovative Projects...... 37 Table 5-4: Area Metrics for Extended Support for Community Codes...... 38 Table 5-5: Area Metrics for Extended Support for Science Gateways...... 40 Table 5-6: Area Metrics for Extended Support for Education, Outreach, & Training...... 41 Table 6-1: Area Metrics for XSEDE Cyberinfrastructure Integration (XCI)...... 43 Table 6-2: Area Metrics for Requirements Analysis & Capability Delivery...... 44 Table 6-3: Area Metrics for XSEDE Cyberinfrastructure Resource Integration...... 45 Table 7-1: Area Metrics for Operations...... 47 Table 7-2: Area Metrics for Cybersecurity...... 48 Table 7-3: Area Metrics for Data Transfer Services...... 49 Table 7-4: Area Metrics for XSEDE Operations Center...... 49 Table 7-5: Area Metrics for System Operations Support...... 50 Table 8-1: Area Metrics for Resource Allocation Service...... 51 Table 8-2: Area Metrics for XSEDE Allocations Process & Policies...... 52
RY2 IPR 4 Page vi
Table 8-3: Area Metrics for Allocations, Accounting, & Account Management CI...... 53 Table 9-1: Area Metrics for Program Office...... 55 Table 9-2: Area Metrics for External Relations...... 57 Table 9-3: Area Metrics for Project Management, Reporting, & Risk Management...... 58 Table 9-4: Area Metrics for Business Operations3...... 59 Table 9-5: Area Metrics Strategy, Planning, Policy, Evaluation & Organizational Improvement. . 60 Table 10-1: Project Level Financial Summary...... 62 Table 10-2: Partner Institution Level Financial Summary...... 63 Table 12-1: Quarterly activity summary...... 71 Table 12-2: End of quarter XSEDE open user accounts by type, excluding XSEDE staff...... 73 Table 12-3: Active institutions in selected categories. Institutions may be in more than one category...... 74 Table 12-4: Project summary metrics...... 75 Table 12-5: Project activity by allocation board type...... 75 Table 12-6: Resource activity, by type of resource, excluding staff projects. Note: A user will be counted for each type of resource used...... 77 Table 12-7: Globus Online activity to and from XSEDE endpoints, excluding GO XSEDE speed page user...... 79
RY2 IPR 4 Page vii
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. 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 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 the 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. Metrics associated with Work Breakdown Structure Level 2 and Level 3 areas are designated as “Area Metrics.” These 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 by each of these areas.
The Executive Summary (§1) is intended to effectively and concisely communicate the status of Deleted: Executive Summary the project toward delivery of the mission and realization of the vision by reaching three Formatted: Underline, Underline color: Accent 1, Font strategic goals. Stoplight indicators (§1.1) are used to visually provide a quick understanding of color: Accent 1 our assessment of overall project progress with respect to the strategic goals in light of our KPIs.
The Science and Engineering Highlights (§2) section provides a small selection of a continuing Deleted: Science and Engineering Highlights series of scientific and engineering research and education successes XSEDE has enabled. These Formatted: Underline, Underline color: Accent 1, Font successes are an ongoing testament to the importance of our services to the research color: Accent 1 community.
The Discussion of Strategic Goals and Key Performance Indicators (§3) section provides the next Formatted: Underline, Underline color: Accent 1, Font level of detail in understanding project progress. It decomposes the strategic goals into sub- color: Accent 1 goals and discusses progress toward each of the sub-goals using KPIs that, where possible, Deleted: Discussion of Strategic Goals and Key represent measures of impact to the communities XSEDE supports. As noted in the report, in Performance Indicators some cases metrics for impact, outcome, or both are not currently being collected and in these cases, the best available metric is used as the KPI. 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 additional Area Metrics 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. 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.
RY2 IPR 4 Page viii
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.
RY2 IPR 4 Page ix
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, Investing in Science, Engineering, and Education for the Nation's Future: NSF Strategic Plan for 2014 - 20181, 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. 1.1. 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/about/performance/strategic_plan.jsp 2 http://www.nsf.gov/cise/aci/cif21/CIF21Vision2012current.pdf 3 http://www.nsf.gov/publications/pub_summ.jsp?ods_key=nsf12051
RY2 IPR 4 Page 1 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 assure 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.
Table 1-1 below shows the project’s progress toward the three strategic goals and associated Formatted: Underline, Underline color: Accent 1, Font sub-goals. Status icons are used in the table as follows: color: Accent 1 A green status is defined as a strategic goal for which at least 90% of the targets Deleted: Table 1-1 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.
RY2 IPR 4 Page 2
Table 1-1: Summary of key performance indicators (KPIs) for XSEDE.
Strategic Goals Sub-goals KPIs
Deepen and Extend Use (§3.1)
● Average ECSS impact rating Deepen use (existing ● Average satisfaction with ECSS support communities) (§3.1.1) ● Number of completed ECSS projects
● Number of new users from underrepresented communities and non- Extend use (new traditional disciplines of XSEDE resources and services communities) (§3.1.2) ● Number of sustained users from underrepresented communities and non-traditional disciplines of XSEDE resources and services
● Number of attendees in synchronous and asynchronous training Prepare the current and ● Average impact assessment of training for attendees registered next generation (§3.1.3) through the XSEDE User Portal
● Number of pageviews to the XSEDE website Raise awareness of the ● Number of pageviews to the XSEDE User Portal value of advanced digital ● Number of social media impressions services (§3.1.4) ● Number of media hits
Advance the Ecosystem (§3.2)
Create an open and ● Number of new capabilities made available for production evolving e-infrastructure deployment (§3.2.1) ● Average satisfaction rating of XCI services
Enhance the array of ● Average rating of staff regarding how well-prepared they feel to technical expertise and perform their jobs support services (§3.2.2)
Sustain the Ecosystem (§3.3)
Provide reliable, ● Average composite availability of core services efficient, and secure ● Hours of downtime with direct user impacts from an XSEDE security infrastructure (§3.3.1) incident
● Mean time to ticket resolution Provide excellent user ● Average user satisfaction rating for allocations and other support support (§3.3.2) services
Operate an effective and productive virtual ● Percentage of recommendations addressed by relevant project areas organization (§3.3.3)
● Number of key improvements addressed from systematic evaluation Operate an innovative ● Number of key improvements addressed from external sources virtual organization ● Ratio of proactive to reactive improvements (§3.3.4) ● Number of staff publications
RY2 IPR 4 Page 3 1.2. 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 areas. Specific examples from this reporting period covering solar science, cosmology, polar science, historical document analysis, neuroimaging, virology, and gravitational waves are summarized in §2 of this report; notable partnerships with research teams and the ECSS team are called out in §5. A continually- updated collection of these successes is documented on XSEDE’s website (see: https://www.xsede.org/science-successes). In support of the broad range of impressive and high-impact research projects, demand for and usage of XSEDE resources remains very high with over 1,000 requests processed during this reporting period, while maintaining a high level of user satisfaction. The use of XSEDE-allocated resources by student researchers continues to grow, in part, due to the XSEDE EMPOWER program; eleven students were engaged through the EMPOWER program this reporting period. Supporting current researchers utilizing XSEDE resources and attracting new researchers are our highest priorities. Fast and easy access to information is important to potential and existing consumers of our resources and services. During this reporting period, the newly redesigned XSEDE website was released in response to feedback from the community. The new web presence effectively markets to external audiences and provides intuitive access to information. The Campus Champions program has been a major success and continues to expand in scope and size. Progress continues toward their sustainability goal and increasing their positive impact to the user community. This includes receiving membership requests from industry and international higher education institutions. The newly elected leaders are actively discussing options to expand membership in these areas. Behind the scenes, work continues to improve the efficiency and effectiveness of internal processes that have a direct positive impact on the researcher experience. A new development in the XRAS administration interface is expected to result in improving the processing of allocation requests. Another important example is a new central logging service that has been implemented, enabling improved security audits and data analytics with real-time support. This capability provides better information to the team resulting in better, more timely decisions and enhancing our security posture. XSEDE staff have also been very busy improving the overall security of the XSEDE network. Many cross-area security improvements were implemented, including a new user management process to ensure unused user accounts, as identified by PIs, are removed. Significant improvements in hardware, software, and administrative ownership were made to the Kerberos service. In addition, XSEDE users and staff now have access to a new online Information Security Training module, and a new document titled, “User authentication service for XSEDE science gateways” has been delivered containing information on how XSEDE’s public authentication service can be leveraged by science gateways. The methods described in that document were reviewed at a tutorial during the Gateways 2017 conference. Additional details about all highlights can be found in §4 through §9 of this report.
RY2 IPR 4 Page 4 2. Science and Engineering Highlights Provided in this section is a select set of science and engineering highlights from the community of researchers with whom we collaborate. These are reported from the most recent reporting period. A complete collection of highlights can be found at: https://www.xsede.org/science- successes. 2.1. Computer Simulations Provide Eclipse Preview On August 21, 2017, a total eclipse of the Sun was visible across the U.S. The eclipse traced out a 70-mile-wide band across 14 states. Beyond their rarity, solar eclipses help astronomers better understand the Sun—its structure, inner workings, and the space weather it generates. They also provide an opportunity for researchers who study solar science to forecast in advance how the Sun will look during the eclipse—proving their predictive chops, so to speak. A team from Predictive Science Inc. (PSI), based in San Diego, is one such research group. Beginning on July 28, 2017, with support from NASA, the Air Force Office of Scientific Research, and the National Science Foundation, they began a large-scale simulation of the Sun's surface in preparation for a prediction of what the solar corona—the aura of plasma that surrounds the sun and extends millions of kilometers into space—will look like during the eclipse. Using massive supercomputers, including time on Stampede2 at the Texas Advanced Computing Center (TACC) and Comet at the San Diego Supercomputer Center (SDSC) provided via XSEDE, and NASA's Pleiades, the researchers completed a series of highly-detailed solar simulations timed to the moment of the eclipse. "Advanced computational resources are crucial to developing detailed physical models of the solar corona and solar wind," says Jon Linker, President and Senior Research Scientist at PSI. "The growth in the power of these resources in recent years has fueled an increase in not only the resolution of these models, but the sophistication of the way the models treat the underlying physical processes as well." “Based on a very preliminary comparison, it looks like the model did very well in capturing features of the large-scale corona,” Linker said. With its increased complexity, the model demonstrates that even the Sun’s fine magnetic structures are intimately related to the vast structure of the corona. The simulations are among the largest the research group has performed, using 65 million grid points to provide greater accuracy and realism. Once completed, the researchers converted their computer simulations into scientific visualizations that approximated what the human eye might see during the solar Figure 1: These images show two versions of the eclipse (see Figure 1). predicted brightness of polarized white light in the Formatted: Font: 11 pt, Underline, Font color: Custom "A solar eclipse allows us to see levels of the corona. The left image shows an image processed to Color(RGB(46,118,161)) simulate what would be seen when using a "Newkirk" Deleted: solar corona not possible even with the most radially graded filter (a method that captures the Figure 1 powerful telescopes and spacecraft," says Niall clearest pictures of the solar corona). The image on the Gaffney, a former Hubble scientist and director right represents the polarized brightness on a log scale, of Data Intensive Computing at the Texas sharpened using an "Unsharp Mask" filter. The images Advanced Computing Center. "It also gives are aligned to replicate the view of an observer on Earth with a camera pointed toward the Earth's North high performance computing researchers who Pole. Courtesy of Predictive Science Inc.
RY2 IPR 4 Page 5 model high energy plasmas the unique ability to test our understanding of magnetohydrodynamics at a scale and environment not possible anywhere else." 2.2. Cosmos Code Helps Probe Space Oddities A computer code named Cosmos now fuels supercomputer simulations of black hole jets and is starting to reveal the mysteries of black holes and other space oddities. "Cosmos, the root of the name, came from the fact that the code was originally designed to do cosmology. It's morphed into doing a broad range of astrophysics," explained Chris Fragile, the Principal Investigator of Cosmos and professor in the Physics and Astronomy Department of the College of Charleston. Some of the physics packages of Cosmos include chemistry, nuclear burning, Newtonian gravity, relativistic gravity, and even radiation and radiative cooling. The current iteration of the code is CosmosDG, which utilizes discontinuous Gelarkin methods. "You take the physical domain that you want to simulate," explained Fragile, "and you break it up into a bunch of little, tiny computational cells, or zones. You're basically solving the equations of fluid dynamics in each of those zones." CosmosDG has allowed a much higher order of accuracy than ever before, according to results published in the Astrophysical Journal, August 2017. "We were able to demonstrate that we achieved many orders of magnitude more accurate solutions in that same number of computational zones," stated Fragile. "So, particularly in scenarios where you need very accurate solutions, CosmosDG may be a way to get that with less computational expense than we would have had to use with previous methods." The Cosmos code has helped Fragile study accretion, the fall of molecular gases and space debris into a black hole, that powers its jets. Black holes spin, and so do the surrounding disk of gases and debris that falls in. However, they spin on different axes of rotation. "We were the first people to study cases where the axis of rotation of the disk is not aligned with the axis of rotation of the black hole," Fragile said. Fragile's simulations showed the black hole wobbles, a movement called precession, from the torque of the spinning accretion disk. Fragile and colleagues also use the Cosmos code to study other space oddities such as tidal disruption events and Minkowski's Object, where Cosmos simulations support observations that a black hole jet collides with a molecular cloud to trigger star formation (see Figure Formatted: Font: Not Bold, Underline, Underline color: 2). Accent 1, Font color: Accent 1 Since 2008, TACC has provided Deleted: Figure 2 computational resources for the development of the Cosmos code—about 6.5 million supercomputer core hours on the Ranger system and 3.6 million core hours on the Stampede system. XSEDE awarded Fragile's group with the allocations. "I can't praise enough how meaningful the XSEDE resources are," Fragile said. "The science that I do wouldn't be possible without resources like that." Fragile has recently enlisted the help of Figure 2: General Relativistic Radiation Magnetohydrodynamic simulation of accretion of gas into XSEDE ECSS staff to optimize the a 6.6 solar mass black hole. CosmosDG code for Stampede2, a
RY2 IPR 4 Page 6 supercomputer capable of 18 petaflops, the flagship of TACC, and currently the most powerful system in the XSEDE portfolio. 2.3. With Help from XSEDE, ArcticDEM Completes Presidential Order Video: https://youtu.be/xpJKFrFraQE. The final installment of Arctic maps, released in September by the ArcticDEM project, was made possible by NCSA at Illinois as well as the University of Minnesota, Ohio State University, and Cornell University. But without the help of XSEDE's Project Director John Towns, the entire project may have been executed differently. The ArcticDEM project is part of a White House Arctic initiative to inform better decision- making in the Arctic. Headed up by Paul Morin, director of the University of Minnesota's Polar Geospatial Center (PGC), the ArcticDEM project is using high-performance computing resources to create digital elevation models (DEMS) that are swiftly changing what we know about the Arctic. A DEM is a computer-generated 3D topographic map that shows the height of everything on the earth's surface in the map area. Current elevation models for the Arctic have a resolution of one kilometer—Morin's models offer a resolution of five meters or less over an area of 20 million square kilometers, and are much more accurate at gauging height. The jump in detail will help scientists better track ice loss, and enable a host of other research (see Figure 3). Formatted: Font: Not Bold, Underline, Underline color: Morin met Towns in December 2014 at a “High-Performance & Distributed Computing for Polar Accent 1, Font color: Accent 1 Sciences” workshop and Towns immediately introduced the XSEDE project as a potential Deleted: Figure 3 problem-solver for the massive scale of data that would come from an array of satellites collecting topographic images. "We did a proof-of-concept pilot that rapidly turned into actual production effort," said Towns of XSEDE's original involvement in ArcticDEM. "It became clear that the resources necessary to go for the ambitious goals that Paul had— and still has—were simply beyond the capacity and capability to support with XSEDE compute resources." XSEDE stayed involved with ArcticDEM, supporting the project through two allocations. Morin used XSEDE resources, most notably Gordon at San Diego Supercomputer Center and ECSS staff support. "As our project was getting off the ground, we realized that our code was probably far enough along to start putting on XSEDE resources. We got a start-up allocation and got consulting help to allow us to work on that and ran things in relatively small areas of space—like the North Slope of Alaska, those kinds of things," said Morin. "After a while, we knew we needed a bigger Figure 3: Mapping of the Arctic in strip coverage. Orange resource to do compute, and Towns shows the mapping completed over the last release cycle, an introduced us to Bill Kramer (PI of Blue additional 32% from past releases. In total, high resolution Waters).” terrain data has now been mapped for 97.4% of the Arctic.
RY2 IPR 4 Page 7 "With Towns, he's the ultimate connector. He was able to point us at the right places. When something needed to be goosed, he goosed it. The XSEDE support folks (ECSS) helped do a lot of the initial optimization and grunt work and profiling.” Morin concluded: "The way that people should start doing research on HPC now is either through their own campus resources or through XSEDE." The production of these Arctic DEMs is transforming the Arctic research community as they are provided time-stamped observations of ice extent and ice surface height which can be examined within the context of changing environmental factors. This enables researchers to study the evolution of surface water flows on glaciers which can be examined down to the level of individual lakes and streams. The resulting research is impacting not only scientists’ ability to track ice loss but also ecological conditions of arctic ecosystems, including wildlife management, and sustainability. 2.4. Extracting Meaningful Data from Decomposing Bodies While machine recognition of handwritten and cursive records has made strides in recent years, the technology faces challenges stemming from the inconsistency between and among humans’ handwriting. Alison Langmead of the University of Pittsburgh has explored a unique collection of hand-written prison records stored by the state of Ohio’s penal system from the 19th through early 20th centuries with the intent of opening up a trove of criminological, social, and historical information that should be useful to investigators in many fields. Experts from XSEDE’s ECSS and the XSEDE-allocated Bridges system at PSC made this undertaking possible. Langmead, her collaborator Josh Ellenbogen, and a team of graduate and undergraduate students have previously studied the late-19th-century system of anthropometrical measurement introduced in France by Alphonse Bertillon. “Bertillonnage,” as this system is commonly known, was the first measurement-based, state-controlled system used for criminal identification. Adopted by the Ohio State Reformatory and Ohio Penitentiary for identifying prisoners and storing miscellaneous information about them, there are an estimated 40,000 physical cards remaining from its implementation. However, in order to turn these raw historical artifacts into a usable dataset, Langmead’s project, Decomposing Bodies, needs to extract information from these cards. Using human readers is a painstaking and time- consuming process. With the encouragement of PSC Acting Director Nick Nystrom and ECSS expert Rick Costa at PSC, Langmead engaged XSEDE experts with the goal of creating an end-to-end, semi-automated system for Figure 4: The result of various steps in the segmentation extracting handwritten text and numbers process of decimal number recognition in the “Decomposing Bodies” project. a) Cropped and rotation from the scanned Bertillon, thus giving corrected top region of a scanned card containing researchers the ability to browse through anthropometrical measurements. b) Column images the original data and generate metadata generated after the column image segmentation step. c) using a web interface. ECSS expert Alan Cell images extracted from column images that need Craig, of the Shodor Foundation, helped further processing. d) Digit images isolated from cell images. shepherd the project in the XSEDE
RY2 IPR 4 Page 8 environment, recommending Sandeep Puthanveetil Satheesan of NCSA and Paul Rodriguez of SDSC to work on constructing the artificial intelligence. Using Bridges to analyze a set of 3,600 of two types of the cards, the collaborators carried out a preliminary study of number recognition (one side of the cards containing mostly Bertillon measurements) and of word recognition (the other side containing mostly demographic information) (see Figure 4). In addition to variations in handwriting, information that crossed Formatted: Font: Not Bold, Underline, Font color: lines out of their intended boxes, ink smudges. and recognizing decimal points as intentional Custom Color(RGB(46,118,161)) marks all posed a challenge. Preliminary digit-reading results from the first 300 cards analyzed Deleted: Figure 4 had an average error rate of 14% in 11 body measurements on one card type, 20% in the other. For word recognition, in a set of 6,316 cards analyzed, the program had an error rate of 44%. Interestingly, though, the error rate varied, between 30% for cleanly written entries and 60% for cards with more ink stains. The team has now started to use Bridges to analyze a wider selection of cards. While Langmead concluded that computer vision is not yet mature enough to forward the project, she believes that the working relationship she established between computational scientists and humanities scholars will be a model for establishing such collaborations. The preliminary work, presented at PEARC17 in July, won the award for best paper in the “Accelerating Discovery in Scholarly Research” track at the conference. 2.5. Shared High Value Research Resource: The CamCAN Human Lifespan Neuroimaging Dataset One of the enduring challenges in emergency medicine and neurological surgery is the early identification and treatment of traumatic brain injury (TBI). The condition, while serious and often progressive, can present with minimal symptoms that appear to resolve even though the patient is still at increased risk of lasting neurological damage. One major problem has been the lack of an imaging technology that can identify the damage caused by early TBI. Stereo electroencephalography can measure brain activity directly but can only show activity on the surface of the brain because intervening tissues block the electrical field. A new method of magnetic resonance imaging appears to image loss of nerve fibers in the white matter following a TBI, but the causality chain is not clear as it Figure 5: Regional patterns of brain activity demonstrate measures blood flow rather than nerve cell high classification accuracy between cohorts. function. Stereo magnetoencephalography Neuroelectric activity density was measured from resting MEG recordings in each of 164 brain regions in 3 (MEG) provides a non-invasive experimental cohorts: controls (n=414), TBI (n=64) and measurement, detecting the magnetic HIV at-risk (n=55). The middle and lower panels show the activity associated with nerve-cell brain regions that contribute their fair share or more to electrical activity (see Figure 5). It holds the classifier; Red/blue indicates a positive/negative Deleted: Figure 5 the promise of identifying the earliest weight; the more saturated (deeper) the color, the greater the weight. The cyan landmarks are the boundaries Formatted: Font: Not Bold, Underline, Font color: neurological dysfunction caused by a TBI, between gray and white matter in the pre-central, Custom Color(RGB(46,118,161)) offering quicker, more accurate cingulate, insula and fusiform regions.
RY2 IPR 4 Page 9 identification of patients at risk and as a tool for assessing early interventions since it is functional and the signal is not affected by intervening tissues. Don Krieger at the University of Pittsburgh worked with colleagues to analyze more than 1.7 TB of functional MEG data from three groups of patients. The first study group consisted of 64 patients with TBI. One control group consisted of a large neuroimaging dataset of 414 matched, healthy patients from The Cambridge Centre for Ageing and Neuroscience. Finally, the scientists analyzed a further control group of 55 patients at risk of HIV infection. In each of these groups, the investigators imaged brain activity in the subjects in three mental states: carrying out a simple task, resting with eyes closed, and passively listening/viewing. Comparing those three mental states allowed them to separate brain activity associated with performing the task from background brain activity (resting) and brain activity stemming from thinking rather than carrying out a task (listening). The initial project attempted to analyze the massive data using cloud-based resources from the Open Science Grid (OSG), but the work progressed slowly due to the complexity of the analysis and the lack of availability of OSG resources. To overcome this issue, Krieger worked with XSEDE ECSS expert Anirban Jana at the Pittsburgh Supercomputing Center (PSC) to use the XSEDE-allocated systems Bridges at PSC and Comet at the San Diego Supercomputer Center as overflow resources, integrated into his pre-existing OSG solution and using only idle cycles on the XSEDE machines. Together, the two XSEDE resources reduced the expected computational time from over a year to seven months. Initial results indicate a clear, statistically robust difference in 164 brain regions between both the healthy controls and the TBI patients, the HIV-at-risk patients versus the TBI patients, and HIV-at-risk versus healthy. While the current focus of the work is methodological, there is a clear indication that the technique may be useful for diagnosing early TBI and testing possible interventions, such as transcranial magnetic stimulation. The researchers have reported their initial results in arXiv Quantitative Biology. 2.6. Tools of the 21st Century: HPC, Analytical Ultracentrifugation, and a New Detector West Nile virus is maintained in the environment by a mosquito-bird transmission cycle. Although most infected people develop no symptoms or a mild flu-like illness, in some cases, West Nile virus can enter the brain and induce encephalitis. In a paper published by Margo Brinton and colleagues in Analytical Chemistry, the researchers describe how they previously used viral stem loop RNAs to pull out cell proteins that specifically interact with them. "That's how we initially found TIAR, which is a cell protein that interacts with one of the viral stem loop RNAs," Brinton said. The result: the researchers found that the stoichiometry of the interaction was four TIAR proteins to one viral stem loop RNA molecule. "This finding told us that this viral RNA-cell protein interaction is more complex than we expected," Brinton explained. "Based on previous data that was obtained under test tube conditions, we speculated that there were two protein binding sites on the viral stem loop RNA and finding four proteins in the complex was surprising. Whether one RNA molecule binds to four individual TIAR molecules, or two dimers, or one tetramer, with the RNA functioning as an oligomerization enhancer, must next be determined.” The research was enabled by a new technology that could have a broad impact by expanding the biological systems that scientists can study. Borries Demeler, an author on the paper and a biophysicist at The University of Texas Health Science Center in San Antonio, develops software for analytical ultracentrifugation, a biophysical technique that spins biopolymers and protein
RY2 IPR 4 Page 10 molecules at high speed to characterize how they interact. This technology can separate biological macromolecules by spinning them at up to 60,000 revolutions per minute, generating forces that are about 300,000 times as strong as the Earth's gravity. Over several years, Demeler has developed analysis software for experiments performed with analytical ultracentrifuges. The goal is to facilitate the extraction of all of the information possible from the available data. "To do this, we developed very high-resolution analysis methods that require high performance computing (HPC) to access this information," he said. "We rely on HPC. It's absolutely critical." "The calculations that underlie the fitting of experimental data requires finite element solutions and esoteric optimization routines that require a lot of computational power." The UltraScan project also has a community allocation through XSEDE using Stampede2 (TACC), Jetstream (TACC), and Comet (SDSC). "These are the main resources we're using," Demeler said. Importantly, these researchers get the benefit of doing top-notch science with high-resolution analysis by HPC without ever having to have expertise in biophysics or computer science. All of the analyses are done via web interface, which provides computing cycles—transparently to any user—through an NSF/XSEDE allocation grant awarded to Demeler. The results from this study contribute to the overall body of biological knowledge. But they also represent the first application of multi-wavelengths ultracentrifugation to a discrete protein/RNA interaction problem. 2.7. XSEDE Resources Help Confirm LIGO Discovery The recently announced discovery of gravitational and light waves generated by the collision of two neutron stars eons ago was made possible in part by XSEDE resources and staff expertise (see Figure 6). Formatted: Font: Not Bold, Underline, Font color: The discovery was made using the National Science Foundation’s Laser Interferometer Custom Color(RGB(46,118,161)) Gravitational Wave Observatory (LIGO), which recently earned three researchers the 2017 Deleted: Figure 6 Nobel Prize in Physics for their detection of gravitational waves in the universe, as hypothesized by Albert Einstein in 1915. XSEDE, a network of the National Science Foundation's cyberinfrastructure investments, includes not only high- performance computing systems, but experts who collaborate with researchers to move projects forward. LIGO first obtained access to XSEDE resources in 2013 and is now using the Open Science Grid as its standard interface for XSEDE and other shared computing systems.
Since then, LIGO has been allocated almost four million hours via the OSG on both Figure 6: “Cataclysmic Collision” - Artist’s illustration of XSEDE and non-XSEDE resources, out of two merging neutron stars. The rippling space-time grid which 628,602 hours were on Comet, based represents gravitational waves that travel out from the collision, while the narrow beams show the bursts of at SDSC and 430,960 hours on Stampede, gamma rays that are shot out just seconds after the based at TACC, according to Frank gravitational waves. Swirling clouds of material ejected Würthwein, OSG's executive director and from the merging stars are also depicted. The clouds glow lead for distributed high-throughput with visible and other wavelengths of light. NSF/LIGO/Sonoma State University/A. Simonnet
RY2 IPR 4 Page 11 computing at SDSC. Via XSEDE allocations alone, LIGO researchers consumed millions of hours on both Comet and Stampede. This collaboration also provided LIGO with access to XSEDE's ECSS staff. During the first year of the collaboration, ECSS worked with LIGO to increase the speed of the applications—making them 8-10 times faster on average. "The collaborative efforts XSEDE has engaged in with the LIGO project have been quite fruitful and we look forward to ongoing observations of events and greater understanding of our universe,” said XSEDE Project Director, John Towns.
RY2 IPR 4 Page 12 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 our 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. In some cases, metrics for impact, outcome, or both are not currently being collected. In these cases, the best available metric is used. 3.1. 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 Although efforts to identify new technologies and new service providers along with efforts to evolve the e-infrastructure and enhance the research prowess of current and future researchers all serve to also deepen use, the collaborative, year-long work done to help research teams more effectively and broadly use the ecosystem is the best indicator of deeper use. These efforts enable increased scale and efficiency of usage and allow use of new capabilities for the delivery of science. The project has chosen three metrics (Table 3-1) that together measure the scope, Formatted: Underline, Underline color: Accent 1, Font quality, and impact of these activities: 1) the total number of projects completed by the Extended color: Accent 1 Collaborative Support Services team through work with research teams, community codes, and Deleted: Table 3-1 gateways and the average ratings for 2) satisfaction with and 3) overall impact from the ECSS work. Satisfaction and overall impact are scores provided by the PIs of the projects on a 1-5 scale following project completion.
Table 3-1: KPIs for the sub-goal of deepen use to (existing communities).
Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Average ECSS RY5 ECSS (§5) impact rating RY4 RY3 RY2 4 of 5/yr 4.11 4.03 4 of 5 RY1 * 4.56 4.61 3.29 4.14 /qtr Average RY5 ECSS (§5) satisfaction RY4 with ECSS support RY3 4.5 of 5 RY2 4.65 4.56 /yr 4.5 of 5 RY1 * 4.86 4.72 4.64 4.54 /qtr RY5 ECSS (§5)
RY2 IPR 4 Page 13 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of RY4 completed ECSS RY3 projects RY2 50/yr 16 9 RY1 50/yr * 10 13 25 48 Measures of KPIs for this sub-goal are within expectations and are projected to meet or exceed targets. 3.1.2. Extending Use to New Communities New communities are defined as new fields of science, industry, and underrepresented communities. New fields of science are those that represent less than 1% 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 sustained users on research projects from under-represented communities and non-traditional disciplines of XSEDE resources and services as the indicators of progress (Table 3-2). Formatted: Underline, Underline color: Accent 1, Font color: Accent 1 Table 3-2: KPIs for the sub-goal of extend use (new communities). Deleted: Table 3-2 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of new ECSS — NIP RY5 users from (§5.3) underrepresented RY4 communities and CEE — non-traditional Broadening disciplines of RY3 Participation XSEDE resources (§4.4) and services RY2 1,100/yr 352 274
RY1 >500/yr * 297 227 398 922 Number of ECSS — NIP RY5 sustained users (§5.3) from CEE — RY4 underrepresented Broadening communities and Participation RY3 non-traditional (§4.4) disciplines of RY2 3,500/yr 1,190 1,117 XSEDE resources and services RY1 2,600/yr1 * 773 755 1,251 2,779 1Target has been updated as NIP adjusted their target in RY1 RP4. Measures of KPIs for this sub-goal are within expectations and are projected to meet or exceed targets. 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. This, and a complementary measure of impact as indicated by those
RY2 IPR 4 Page 14 same individuals, are therefore considered the key indicators (Table 3-3) of performance toward Formatted: Underline, Underline color: Accent 1, Font this goal. color: Accent 1
Table 3-3: KPIs for the sub-goal of preparing the current and next generation. Deleted: Table 3-3
Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of RY5 CEE — attendees in RY4 Workforce synchronous Development RY3 and (§4.2) asynchronous RY2 5,200/yr 1,305 890 training RY1 6,000/yr * 1,304 1,084 1,264 3,652 Average impact RY5 CEE — assessment of Workforce training for RY4 Development attendees (§4.2) RY3 registered through the RY2 4 of 5/qtr 4.29 4.34 XSEDE User Portal RY1 4 of 5/qtr * 4.54 4.39 4.28 4.36 Measures of KPIs for this sub-goal are within expectations and are projected to meet or exceed targets. See §4.2 for an explanation about why the number of attendees in synchronous and asynchronous trainings appears low. 3.1.4. Raising Awareness While many PY6 activities, such as our Workforce Development (§4.2), User Engagement (§4.3) and Broadening Participation (§4.4) efforts, and the visibility of our Champions and other Campus Engagement (§4.6) activities, contribute to our ability to raise the general awareness of the value of advanced digital research services, we have chosen to focus on measures in four areas (Table 3-4): website, social media, public relations, and media hits. Desirable trends in Deleted: Table 3-4 these key outcomes can be correlated to success for this sub-goal. Formatted: Underline, Underline color: Accent 1, Font Table 3-4: KPIs for the sub-goal of raise awareness of the value of advanced digital research services. color: Accent 1
Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of RY5 Community pageviews to Engagement the XSEDE RY4 & website Enrichment— RY3 UII (§4.5) 80,000 RY2 63,998 NA1 /qtr 80,000 RY1 * 49,409 65,157 68,227 183,212 /qtr Number of RY5 Community pageviews to Engagement the XSEDE RY4 & Enrichment User Portal — UII (§4.5) RY3 250,000 RY2 256,482 282,722 /qtr
RY2 IPR 4 Page 15 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year
100,000 RY1 * 183,408 219,644 265,151 670,080 /qtr
Number of RY5 Program Social Media Office — ER impressions RY4 (§9.2) RY3 300,000 RY2 69,607 55,506 /yr 190,000 RY1 * 52,500 128,675 81,332 262,207 /yr Number of RY5 Program media hits Office — ER RY4 (§9.2) RY3 RY2 169/yr 42 30 RY1 140/yr * 32 30 18 80
1Google Analytics was not tracking correctly so we do not have numbers for this quarter. Measures of KPIs for this sub-goal are within expectations and are projected to meet or exceed targets. The “Number of Social Media impressions” KPI appears to be less than expected; however, we expect to reach the target by the end of the report year. See §9.2 for details. 3.2. 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 be the most impactful. Thus, XSEDE focuses on the number of new capabilities made available for production deployment along with the satisfaction rating of all XSEDE Cyberinfrastructure Integration (XCI) services as indicators of performance with respect to this sub-goal (Table 3-5). Formatted: Underline, Underline color: Accent 1, Font color: Accent 1 Table 3-5: KPIs for the sub-goal of create an open and evolving e-infrastructure. Deleted: Table 3-5 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of RY5 XCI (§6) new RY4 capabilities made RY3 available for RY2 7/yr 1 2
RY2 IPR 4 Page 16 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year production RY1 7/yr * 0 0 6 6 deployment Average RY5 XCI (§6) satisfaction RY4 rating of XCI services RY3 4 of 5 RY2 4.25 4.5 /yr 4 of 5 RY1 * 4.8 4.81 4.5 4.5 /yr 1 This number is for RACD only as XCRI satisfaction numbers are unavailable for this reporting period. Measures of KPIs for this sub-goal are within expectations and are projected to meet or exceed targets. 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 average rating by staff for training will continue to be a KPI, though the staff climate survey question that measures this has been changed to focus on staff having the training they need, instead of staff turning to XSEDE training to fulfill these needs (Table 3-6). This represents a change in approach Formatted: Underline, Underline color: Accent 1, Font to staff training that reflects budget constraints, where we will leverage existing training offered color: Accent 1 by the Service Providers, universities, and professional associations alongside our own training Deleted: Table 3-6 to enhance the expertise of staff.
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 Average rating RY5 Program Office of staff RY4 — Strategy, regarding how RY3 Planning, Policy, well-prepared 4 of 5 Evaluation & RY2 - 3.40 they feel to /yr Organizational perform their Improvement 4 of 5 jobs RY1 * - - - - (§9.5) /yr - Data reported annually. Staff sentiment regarding job preparedness was under target. The L2 Directors and L3 Managers have been provided their group’s rating for this metric and will be required to provide a response including plans to address this during the December quarterly meeting. 3.3. 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.
RY2 IPR 4 Page 17 3.3.1. Provide Reliable, Efficient, and Secure Infrastructure Many activities support this sub-goal—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 a composite measure of the availability of XSEDE infrastructure components and the number of hours of downtime with direct user impacts from security incidents (Table 3-7). The composite measure is a geometric mean of the availability of Formatted: Underline, Underline color: Accent 1, Font critical enterprise services and the XRAS allocations request management service. color: Accent 1
Table 3-7: KPIs for the sub-goal of provide reliable, efficient, and secure infrastructure. Deleted: Table 3-7 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Average RY5 XSEDE composite RY4 Operations (§7) availability of RAS (§8) core services RY3 (geometric RY2 99.9%/qtr 99.8 99.9 mean of critical services and RY1 99%/qtr * 99.9 99.9 99.9 99.9 XRAS) Hours of RY5 Cybersecurity downtime with RY4 (§7.2) direct user impacts from RY3 an XSEDE RY2 0/qtr 0 0 security RY1 < 24/qtr * 0 146 0 146 incident Measures of KPIs for this sub-goal meet or exceed targets. 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) metrics with respect to ticket resolutions times and the average satisfaction rating of the allocations process. The XOC is the frontline centralized support group that either resolves or escalates tickets to the appropriate resolution center depending on the request. Two KPIs are measured (Table 3-8): the mean time to resolution on support tickets that Deleted: Table 3-8 are resolved by the XOC or routed to, and resolved by, other XSEDE areas, and the average Formatted: Underline, Font color: Custom satisfaction rating for the allocations process measured via a quarterly survey of users who have Color(RGB(79,129,189)) interacted with the allocations request system and the allocations process more generally.
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 RY5 XSEDE ticket Operations (§7) RY4 resolution by XOC and WBS RY3 ticket queues RY2 < 24/qtr 26 20.1 (hrs) RY1 < 24/qtr * 24.0 28.2 23.1 25.1 RY5 RAS (§8)
RY2 IPR 4 Page 18 Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Average user RY4 satisfaction RY3 with allocations RY2 4 of 5/yr 4.08 4.00 process and other support RY1 4 of 5/yr * 3.98 4.03 4.03 4.01 services The quarterly measure of KPIs for this sub-goal have met the target. The annual measure is within expectations and projected to meet or exceed the target. 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 Criteria4 and has assessed and applied applicable criteria from all seven criteria by that methodology. These include annual reviews of the vision, mission, strategic goals, project-wide processes and standards (KPIs and area metrics); user and staff surveys (§4.3, §9.5); stakeholder communications (§9.2); advisory boards (§9.1); community engagement (§4); workforce enhancement (§4.2); and the analysis of organizational data that leads to organizational learning, strategic improvement and innovation. Thus, XSEDE has chosen to monitor its continuous improvement efforts as an organizational measurement of recommendations addressed by the relevant project areas (Table 3-9). The idea Formatted: Underline, Underline color: Accent 1, Font here is that a stable or expanding number of improvements shows that XSEDE continues to color: Accent 1 systematically evaluate the organization and make informed, proactive improvements. Deleted: 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 Percentage of RY5 Project Office recommendations RY4 — Strategic addressed by RY3 Planning, relevant project RY2 90%/qtr 23 15 Policy & areas Evaluation 1 RY1 90%/qtr * NA 100 57 67 (§9.5) 1 L2 Directors are currently responding to climate study recommendations; data will become available in RY1 RP3. Strategic Planning and Evaluation continues to discuss the collection and measurement issues as part of the XSEDE-wide Metric Review process. See §9.5 for details on this metric. 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. A partial measure is the number of staff publications produced, as this shows that XSEDE staff are involved in novel activities that achieve peer- reviewed publication. Additionally, after much thought and discussion both internally and with external stakeholders and advisors, we have identified two additional indicators that strongly correlate to innovation: 1) the ratio of proactive organizational improvements to those that were reactive and 2) the number of improvements that are innovative or lead to innovations. The first indicator is a measurement of organizational maturity and agility; the second measures innovative actions directly (Table 3-10). While these provide some indication of innovation, they Deleted: Table 3-10
Formatted: Underline, Font color: Custom 4 http://www.nist.gov/baldrige/ Color(RGB(79,129,189))
RY2 IPR 4 Page 19 are still not satisfactory. 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. In particular, this will be discussed in earnest within the Strategy, Planning, Policy, Evaluation & Organizational Improvement team (§9.5).
Table 3-10: KPIs for the sub-goal of operate an innovative organization.
Report KPI Target RP1 RP2 RP3 RP4 Total Owner Year Number of RY5 Program Office staff (§9) publications RY4 RY3 RY2 20/yr 2 6 RY1 70/yr * 5 0 13 18 Number of key RY5 Project Office improvements — Strategy, RY4 addressed Planning, from RY3 Policy, systematic RY2 10/yr NA3 NA3 Evaluation & evaluation1 Organizational RY1 * * * * 19 19 Improvement (§9.5) Number of key RY5 Project Office improvements — Strategy, RY4 addressed Planning, from external RY3 Policy, sources.1 RY2 10/yr 2 6 Evaluation & Organizational RY1 * * * * 11 11 Improvement (§9.5) Ratio of RY5 Project Office proactive to — Strategy, RY4 reactive Planning, improvements RY3 Policy, RY2 4:1/yr 1:1 1:2 Evaluation & Organizational RY1 3:1/yr * 2:12 7:1 8:9 17:11 Improvement (§9.5) 1 This metric is one of two new metrics that resulted from splitting a former metric, called "Number of strategic or innovative improvements," into two. The former metric had a target of 9/yr and results were 3 for RP2 and 8 for RP3 in RY1. These two new metrics each have a target of 10/yr and will be reported annually. 2 Number was updated from what was originally reported due to reporting error. 3 L2 Directors will be responding to climate study recommendations prior to the IPR for RY2 RP3. With exceptions noted below, measures of KPIs for this sub-goal are within expectations and are projected to meet or exceed targets. The “Number of key improvements addressed from systematic evaluation” metric will be addressed during the December quarterly meeting as part of the L2 functional area updates. The “Ratio of proactive to reactive improvements” KPI continues to be low. Strategic Planning and Evaluation continues to discuss the collection and measurement issues as part of the XSEDE- wide Metric Review process. See §9.5 for details on this metric.
RY2 IPR 4 Page 20 4. Community Engagement & Enrichment (WBS 2.1) 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 area metrics 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. Area metrics for CEE are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 4-1: Area Metrics for Community Engagement & Enrichment. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of new RY5 Deepen/ users of XSEDE RY4 Extend — resources and Extend use services RY3 to new 2,000 RY2 2,305 2,813 communities /qtr (§3.1.1) > 1,000 RY1 * 1,9731 1,8491 2,359 6,181 /qtr Number of RY5 Deepen/ sustained users RY4 Extend — of XSEDE Deepen use RY3
RY2 IPR 4 Page 21 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported resources and 3,000 to existing RY2 3,962 3,754 services /qtr communities > 5,000 (§3.1.1) RY1 * 4,755 4,4461 4,924 6,186 /qtr Number of new RY5 Deepen/ users from RY4 Extend — under- Extend use represented RY3 to new communities RY2 150/qtr 251 175 communities using XSEDE (§3.1.2) resources and > RY1 * 150 135 240 525 services 100/qtr
Number of RY5 Deepen/ sustained users RY4 Extend — from under- Deepen use represented RY3 to existing 1,500 communities RY2 490 408 communities using XSEDE /yr (§3.1.1) resources and > 1,000 RY1 * 322 238 535 1,095 services /yr Number of RY5 Deepen/ attendees in RY4 Extend — synchronous Prepare the and RY3 current and 5,200 asynchronous RY2 1,305 890 next training /yr generation > 6,000 RY1 * 1,304 1,084 1,264 3,652 (§3.1.3) /yr Average impact RY5 Deepen/ assessment of RY4 Extend — training for Prepare the attendees RY3 current and 4 of 5 registered RY2 4.29 4.34 next through XSEDE /qtr generation 4 of 5 User Portal RY1 * 4.54 4.39 4.28 4.36 (§3.1.3) /qtr Number of RY5 Deepen/ pageviews to RY4 Extend — the XSEDE Raise website RY3 awareness 80,000 RY2 63,998 NA2 of the value /qtr of advanced 80,000 digital RY1 * 49,409 65,157 68,227 183,212 services /qtr (§3.1.4) Number of RY5 Deepen/ pageviews to RY4 Extend — the XSEDE User Raise Portal RY3 awareness 250,000 RY2 256,482 282,722 of the value /qtr of advanced digital 100,000 RY1 * 183,408 219,664 261,115 670,080 services /qtr (§3.1.4) 1Number was updated from original reported as this number was calculated incorrectly. 2Google Analytics was not tracking correctly so we do not have numbers for this quarter.
RY2 IPR 4 Page 22 CEE is on target or exceeding most L2 Area Metric targets with two notable exceptions. The numbers of attendees for both asynchronous and synchronous trainings appear low because we are only tracking attendees who are registered for the event via the XSEDE User Portal (XUP). Numbers would be higher and closer to metric targets if we were adding the attendees who did not register via the XUP; however, we were asked to collect verified, specific, user data. We continue to encourage attendees to register through the XUP. Additionally, numbers for trainings are lower due to training date and academic calendar conflicts for the August training as well as September training sites being affected by hurricane closures. We will closely monitor this metric over the next quarter. Finally, the number of pageviews to the XSEDE website was not reported this quarter because Google Analytics was not tracking correctly; this has been resolved for future metric tracking. CEE Highlights The CEE User Information and Online Interfaces (UII) team reached a major milestone with the release of a new and improved XSEDE website. The goal of the XSEDE website redesign was to present a polished, professional, comprehensive, state-of-the-art web presence that effectively markets to external audiences and provides intuitive access to information and applications for an internal audience of users. The objectives of the new web site are as follows: unify the XSEDE brand and logo by extending it to the website; have a common consistent theme and design; remove outdated information and provide current updated information on the site; improve the organization and presentation of the content provided for easy navigation; redesign the site to be more visually appealing and easily accessible on mobile devices; devise a more systematic method to create and maintain content by assigning area leads to each part of the site and ensure quarterly sign off on site content; and be intentional about what pages are created and where they live in the site architecture. With this in mind, the UII team led a comprehensive redesign of the site with an updated look and feel as well as new content. The UII team worked with the entire XSEDE organization for technical content, visual guidance, architecture, and navigation and with the Senior Management Team specifically for approval with the new site going live in early September. The CEE User Engagement (UE) team is contributing to the cross-area effort to more effectively manage user accounts to eliminate stale accounts as a vector for intrusion. To date, PIs have been responsible for adding and removing users from their projects, with users being left on by default at project renewal. The new plan calls for requiring PIs to specify which users are to remain on the project when submitting renewal proposals, thus minimizing the number of stale accounts. The RAS and UII teams developed a timeline to complete their tasks by the end of November 2017. RAS and UE are currently working on user announcements and edits to existing communications to research teams regarding their allocations and future proposal submissions, all scheduled to be complete in time for the proposal submission window that opens on December 15, 2017. To begin to reduce the number of stale accounts, UE contacted all current PIs on October 3, requesting they review their current users and remove those who should no longer have access. There were 975 distinct people removed from active allocations during the period October 3 through October 31. For comparison, the month of September 2017 had 573 distinct people removed from allocations. The CEE Workforce Development (WD) team is continuing to expand the XSEDE community with the XSEDE EMPOWER program. In August, six undergraduate students completed their summer XSEDE EMPOWER programs, and a new round of applicants were. The Education team within CEE WD collaborated with Campus Engagement throughout the review process, and five new undergraduate students have now begun new EMPOWER projects with faculty advisors. These undergraduate students, both summer and fall, will continue to be supported by CEE WD
RY2 IPR 4 Page 23 going forward and will be encouraged to present their work via student research posters at PEARC18. The CEE Student Programs and Education teams continue joint engagement efforts with Broadening Participation through exhibits at conferences including Tapia Diversity in Computing, Grace Hopper, and Society for Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS). Going forward, the Education team is developing relationships and making initial plans to offer professional development workshops for faculty at South Carolina State University and Claflin University. Furthermore, despite some sites being unable to participate due to the effects of hurricanes, CEE WD Training workshops offered in August and September were very well received with site contacts sending reports such as: “It went well here, and participants appreciated what they learned today. As always, good workshop.” and “A wonderful course that was masterfully executed.” After productive discussions at the August XSEDE quarterly meeting, the Campus Champions have made progress on sustainability by developing recommendations for neutral (non-XSEDE) mailing lists and web presence. We have established a Technology Refresh working group, which has recommended Google groups for email lists and is also exploring G Suite for a new Champion communications platform overall. This working group recommended that the Champions consider exploring becoming a non-profit organization (e.g., 501(c)(3)). Requests for membership in the Champion community have been received from industry and international higher education institutions. Historically, Champions have been based at US academic and non-profit organizations. The Champion leadership team will begin discussions to determine membership requirements with advice and support from XSEDE leadership. Recent Champion and XSEDE staff collaborations include 12 new Champion User Requirements Evaluation and Prioritization (UREP) reviewers, new Training material reviewers, a Novel and Innovative Projects (NIP) call and a Champion call devoted to collaborations and Domain Champions. A substantial number of Campus Champions are engaging in a broad spectrum of workforce and professional development activities, suggesting that this population is a key contributor to expanding the national Cyberinfrastructure workforce. 4.1. 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 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. 4.2. 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,
RY2 IPR 4 Page 24 integrators, and students utilizing advanced digital resources. This includes providing professional development for XSEDE team members. Workforce Development provides an integrated suite of training, education, and student preparation activities to address formal and informal learning about advanced digital resources addressing the needs of researchers, developers, integrators, IT staff, XSEDE staff, faculty, and undergraduate and graduate students. 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. Area metrics for Workforce Development are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 4-2: Area Metrics for Workforce Development. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of RY5 Deepen/Extend unique RY4 — Prepare the attendees, current and next synchronous RY3 generation 1,200 training RY2 325 438 (§3.1.3) /yr 1,600 RY1 112 371 551 981 /yr * Number of RY5 Deepen/Extend total attendees, RY4 — Prepare the synchronous current and next training RY3 generation 1,400 (One person RY2 921 484 (§3.1.3) can take /yr several 2,000 RY1 * 188 382 614 1,184 classes) /yr Number of RY5 Deepen/Extend unique RY4 — Prepare the attendees, current and next asynchronous RY3 generation 1,200 training RY2 148 170 (§3.1.3) /yr 2,000 RY1 * 215 213 211 520 /yr Number of RY5 Deepen/Extend total attendees, RY4 — Prepare the asynchronous RY3 current and next training (One generation 4,000 person can RY2 384 406 (§3.1.3) /yr
RY2 IPR 4 Page 25 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported take several 4,000 classes) RY1 * 1,116 702 650 2,468 /yr
Average impact RY5 Deepen/Extend assessment of RY4 — Prepare the training for current and next attendees RY3 generation 4 of 5 registered RY2 4.29 4.34 (§3.1.3) through the /qtr XSEDE User 4 of 5 RY1 * 4.54 4.39 4.28 4.36 Portal /qtr Number of RY5 Deepen/Extend formal degree, RY4 — Prepare the minor, and current and next certificate RY3 generation programs RY2 3/yr 0 0 (§3.1.3) added to the RY1 3/yr * 1 0 1 2 curricula Number of RY5 Deepen/Extend materials RY4 — Prepare the contributed to current and next public RY3 generation repository RY2 50/yr 0 10 (§3.1.3) RY1 40/yr * 10 25 10 45 Number of RY5 Deepen/Extend materials RY4 — Prepare the downloaded current and next from the RY3 generation 62,000 repository RY2 16,545 21,340 (§3.1.3) /yr 56,000 RY1 * 11,114 25,233 25,279 61,626 /yr Number of RY5 Deepen/Extend computational RY4 — Prepare the science current and next modules added RY3 generation to courses RY2 40/yr 18 - (§3.1.3) RY1 40/yr * - - - NA1 Number of RY5 Deepen/Extend students RY4 — Prepare the benefiting from current and next XSEDE RY3 generation resources and RY2 950/qtr 1,722 1,478 (§3.1.3) services RY1 50/qtr * 9972 1,148 2,679 4,824 Percentage of RY5 Deepen/Extend underrepresen RY4 — Prepare the ted students RY3 current and next benefiting from 50% generation RY2 28 27 XSEDE /qtr (§3.1.3) resources and 50% RY1 * 342 33 19 28 services /qtr - Data reported annually. 1 Not measured yet. Survey will go out in RY2 RP1. 2 Number was updated from original reported as this number was calculated incorrectly. We are on track to meet our annual targets with materials added to repository and materials downloaded from the repository. We replaced our student who is the largest content manager
RY2 IPR 4 Page 26 for these materials and the transition slowed down our additions to the site; we are confident that we can pick up the pace in the latter part of the reporting year to reach our overall goal. Several computational programs are in process and are awaiting administrative approval. The numbers of attendees for both asynchronous and synchronous trainings appear low because we are only tracking attendees who registered via the XSEDE User Portal (XUP). Numbers would be higher and closer to metric targets if we were adding the attendees who do not registered via the XUP; however, we were asked to collect verified, specific, user data which is recorded in the XUP. We continue to encourage attendees to register through the XUP when taking asynchronous training and are discussing if XUP registration should be required. CI-Tutor and Cornell Virtual Workshop would need to be modified to use the updated login procedures. Additionally, numbers for trainings are lower due to training date and academic calendar conflicts for the August training as well as September training sites being affected by hurricane closures. We have continually fallen short of our goal of 50% underrepresented students benefiting from XSEDE resources and services. We recognize that this is a difficult target to reach but it serves to drive home the point that our efforts in workforce development (and broadening participation) are much needed as this is an issue that is so persistent. 4.3. 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 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. Area metrics for User Engagement are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 4-3: Area Metrics for User Engagement.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Percentage of RY5 Sustain — active and RY4 Provide new PIs RY3 excellent contacted 100% 100 100 user support RY2 /qtr (2,040) (2,026) (§3.3.2) 100% 100 100 100 RY1 * ** /qtr (1,533) (1,586) (2,062)2 Percentage of RY5 Sustain — user RY4 Provide requirements RY3 excellent resolved1 100% 78 102 user support RY2 /yr (36/44) (47/46) (§3.3.2) 100% 50 89 75 74 RY1 * /yr1 (16/32) (40/45) (40/53) (96/130) 1 Resolution may be dependent upon SPs and other XSEDE groups. 2 Query was not catching all PIs, modified for RY1, RP4 and subsequent reporting periods. **Quarterly metric, 5,181 total contact emails sent in RP2-RP4. The metric “Percentage of user requirements resolved” exceeds the target of 100% as there were unresolved action items in RP1 that were resolved in RP2.
RY2 IPR 4 Page 27 4.4. 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 that 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 XRAC, participation in Champions, 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. Area metrics for Broadening Participation are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 4-4: Area Metrics for Broadening Participation.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of new RY5 Deepen/Extend users from RY4 — Extend use to under- RY3 new represented RY2 150/qtr 251 175 communities communities (§3.1.2) using XSEDE resources and RY1 >100/yr * 150 135 240 525 services Number of RY5 Deepen/Extend sustained RY4 — Extend use to users from RY3 new under- 1,500 communities RY2 490 408 represented /yr (§3.1.2) communities using XSEDE >1,000 RY1 * 322 238 535 1,095 resources and /yr services Longitudinal RY5 Advance — Assessment of RY4 Enhance the inclusion in RY3 array of XSEDE via the 5% technical Staff Climate RY2 improve- - 4.25 expertise and Study ment/yr support services 5% (§3.2.2) RY1 improve- * - - - - ment/yr Longitudinal RY5 Advance — Assessment of RY4 Enhance the Equity in RY3 array of
RY2 IPR 4 Page 28 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported XSEDE via the 5% technical Staff Climate RY2 improve- - -5 expertise and Survey ment/yr support services 5% (§3.2.2) RY1 improve- * - - - - ment/yr - Data reported annually. The number of new and sustained users from underrepresented communities are on target. The climate study revealed that gains were made in the area of inclusion, but in the area of equity there was a decline from 2016 to 2017. The inclusion gain is close to the target of 5%. Unlike previous years where men rated equity items significantly higher than women, in 2017 no gender differences in equity ratings were found. Women continue to rate the index similarly to previous years. Education activities were conducted at staff meetings to increase awareness of the issue. As a result, it appears men are more aware though not necessarily experiencing discrimination themselves, perhaps explaining the resulting 5% dip in staff perception of equity. XSEDE will continue education and awareness of inclusion and diversity and best practices to improve the project climate. 4.5. 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 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. Area metrics for User Interfaces & Online Information are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 4-5: Area Metrics for User Interfaces & Online Information.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of new RY5 Deepen/ users of XSEDE RY4 Extend — resources and RY3 Extend use services 2,000/ to new RY2 2,305 2,813 qtr communities >1,000/ (§3.1.2) RY1 * 1,9732 1,8492 2,359 6,181 qtr Number of RY5 Deepen/ sustained RY4 Extend — users of XSEDE RY3 Deepen use resources and 3,000/ to existing RY2 3,962 3,754 services qtr communities >5,000 (§3.1.1) RY1 * 4,7552 4,4462 4,924 6,168 /qtr Number of RY5 Advance — pageviews to RY4 Raise
RY2 IPR 4 Page 29 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported the XSEDE RY3 Awareness website 80,000/ of the value RY2 63,998 NA3 qtr of advanced digital 80,000/ RY1 * 49,409 65,157 68,227 183,212 services qtr (§3.1.4) Number of RY5 Advance — pageviews to RY4 Raise the XSEDE User RY3 Awareness Portal 250,000 of the value RY2 256,482 282,722 /qtr of advanced digital 100,000 RY1 * 183,408 219,664 265,115 670,080 services /qtr (§3.1.4) User RY5 Sustain — satisfaction RY4 Provide with website RY3 excellent RY2 4 of 5/yr 4.17 - user support RY1 4 of 5/yr * - - - - (§3.3.2) User RY5 Sustain — satisfaction RY4 Provide with user RY3 excellent portal RY2 4 of 5/yr 4.18 - user support RY1 4 of 5/yr * - - - - (§3.3.2) User RY5 Sustain — satisfaction RY4 Provide with user RY3 excellent documentation 4.25 of user support RY2 4.07 - 5/yr (§3.3.2) RY1 4 of 5/yr * - 4.221 - 4.22 - Data reported annually. 1 This measurement is based on an XSEDE Microsurvey focused on XSEDE User Documentation. The measurement is the mean satisfaction rating for the question “Please rate your level of satisfaction with the technical documentation available on the XSEDE User Portal (XUP) and XSEDE Web Site.” 2 Number was updated from original reported as this number was initially calculated incorrectly. 3 An issue with the Google Analytics was discovered at the end of the quarter and metrics could not be added. The number of new and sustained users exceeds targets. The number of pageviews for the XSEDE User Portal also exceeds the target. We released the new XSEDE website this quarter and unfortunately, the Google Analytics tool used to measure the number of pageviews was not working correctly. Therefore, we do not have those metrics for this quarter but plan to resolve for the reporting period. The user satisfaction metrics are measured annually and are not measured this quarter. 4.6. 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 US 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
RY2 IPR 4 Page 30 digital services from providers at all levels (workgroup, institutional, regional, national, 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. CE also aims to assist with the establishment and expansion of consortia (for example, intra-state, regional, domain-specific) that collaborate to better serve the needs of their advanced computing stakeholders. Area metrics for Campus Engagement are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 4-6: Area Metrics for Campus Engagement.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of RY5 Deepen/Extend Institutions with RY4 — Deepen use a Champion to existing RY3 communities RY2 240 218 238 (§3.1.1) RY1 225 * 224 231 234 234 Number of RY5 Deepen/Extend unique — Deepen use contributors to RY4 to existing the Champion RY3 communities email list RY2 100/yr 129 112 (§3.1.1) (campuschampi [email protected]) RY1 50/yr * 90 112 125 190 Number of Deepen/Extend activities that (i) RY5 — Deepen use expand the to existing emerging CI communities workforce RY4 (§3.1.1) and/or (ii) improve the RY3 extant CI workforce, participated in RY2 40/yr 28 30 by members of the Campus Engagement RY1 20/yr * 10 11 10 30 team
We are within expectations and are projected to meet or exceed targets for all metrics.
RY2 IPR 4 Page 31 5. Extended Collaborative Support Service (WBS 2.2) The Extended Collaborative Support Service (ECSS) improves the productivity of the XSEDE user community both through successful, meaningful collaborations, and well-planned training activities. The goal 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 under-represented 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 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 & 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. If reviewers recommend support and if staff resources are available, the ECSS expert and the requesting PI develop a work plan outlining the project tasks. The work plan includes concrete quarterly goals and staffing commitments from both the PI team and ECSS. ECSS managers review work plans and also track progress via quarterly reports. Area metrics for Extended Collaborative Support Service are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 5-1: Area Metrics for Extended Collaborative Support Service. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported RY5 Deepen/Extend — Average ECSS impact RY4 Deepen use to rating existing RY3 communities RY2 4 of 5 /yr 4.11 4.03 (§3.1.1) 4 of 5 RY1 * 4.56 4.61 3.29 4.14 /qtr Average satisfaction RY5 Deepen/Extend — with ECSS support RY4 Deepen use to existing RY3 communities 4.5 of 5 RY2 4.65 4.56 (§3.1.1) /yr 4.5 of 5 RY1 * 4.86 4.72 4.64 4.54 /qtr Number of completed RY5 Deepen/Extend — ECSS projects RY4 Deepen use to (ESRT + ESCC + existing ESSGW) RY3 communities RY2 50/yr 16 9 (§3.1.1) RY1 50/yr * 10 13 25 48 RY5
RY2 IPR 4 Page 32 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of new users RY4 Deepen/Extend — from non-traditional RY3 Extend use to new disciplines of XSEDE communities RY2 500/yr 101 99 resources and (§3.1.2) services RY1 400/yr * 147 92 158 397
Number of sustained RY5 Deepen/Extend — users from non- RY4 Deepen use to traditional existing disciplines of XSEDE RY3 communities resources and RY2 500/qtr 700 709 (§3.1.1) services RY1 400/qtr1 * 451 517 716 1,684
Average estimated RY5 Deepen/Extend — months saved due to RY4 Deepen use to ECSS support2 RY3 existing 12 mo/ communities RY2 13.61 12.23 project (§3.1.1) RY1 * * * * * * 1 Target has been updated for RY1 RP4. 2 New metric for PY7. Our metrics are on track to meet their RY2 targets, with the possible exception of “Number of new users from non-traditional disciplines.” The NIP team will endeavor to increase the number of new projects in the remaining quarters, but we will also evaluate whether this aggressive target needs to be reduced. Though completed ECSS projects dropped in RP2, when combined with RP1, we are still on track to meet our goal midway through the reporting year. The number of sustained users from non-traditional disciplines far exceeds the target for the second quarter. We will monitor these metrics and submit a PCR after the next reporting period if the trends continue. There were two changes in ECSS leadership that occurred during this reporting period. Ralph Roskies, who has been the co-director of ECSS since the beginning of XSEDE, stepped down from this role. Sergiu Sanielevici has served as deputy director of ECSS and as the NIP L3 Manager and is now serving as interim co-director of ECSS. Philip Blood, who has been previously serving as deputy lead in NIP, now steps into an interim leadership role as NIP’s L3 Manager. Because of the extensive involvement of Sanielevici and Blood leading up to these changes, the transition has been smooth. ECSS also added two project managers, Sonia Nayak and Leslie Morsek, to help with the transition of staff using JIRA and also to aid in the day-to-day management of ECSS. Karla Gendler and Marques Bland will continue their work as PMs, but with Gendler at a reduced level. ECSS Highlights Simulation for 2D Semiconductor with Parallel Uniform and Adaptive Multigrid Method for Phase Field Crystal Models – Dr. Guan, University of California, Irvine A research team led by Dr. Guan of the University of California, Irvine is working to derive phase field crystal (PFC) models for 2D semiconductor growth. Two-dimensional (2D) materials exhibit unique properties due to confinement in the third dimension, which hold promise to yield revolutionary new technologies, ranging from nanosized transistors and efficient light emitting diodes to highly sensitive chemical sensors. To fully utilize these materials, however, it is necessary to develop techniques for growing large-area films while precisely controlling the structure and material defects, which affect material properties. Computational modeling is an
RY2 IPR 4 Page 33 essential tool to understanding this relation between material properties and their growth conditions. With assistance from ESRT consultant Dmitry Pekurovsky (SDSC), the performance and scalability of the existing multigrid solver was improved by incorporating new MPI-OpenMP hybrid code that utilizes P3DFFT libraries in place of previously used FFTW libraries. This allows the study of systems utilizing grids up to 20483; the previous code did not scale beyond 10243. This work is currently in preparation for submission to Computer Physics Communications. Additionally, ESRT consultants David Bock (NCSA), Sudhakar Pamidighantam (IU), and ECSS fellow Dr. Tsai-Wei (Purdue) worked to extend the visualization capability through differential analysis of the resulting electron density fields using Paraview and Ovito. Predictability and Data Assimilation of Severe Weather and Tropical Cyclones, Dr. Fuqing Zhang, Penn State University Greg Foss (TACC) in Extended Support for Community Codes (ESCC) worked with Fuqing Zhang's (Penn State) research group to implement a workflow to visualize hurricanes Joaquin, Gonzalo, and Sandy. The project began using the tool Vapor, but Vapor is not particularly well suited to smooth animations. Although the animation workflow was completed using Vapor, the ESCC project attempted to integrate ParaView, a more general visualization tool, into the visualization pipeline to create smooth animations with detailed terrain graphics. This work was not completed and will continue in a future project. See Figure 7 for an image from one of the Formatted: Font: 11 pt, Underline, Font color: Custom resulting animations that shows superstorm Sandy striking the East Coast. Color(RGB(46,118,161)) Highlights from ECSS Training, Education and Outreach Deleted: Figure 7 In the area of Extended Support for Training, Education and Outreach (ESTEO), Weijia Xu delivered a successful two-day training workshop on Big Data Analysis, hosted by the Partnership for an Advanced Computing Environment (PACE) at Georgia Tech that had 98 attendees. In January 2017, staff from XSEDE's Community Engagement and Enrichment (CEE) Broadening Participation team, as well as ESTEO lead Jay Alameda, visited the University of
Figure 7: An animation showing superstorm Sandy striking the East Coast.
RY2 IPR 4 Page 34 Puerto Rico, Mayaguez. As a result of that meeting, ESTEO members Jay Alameda and Alan Craig submitted a proposal to NSF Computational and Data-Enabled Science and Engineering (CDS&E)/Chemical, Bioengineering, Environmental, and Transport Systems (CBET) with PI Guillermo Arraya. Three ESTEO staff members, Eroma Abeysinghe, Suresh Marru, and Marcus Christie are developing tutorials in collaboration with CEE Workforce Development staff Jeff Sale for gateway administrators and users that will be hosted on Cornell Virtual Workshop. Finally, ESTEO consultants Ritu Arora and Lars Koesterke collaborated to deliver live training to introduce new parallel computing users to OpenMP through use of the Interactive Code Adaptation Tool (ICAT) developed under NSF OAC-1642396 “SI2-SSE: An Interactive Parallelization Tool,” and are working to develop asynchronous training on the same topic to also be delivered via the Cornell Virtual Workshop. Gene Discovery and Creating Annotations for Non-Human Primates, Dr. Chris Mason, Cornell University For the last five years, Professor Chris Mason and Dr. Lenore Pipes, who recently completed her PhD work at Cornell, have been using XSEDE to shed light on the genetic similarities and differences between humans and our non-human primate relatives. When Dr. Pipes first started using Blacklight at PSC in 2012, she had never used a national supercomputing resource. Novel and Innovative Projects (NIP) team member Dr. Phillip Blood worked as a mentor to Pipes, helping her learn how to effectively use XSEDE resources for her work. In 2013, Blood co- authored two papers with this group describing the Non-Human Primate Reference Transcriptome Resource (NHPRTR, nhprtr.org), a database of genes expressed in various species of non-human primates built from massive transcriptome assembly jobs run on XSEDE. This year, Mason and Pipes came back with transcriptome datasets many times larger than the initial NHPRTR data and ran into new challenges assembling these large datasets on Bridges. During this reporting period, Blood worked with Pipes to develop an improved workflow for these long-running jobs that can take up to two weeks to finish. This collaboration led to the successful completion of new transcriptome assemblies, among the largest ever done, which are being used to update the NHPRTR. These updates, which are reported in a paper submitted to Nucleic Acids Research by Mason, Pipes, Blood, and collaborators, will help researchers gain insight into key evolutionary differences between humans and other primates, as well as insight into human health and disease. 5.1. 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 (Sergiu Sanielevici (interim), who manages ESRT and NIP activities, and Nancy Wilkins-Diehr who manages ESCC, ESSGW, and ESTEO activities) and four project managers (Karla Gendler, Marques Bland, Sonia Nayak, and Leslie Morsek).
RY2 IPR 4 Page 35 Sanielevici and Wilkins-Diehr 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. Wilkins-Diehr also organizes the monthly symposium series, serves as one of the contributors to staff training, and runs the Campus Champions Fellows program (§4.6). Sanielevici convenes User Advisory Committee meetings. 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, both for the managers and directors of ECSS and for ECSS staff. 5.2. 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. Area metrics for ESRT are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 5-2: Area Metrics for Extended Support for Research Teams.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average ESRT RY5 Deepen/Extend impact rating RY4 — Deepen use to existing RY3 communities 4 of 5 RY2 3.70 4.63 (§3.1.1) /yr 4 of 5 RY1 * 5.00 4.67 5.00 4.67 /qtr Average RY5 Deepen/Extend satisfaction RY4 — Deepen use with ESRT to existing support RY3 communities 4.5 of 5 RY2 4.60 4.75 (§3.1.1) /yr 4.5 of 5 RY1 * 5.00 4.33 5.00 4.86 /qtr Number of RY5 Deepen/Extend completed RY4 — Deepen use ESRT projects to existing RY3 communities RY2 30/yr 10 2 (§3.1.1) RY1 30/yr * 3 6 15 24 Average RY5 Deepen/Extend estimated RY4 — Deepen use
RY2 IPR 4 Page 36 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported months saved RY3 to existing due to ESRT 6 mo/ communities RY2 18.3 16 support1 project (§3.1.1) RY1 * * * * * * 1 New metric for PY7. For this reporting period, we are meeting our targets for average ESRT impact rating, average satisfaction, and average estimated months saved. However, we are slightly behind on the number of completed ESRT projects. Our goal for this metric is 30 per year. Currently, we are at 12 completed projects over the first two reporting periods, which is slightly behind the 15 we would expect by this point in the year. The main reasons for the downtrend in completed projects this reporting period are that two of projects were extended beyond their original end dates and compensation from the slightly higher than average number of projects that completed last reporting period. We still expect to meet this target by the end of the year. We currently have 30 active projects. 5.3. Novel & Innovative Projects (WBS 2.2.3) Novel & 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. Area metrics for NIP are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 5-3: Area Metrics for Novel & Innovative Projects. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of new RY5 Deepen/Extend users from non- RY4 — Extend use to traditional new disciplines of XSEDE RY3 communities resources and RY2 500/yr 101 99 (§3.1.2) services RY1 400/yr * 147 92 158 397
Number of RY5 Deepen/Extend sustained users RY4 — Deepen use to from non- existing traditional RY3 RY2 500/qtr 700 709
RY2 IPR 4 Page 37 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported disciplines of XSEDE communities resources and (§3.1.1) 1 services RY1 400/qtr * 451 517 716 1,684
Number of new RY5 Deepen/Extend XSEDE projects from RY4 — Extend use to target communities new generated by NIP RY3 communities RY2 30/yr 11 10 (§3.1.2) RY1 20/yr * 16 5 17 38 Number of RY5 Deepen/Extend successful XSEDE RY4 — Extend use to projects from target new communities RY3 communities mentored by NIP RY2 25/qtr 24 35 (§3.1.2) RY1 20/qtr1 * 23 25 34 82 1 Target has been updated for RY1 RP4. We are on target to meet our RY2 target for “Number of new XSEDE projects from target communities”, but we may have been too optimistic in raising our target for “Number of new users from non-traditional disciplines” from 400 to 500 new users per year. New projects from non-traditional disciplines typically have few active users. We are also consistently far exceeding our target for “Number of sustained users from non-traditional disciplines.” We will evaluate whether these metrics need to be adjusted. At the same time, the NIP team will evaluate our efforts to reach new users and endeavor to increase the number of new projects in the remaining quarters. 5.4. 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 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. Area metrics for ESCC are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 5-4: Area Metrics for Extended Support for Community Codes.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average ESCC RY5 Deepen/Extend impact rating RY4 — Deepen use to existing RY3 communities 4 of 5 RY2 5 1 (§3.1.1) /yr 4 of 5 RY1 * 4.67 5 0 3.00 /qtr
RY2 IPR 4 Page 38 Average RY5 Deepen/Extend satisfaction RY4 — Deepen use with ESCC to existing support RY3 communities 4.5 of 5 RY2 5 2.75 (§3.1.1) /yr 4.5 of 5 RY1 * 4 5 4 4.57 /qtr Number of RY5 Deepen/Extend completed RY4 — Deepen use ESCC projects to existing RY3 communities RY2 10/yr 4 4 (§3.1.1) RY1 10/yr * 3 4 3 10 Average RY5 Deepen/Extend estimated RY4 — Deepen use months saved RY3 to existing due to ESCC 12 mo/ communities RY2 11.5 3 support1 project (§3.1.1) RY1 * * * * * * 1 New metric for PY7. We are on target for completed projects but received low ratings for Impact and Satisfaction. As indicated in our Other Metrics (§12.2.2.2.3), only two PI interviews were conducted this reporting period. In one project, there were communications issues between the PI and the ECSS expert, causing the project to stall for several weeks. Due to the delays, the ESCC manager terminated the project early. However, the PI was not pleased with the decision to end the project before the end of the allocation. The PI rated both satisfaction and impact a “1.” After a thoughtful conversation during the PI interview and later amongst the management team, TACC was able to identify a staff member with the right skills to work closely with the PI to achieve the goals. There were several lessons learned here for the ECSS management team. One is to pay careful attention to communication styles and not just technical expertise when pairing staff with PI teams. A second is to check reports more closely to uncover building problems and solve them proactively. The second PI interview this quarter involved a project where ECSS staff did more of a literature search for the PI to determine the availability of particular Java libraries. The PI was very satisfied with the support (4.5/5), but felt he couldn’t evaluate the impact and gave this a “not applicable” rating. Thus the impact rating across ESCC this quarter is due solely to the 1/5 rating from the project described in the paragraph above. 5.5. 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 projects primarily begin through user requests from the XSEDE allocation process. Similar to ESRT and ESCC, ESSGW 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. Third, when the project is completed, the ESSGW 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.
RY2 IPR 4 Page 39 Area metrics for ESSGW are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 5-5: Area Metrics for Extended Support for Science Gateways. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average ESSGW RY5 Deepen/Extend impact rating RY4 — Deepen use to existing RY3 communities 4 of 5 RY2 4.25 4.00 (§3.1.1) /yr 4 of 5 RY1 * 4.00 4.70 4.50 4.54 /qtr Average RY5 Deepen/Extend satisfaction with RY4 — Deepen use to ESSGW support existing RY3 communities 4.5 of 5 RY2 4.50 4.80 (§3.1.1) /yr 4.5 of 5 RY1 * 5.00 4.70 4.88 4.75 /qtr Number of RY5 Deepen/Extend completed RY4 — Deepen use to ESSGW projects existing RY3 communities RY2 10/yr 2 3 (§3.1.1) RY1 10/yr * 4 3 7 14 Average RY5 Deepen/Extend estimated RY4 — Deepen use to months saved RY3 existing due to ESSGW 12 mo/ communities RY2 4.00 11.40 support1 project (§3.1.1) RY1 * * * * * * 1New metric for PY7. ESSGW is currently on pace to meet its yearly targets for impact, satisfaction, and number of completed projects. ESSGW’s ability to meet the new yearly target for months saved due to support is more uncertain. The ESSGW L3 Manager is reminding all consultants about this new metric and we will continue to monitor progress. 5.6. Extended Support for Education, Outreach, & Training (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.
RY2 IPR 4 Page 40 Area metrics for ESTEO are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 5-6: Area Metrics for Extended Support for Education, Outreach, & Training. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of Campus RY5 Deepen/Extend — Champions Fellows RY4 Prepare the current and next generation RY3 (§3.1.3) RY2 4/yr - 4 RY1 4/yr * 5 - - 5 Average score of RY5 Deepen/Extend — Fellows RY4 Prepare the current assessment and next generation RY3 (§3.1.3) 4.5 of 5 RY2 - 4.50 /yr 4 of 5 RY1 * 4.33 - - 4.33 /yr Number of live RY5 Deepen/Extend — training events RY4 Prepare the current staffed and next generation RY3 (§3.1.3) RY2 20/yr 15 8 RY1 20/yr * 7 9 10 26 Number of staff RY5 Deepen/Extend — training events RY4 Prepare the current and next generation RY3 (§3.1.3) RY2 2/yr 2 0 RY1 2/yr * 0 0 4 4 Attendees at staff RY5 Deepen/Extend – training events RY4 Prepare the current and next generation RY3 (§3.1.3) RY2 40/yr 39 0 RY1 40/yr * 0 0 51 51 Attendees at ECSS RY5 Deepen/Extend – Symposia RY4 Prepare the current and next generation RY3 (§3.1.3) RY2 300/yr 48 92 RY1 300/yr * 78 83 75 236 - Data reported annually. We are on track to meet our targets for this reporting year. The survey for the 2016-17 cohort of Campus Champion Fellows is in progress, with the preliminary satisfaction number being reported based on 2 (out of 5 possible) responses to date. We will update the number next reporting period with the full results. Live training is on track and we are evaluating possibilities for staff training to maximize benefit for ECSS project staff.
RY2 IPR 4 Page 41 6. XSEDE Cyberinfrastructure Integration (WBS 2.3) The mission of XSEDE Cyberinfrastructure Integration (XCI) is to facilitate interaction, sharing, 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, straightforward to understand, and straightforward to 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 community, and which 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 community will be based on those already in use and services will be added that address emerging needs including data and computational services. 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 terms of 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 US. 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. 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. To access the CSR please visit: https://software.xsede.org/xcsr/xsede-use-cases. Area metrics for XSEDE Cyberinfrastructure Integration are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
RY2 IPR 4 Page 42 Table 6-1: Area Metrics for XSEDE Cyberinfrastructure Integration (XCI). Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average RY5 Advance — Create satisfaction rating RY4 an open and of XCI services evolving e- RY3 infrastructure 4 of 5 RY2 4.25 4.50 (§3.2.1) /yr 4 of 5 RY1 4.80 4.801 4.50 4.50 /yr * Number of new RY5 Advance — Create capabilities made RY4 an open and available for RY3 evolving e- production RY2 7/yr 1 2 infrastructure deployment RY1 7/yr * 0 0 6 6 (§3.2.1) Total number of RY5 Advance — Create systems that use RY4 an open and one or more CRI evolving e- provided toolkits RY3 infrastructure 700 by (§3.2.1) RY2 the end 628 643 of RY2 RY1 450/yr * 512 563 594 594 Percentage of RY5 Advance — Create XSEDE RY4 an open and recommended RY3 evolving e- tools that are 100%/ infrastructure RY2 * 100 adopted by yr (§3.2.1) allocated systems (all Level 1 and Level 2 SPs that are 100%/ allocated by XSEDE RY1 * 100 100 100 100 in whole or in part) yr where the tools are appropriate 1 This number is for RACD only as XCRI satisfaction numbers are unavailable for this reporting period. We have met metric targets for this period and are on track to meet our annual metric targets. XCI Highlights This reporting period, XCI delivered the document “User authentication service for XSEDE science gateways,” which explains how science gateways can leverage XSEDE's public authentication service (based on Globus Auth) to simplify their user registration and login processes, accept campus and/or XSEDE credentials for gateway login, provide seamless integration with XSEDE resources and services, and help track users across science gateways and XSEDE services. The methods described in the document were highlighted in a tutorial at the Gateways 2017 conference, "User Management with Globus Auth." 6.1. XCI Director’s Office (WBS 2.3.1) The XCI Director’s Office has been established to provide the 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, and retarget effort if necessary. The Director’s Office also attends and supports the preparation of project level reviews and activities.
RY2 IPR 4 Page 43 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 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. 6.2. 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. Area metrics for RACD are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 6-2: Area Metrics for Requirements Analysis & Capability Delivery. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of RY5 Advance — Create capability delivery RY4 an open and plans (CDPs) RY3 evolving e- prepared for UREP RY2 12/yr 3 0 infrastructure prioritization RY1 * * * * * * (§3.2.1) Number of CI RY5 Advance — Create integration RY4 an open and assistance evolving e- engagements RY3 infrastructure RY2 6/yr 4 4 (§3.2.1) RY1 6/yr * 4 1 0 5 Average time from RY5 Advance — Create support request to RY4 an open and solution1 RY3 evolving e- <30 infrastructure RY2 4 8 days (§3.2.1) <45 RY1 * 7 16 4 9 days/yr Number of RY5 Advance — Create significant fixes RY4 an open and and enhancements RY3 evolving e- to production RY2 16/yr 14 19 infrastructure components RY1 * * * * * * (§3.2.1) Number of new RY5 Advance — Create components RY4 an open and instrumented and RY3 evolving e- tracked for usage RY2 4/yr 0 0 infrastructure and ROI analysis RY1 * * * * * * (§3.2.1)
RY2 IPR 4 Page 44 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average RY5 Advance — Create satisfaction rating RY4 an open and of RACD services RY3 evolving e- 4 of 5 infrastructure RY2 3.75 4.3 /yr (§3.2.1) RY1 * * * * * * 1 This metric is reported in each reporting period, although the target is annual. The annual figure is the average over all reporting periods. Although two metrics are below the yearly target, we believe we are on track to meet the target for the whole year. This next reporting period, we will prepare a significant number of capability delivery plans for the User Requirement Evaluation and Prioritization group (UREP) to prioritize which should bring us close to the target for the “Number of capability delivery plans (CDPs) prepared for UREP prioritization” metric. This next reporting period, we will also launch a software provider outreach initiative, which we anticipate will result in more integration engagements later this year. For “Number of new components instrumented and tracked for usage and ROI analysis,” we started to implement usage tracking for two components and plan to implement five components by the end of the project year. 6.3. XSEDE 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. Area metrics for CRI are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 6-3: Area Metrics for XSEDE Cyberinfrastructure Resource Integration. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Total number of RY5 Advance — Create systems that use RY4 an open and one or more CRI evolving e- provided RY3 infrastructure toolkits RY2 700/yr 628 643 (§3.2.1) RY1 450/yr * 512 563 594 594 User satisfaction RY5 Advance — Create with CRI RY4 an open and services RY3 evolving e- RY2 4 of 5 /yr 4.31 5.00 infrastructure RY1 4 of 5 /yr * * 5.00 NA 5.00 (§3.2.1) Number of RY5 Advance — Create repository RY4 an open and subscribers to RY3 evolving e- CRI cluster and RY2 150/yr 102 103 infrastructure laptop toolkits RY1 150/yr * 91 4 4 99 (§3.2.1) Aggregate RY5 Advance — Create number of RY4 an open and TeraFLOPS of RY3 evolving e-
RY2 IPR 4 Page 45 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported cluster systems +10 +10 infrastructure Additional using CRI (742 (752 (§3.2.1) RY2 200/ toolkits aggre- aggre- yr gate) gate) 1,000/ RY1 length of * 5321 5321 732 732 project Number of RY5 Advance — Create partnership RY4 an open and interactions RY3 evolving e- between XCRI RY2 12/yr 10 5 infrastructure and SPs, (§3.2.1) national CI organizations, RY1 12/yr * 51 71 10 22 and campus CI providers Toolkit updates RY5 Advance — Create RY4 an open and RY3 evolving e- RY2 4/yr 12 5 infrastructure RY1 4/yr * 2 1 6 9 (§3.2.1) New Toolkits RY5 Advance — Create released RY4 an open and RY3 evolving e- RY2 2/yr 0 1 infrastructure RY1 2/yr * * 1 0 1 (§3.2.1) 1 Data for this metric was previously underreported. Most CRI metrics are meeting or exceeding targets. For those metric areas that are not (“Aggregate number of Teraflops of cluster systems using CRI toolkits” and “Number of repository subscribers to CRI cluster and laptop toolkits”), we continue to pursue outreach in the activities that drive these metrics—recruiting campuses for adoption and publicizing the benefits of subscribing to the XSEDE National Integration Toolkit (XNIT) for scientific software. Furthermore, we have worked to include the improvements most requested by campus CI providers into the XSEDE Compatible Basic Cluster (XCBC), including the Slurm Workload manager and OpenHPC Provisioning framework. In addition, CRI has completed work on the Jetstream system that allows users to instantiate clusters of their own, including as backends to science gateway systems. The SEAGRID science gateway is making use of such a system and plans to go into production in the next quarter. During this reporting period, CRI has also incorporated, and made transparent, processes for submitting and accepting software based on the processes in RACD, incorporating user input to assist the process.
RY2 IPR 4 Page 46 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 (§7.2), Data Transfer Services (§7.3), XSEDE Operations Center (XOC) (§7.4), and Systems Operational Support (§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. Area metrics for Operations are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 7-1: Area Metrics for Operations.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average RY5 Sustain — composite RY4 Provide availability of reliable, core services RY3 efficient, and (geometric RY2 99.9%/qtr 99.8 99.9 secure mean of infrastructure critical (§3.3.1) RY1 99%/qtr * 99.9 99.9 99.9 99.9 services and XRAS) Hours of RY5 Sustain — downtime RY4 Provide with direct reliable, user impacts RY3 efficient, and from XSEDE RY2 0/qtr 0 0 secure security infrastructure RY1 <24/qtr * 0 146 0 146 incidents (§3.3.1) Mean time to RY5 Sustain — ticket RY4 Provide resolution by excellent user XOC and WBS RY3 support ticket queues RY2 <24/qtr 26.0 20.1 (§3.3.2) (hrs) RY1 <24/qtr * 24.0 28.2 23.1 25.1 All metric targets were met this reporting period. See note in §7.4 below regarding ticket resolution times. Operations Highlights Each year the security team updates the Information Security Training program before it is delivered as part of the new user training program at the annual Practice & Experience in Advanced Research Computing (PEARC) conference. An online training module was also created with a self-exam that is available on demand 24/7. This program will be communicated to XSEDE staff, to the gateways team, to new users creating XSEDE accounts, and referenced in the XSEDE Acceptable Use Policy (AUP). Administration of the XSEDE Kerberos server transitioned to the security team this reporting period. Resulting from the audit of the service that was conducted in July, the Kerberos server was updated to the latest release, new hardware was provisioned, and backups that were not
RY2 IPR 4 Page 47 useful and taking significant disk space were removed. Replication and sync problems were corrected in early September and the decommissioning of weak keys was confirmed. This work greatly improves the security and accuracy of the Kerberos server. With a strategy of collecting best practices and advancing understanding throughout the community of how best to design and implement storage and network systems for end-to-end data movement, the Data Transfer Services (DTS) team drafted a plan for a long-term goal of improving the overall end user (i.e., researcher) experience of data transfers among sites. The DTS team has begun prototyping methods of data transfer log analysis (e.g., GridFTP and flow data) that will provide new insights into common data transfer scenarios amongst XSEDE resources. SysOps implemented a new central logging service in conjunction with the Cybersecurity group. This single point of data collection enables Operations to run audits more effectively and to compute data analytics in real-time. SysOps also fine-tuned the scripts that produce time-to- resolution help ticket data, which provides more accurate results. 7.1. 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, and retarget 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. 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 & annual meetings. Lastly, the Director’s Office will advise the XSEDE PI on many issues, especially those relevant to this WBS area. 7.2. Cybersecurity (WBS 2.4.2) The Cybersecurity Security (Ops-Sec) 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 Area metric for Cybersecurity is listed in the table below. Additional information about this metric can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 7-2: Area Metrics for Cybersecurity.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Hours of RY5 Sustain — downtime with RY4 Provide direct user reliable, impacts from RY3 efficient, and XSEDE security RY2 0/qtr 0 0 secure incidents RY1 <24/qtr * 0 146 0 146 infrastructure (§3.3.1)
RY2 IPR 4 Page 48 The target was met this reporting period. 7.3. 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. The Area metric for DTS is listed in the table below. Additional information about this metric can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 7-3: Area Metrics for Data Transfer Services.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Performance RY5 Sustain — (Gbps) of RY4 Provide instrumented, reliable, intra-XSEDE RY3 efficient, and 1.5 Gbps transfers > RY2 1.8 3.7 secure 1GB /qtr infrastructure RY1 1 Gbps/qtr * 1.6 1.8 1.2 1.5 (§3.3.1) The target was met this reporting period. The huge performance increase this period was due primarily to very large file transfers to the new Stampede2 GridFTP servers as well as a greater number of 40-Gig servers now in the GridFTP infrastructure. 7.4. 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. Area metrics for the XOC are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 7-4: Area Metrics for XSEDE Operations Center.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Mean time to RY5 Sustain — resolution in RY4 Provide XOC ticket excellent user queue RY3 support RY2 <4h/qtr 4.40 0.93 (§3.3.2) RY1 <24h/qtr * 4.20 5.90 3.70 4.60 User RY5 Sustain — satisfaction RY4 Provide with tickets excellent user closed by the RY3 support XOC RY2 4.5 of 5/qtr 4.5 4.8 (§3.3.2) RY1 4 of 5/qtr * 4.8 4.2 5.0 4.7 The target for mean time to resolution was met. The disparity from RP1 to RP2 reflects improvements in the scripts used to extract time-to-resolution data from the help ticket system. The RP2 value is now more accurate. The new scripts will be applied retroactively to the tickets
RY2 IPR 4 Page 49 from the previous reporting periods, and we will provide updated resolution times in the next report. User satisfaction was very high again for the reporting period and the target was met. 7.5. System Operations Support (WBS 2.4.5) Systems Operational Support (SysOps) provides enterprise level support and system administration for all XSEDE central services. The Area metric for SysOps is listed in the table below. Additional information about this metric can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 7-5: Area Metrics for System Operations Support.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average RY5 Sustain — availability of RY4 Provide critical reliable, enterprise RY3 efficient, and 99.9% services (%) RY2 99.9 99.9 secure [geometric /qtr infrastructure mean] RY1 99%/qtr * 99.9 99.9 99.9 99.9 (§3.3.1) The target was met this reporting period.
RY2 IPR 4 Page 50 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. Area metrics for the Resource Allocation Service are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 8-1: Area Metrics for Resource Allocation Service.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Average user RY5 Sustain — satisfaction RY4 Provide rating with excellent user allocations and RY3 support 4 of 5 other support RY2 4.08 4.00 (§3.3.2) services /qtr 4 of 5 RY1 * 3.98 4.03 4.03 4.01 /qtr Availability of RY5 Sustain — XRAS (%) RY4 Provide reliable, RY3 efficient, and 99.9% RY2 99.70 99.80 secure /qtr infrastructure 99% RY1 * 99.90 99.80 99.90 99.87 (§3.3.1) /qtr The average user satisfaction rating with allocations and other support services met the target again this quarter. Several outages early in the quarter led to the overall availability of XRAS being just 0.1% shy of meeting the target. RAS Highlights The Allocation Procedures team conducted the September XRAC meeting, to which 220 requests were submitted with 462 individual reviews. Nearly 800 user requests associated with non- XRAC allocations were also processed. The user satisfaction with the allocations process remained high (3.99 out of 5), despite continued reductions to XRAC-recommended allocation awards due to lack of available resources. The Allocations, Accounting, and Account Management (A3M) team improved the XRAS administration interface to give the administrators the ability to edit a request and subsequent actions (avoiding the delays associated with submitting tickets or impersonating users to fix data entry errors) as well as providing interfaces for editing various notifications that are sent via the XRAS system. The allocations policy committee recommended that XRAS be extended to allow for custom award periods on XSEDE requests, specifically for educational allocations. This feature was included in the XRAS system during this reporting period. In order to improve our security posture, XSEDE has implemented a process by which users are regularly verified. Near the end of each award period, the PI of the project is presented with a
RY2 IPR 4 Page 51 list of all current users and they will have to mark users they would like to keep on the allocation. Only those that are marked as verified will remain on the project with the renewal. A3M participated in this activity. 8.1. 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 Provider 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. 8.2. 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. Area metrics for Allocations Process & Policies are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 8-2: Area Metrics for XSEDE Allocations Process & Policies.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported User RY5 Sustain — satisfaction RY4 Provide with excellent user allocations RY3 support 4 of 5 process RY2 4.14 3.99 (§3.3.2) /qtr 4 of 5 RY1 * 3.97 4.02 4.02 4.00 /qtr Average time RY5 Sustain — to process RY4 Provide Startup excellent user requests RY3 support 14 (§3.3.2) calendar RY2 days or 11.6 10.1 less /qtr 14 calendar RY1 days or * 10.7 11.0 10.4 10.7 less /qtr
RY2 IPR 4 Page 52 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Percentage of RY5 Sustain — XRAC- RY4 Provide recommended excellent user SUs allocated RY3 support 100% RY2 60 69 (§3.3.2) /qtr 100% RY1 * 62 49 56 56 /qtr Percentage of RY5 Sustain — research RY4 Provide requests RY3 excellent user successful (not RY2 85%/qtr 70 69 support rejected) RY1 85%/qtr * 76 75 74 75 (§3.3.2) User satisfaction with the allocations process dropped slightly to just below the target, scoring 3.99. Part of the decline can be linked to the survey being issued after XRAC award notifications had been sent. However, the change in timing affected only the satisfaction with the outcomes (3.94); other aspects of the process continued to show high satisfaction. The percentage of XRAC-recommended SUs allocated remained below the idealized target. This metric is not wholly within the ability of RAS to control, but the data collected by RAS provides insight to XSEDE, NSF, and other stakeholders about the challenges faced by the ecosystem as a whole. This metric also underscores the issues faced by RAS in maintaining satisfaction with the allocations process. The percentage of research requests successful (not rejected) continues to be consistent, with an average of 73% success for the previous three reporting periods. The rate dropped slightly in the current period, but we expect it to improve going forward. RAS continues to work with the community to increase this percentage in several ways—improved documentation, training, development of a simplified resource selection interface, document templates, surveys/feedback, and continuous communication with the user community. 8.3. Allocations, Accounting, & Account Management CI (WBS 2.5.3) The Allocations, Accounting & 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. Area metrics for A3M are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 8-3: Area Metrics for Allocations, Accounting, & Account Management CI.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported User RY5 Sustain — satisfaction RY4 Provide with XRAS excellent user system RY3 support 4 of 5 RY2 4.03 4.00 (§3.3.2) /qtr 4 of 5 RY1 * 3.98 4.03 4.03 4.01 /qtr
RY2 IPR 4 Page 53 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Availability of RY5 Sustain — the XRAS RY4 Provide systems reliable, RY3 efficient, and 99.9% RY2 99.7 99.8 secure /qtr infrastructure 99% RY1 * 99.9 99.8 99.9 99.87 (§3.3.1) /qtr Percentage of RY5 Sustain — JIRA tasks RY4 Provide assigned to reliable, sprints that are RY3 efficient, and 1 95% 98.5 91.2 completed RY2 secure /qtr (66/67) (52/57) infrastructure 95% 100 90 98 RY1 * 96 (§3.3.1) /qtr (7/7) (63/70) (47/48) 1 Prior to RY1 RP4, this metric was titled "Percentage of approved feature change requests implemented." The title was changed as a more intuitive summary of the RAS and A3M team's success and improvements in estimating, planning, and completing work. The target was also lowered from 100% to 95% as a reflection of the team's potential inability to complete tasks that are assigned at the very end of the quarter but cannot be completed within that reporting period. Satisfaction remains high with the XRAS system. Despite several outages early in the quarter, the overall availability of XRAS was also high this quarter. The percentage of tasks assigned to a sprint fell this quarter due to overly optimistic development estimates and staff system difficulties. In the upcoming reporting period, we are reevaluating the method for determining estimates to better predict the number of tasks that can be completed in the sprint. The Earth Observing Laboratory at the National Center for Atmospheric Research has become the fourth client of the XRAS System. Discussions have begun with the University of Wyoming as well as Compute Canada as potential clients of XRAS.
RY2 IPR 4 Page 54 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) (§9.5) 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. As with other areas of the project, the Program Office has established Area metrics to track performance against attaining the project’s strategic goals. Area metrics for the Program Office are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 9-1: Area Metrics for Program Office. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of Social RY5 Deepen/ Media RY4 Extend — impressions Raise RY3 awareness 300,000 RY2 69,607 55,506 of the value /yr of advanced digital 190,000 RY1 52,500 128,675 81,332 262,207 services /yr * (§3.1.4) Number of media RY5 Deepen/ hits RY4 Extend — Raise RY3 awareness RY2 169/yr 42 30 of the value of advanced digital RY1 147/yr * 32 30 18 80 services (§3.1.4) Percentage of RY5 Sustain — recommendations RY4 Operate an addressed by effective and relevant project RY3 productive areas RY2 90%/yr 23 15 virtual organization RY1 90%/yr * NA1 100 57 67 (§3.3.3) Number of key RY5 Sustain — improvements RY4 Operate an addressed from innovative systematic RY3 virtual evaluation2 RY2 10/yr NA4 NA4 organization RY1 * * * * 19 19 (§3.3.4) Number of key RY5 Sustain — improvements RY4 Operate an addressed from innovative external sources2 RY3 virtual RY2 10/yr 2 6
RY2 IPR 4 Page 55 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported organization RY1 * * * * 11 11 (§3.3.4) Ratio of proactive RY5 Sustain — to reactive RY4 Operate an improvements innovative RY3 virtual RY2 4:1/qtr 1:1 1:2 organization RY1 3:1/qtr * 2:13 7:1 8:9 17:11 (§3.3.4) Number of staff RY5 Sustain — publications RY4 Operate an innovative RY3 virtual RY2 20/yr 2 6 organization RY1 70/yr * 5 0 13 18 (§3.3.4) 1 Data will be available in RY1 RP3. 2 This metric is one of two new metrics that resulted from splitting a former metric, called "Number of strategic or innovative improvements," into two. The former metric had a target of 9/yr and results were 3 for RP2 and 8 for RP3 in RY1. These two new metrics each have a target of 10/yr and will be reported annually. 3 This metric was reported incorrectly in IPR1. It has been corrected for this report. 4 L2 Directors will be responding to climate study recommendations prior to the IPR for RY2 RP3. External Relations saw a slight decrease in the run-rate to meet area metrics this quarter related to social media impressions and media hits. This is attributed to the preparation for the annual Supercomputing Conference. See §9.2 for details. Business Operations triggered a risk related to the staffing necessary to process sub-award amendments and invoices. Although sub-award amendments and invoices are being processed, the cycle time is longer than expected. See §9.4 for details. Strategic Planning and Evaluation continues to discuss the collection issues we are experiencing for the recommendations addressed and improvements metrics. Measures and/or collection methodologies are being addressed during the XSEDE-wide Metrics Review activities. See §9.5 for more details on the individual metrics. Program Office Highlights External Relations experienced significant staffing changes during this quarter. The staffing changes were planned and resulted from community media trends and the need to streamline the team’s workflow. See §9.2 for more details. In addition to the science stories featured in the media, ER placed effort in producing a new XSEDE science impact video highlighting XSEDE’s role in enabling the ArticDEM project. The video, which will be promoted at the SC17 conference, can be viewed here: https://youtu.be/xpJKFrFraQE. Twelve of the eighteen sub-award funding amendments for the first half of PY7 have been fully executed during this reporting period. The remaining amendments are expected to be fully executed in the month of November. Preliminary information for PY6 actual spending will be reviewed by the SMT on November 13th, 2017. The XSEDE-wide Metrics Review activities are progressing on schedule. The results from the third and final phase and proposed XSEDE KPI set will be presented and discussed during the December XSEDE quarterly meeting. The SMT-approved KPI set will be presented during the January 2018 mid-year project review. More than sixty percent of the PY7 Project Improvement Funds (PIF) have been allocated with the remaining PY7 PIF funds expected to be allocated by the end of the 2017 calendar year.
RY2 IPR 4 Page 56 9.1. 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. 9.2. 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. Area metrics for ER are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 9-2: Area Metrics for External Relations. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Number of RY5 Deepen/ Social Media RY4 Extend — impressions Raise RY3 awareness of 300,000 RY2 69,607 55,506 the value of /yr advanced 190,000 RY1 * 52,200 128,675 81,332 262,207 digital services /yr (§3.1.4) Number of RY5 Deepen/ media hits RY4 Extend — Raise RY3 awareness of RY2 169/yr 42 30 the value of advanced RY1 147/yr * 32 30 18 80 digital services (§3.1.4) Number of RY5 Deepen/ science Extend — success RY4 Raise stories and awareness of RY3 announce- the value of ments RY2 71/yr 18 12 advanced appearing in digital services media outlets RY1 62/yr * 14 6 14 34 (§3.1.4) Monthly RY5 Deepen/ open and RY4 Extend — click-through Raise RY3
RY2 IPR 4 Page 57 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported rates of Open: Open: Open: awareness of XSEDE’s 34%/qtr 34 34.2 the value of newsletter RY2 Click- Click- Click- advanced through: through: through: digital services 12%/qtr 21 11.2 (§3.1.4) Open: Open: Open: Open: Open: 35%/qtr 34.4 35.7 35 35.03 RY1 Click- * Click- Click- Click- Click- through: through: through: through: through: 20%/qtr 10.9 10.3 7 9.4 Overall, there was a slight decrease in the run-rate to meet area metrics this quarter. External Relations efforts were largely focused on organizing an effective presence for the annual Supercomputing Conference (SC) in order to raise awareness of XSEDE’s resources and services and to communicate the value and importance of XSEDE. A positive impact from this planning will likely be seen in Area Metrics next quarter. We are confident that the difference will be made up in the coming quarter due to an increase in media releases and an engaging social media strategy around this event. Additionally, we expect to identify a number of new science impact stories that can be used for future communications efforts while engaging with researchers at SC. Additionally, ER experienced significant staffing changes during this quarter. We eliminated the communications coordinator role and redistributed the administrative responsibilities to the ER L3 Manager and ER Project Manager. ER also welcomed a strategic content producer and a social media specialist who will help us focus on producing and distributing new strategic content. While there is a steep learning curve for this project, we believe the adjustment in skillsets will be of great value to the project going forward. 9.3. Project Management, Reporting, & Risk Management (WBS 2.6.3) The Project Management, Reporting & Risk Management (PM&R) team enables an effective virtual organization through 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. Area metrics for PM&R are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 9-3: Area Metrics for Project Management, Reporting, & Risk Management. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Variance, in days, RY5 Sustain — Operate between relevant RY4 an effective and report productive virtual submission and RY3 organization due date RY2 0/qtr 0 0 (§3.3.3) RY1 0/qtr * NA1 0 0 - Percentage of RY5 Sustain — Operate risks reviewed RY4 an effective and productive virtual RY3
RY2 IPR 4 Page 58 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported 100% organization RY2 100 100 /qtr (§3.3.3) RY1 95%/qtr * 100 97 0 - Average number RY5 Sustain — Operate of days to execute RY4 an effective and PCR productive virtual RY3 organization <30 (§3.3.3) RY2 calendar 18.5 13 days <30 RY1 calendar * 4 20 14.3 - days/qtr 1 There are no relevant report submissions in RY1RP2. IPRs are submitted in the quarter following the reporting period and thus, this variance will be reported in the subsequent quarter. The targets have been met. 9.4. Business Operations (WBS 2.6.4) The Business Operations 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. Area metrics for Business Operations are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 9-4: Area Metrics for Business Operations3.
Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Percentage of RY5 Sustain — sub-award RY4 Operate an amendments RY3 effective and processed 95% productive within target processed virtual duration2 RY2 within 40 100 90.9 organization calendar (§3.3.3) days/qtr 90% processed RY1 within 40 * NA1 100 - 100 calendar days/qtr Percentage of RY5 Sustain — sub-award RY4 Operate an invoices effective and processed RY3 productive within target 95% virtual duration2 processed organization RY2 within 42 100 80.85 (§3.3.3) calendar days/qtr
RY2 IPR 4 Page 59 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported 90% processed RY1 within 42 * NA1 100 96 98 calendar days/qtr 1 Sub-award institutions do not have XSEDE2 contracts in place yet, so no amendments or invoices can be charged against the grant yet. 2 Metrics were changed from a number of days to process sub-award amendments/sub-award invoices to be a percentage processed within a range of days. Target duration ranges of < 41 and < 45 calendar days, respectively, were also modified. This change took effect RY1 RP3. Amendment processing has been impacted by the need to process two funding amendments for PY7 due to the limited PY7 spending approval by the NSF. This created confusion with some of the XSEDE sub-awards as to the documentation needed to process the first amendment. The upcoming PY7 funding amendments are expected to be processed smoothly and meet or exceed the target cycle time. Invoice processing cycle time has been negatively impacted by new staff members in two of the three UIUC departments involved. A risk for this situation exists in the XSEDE risk register and has been triggered to execute the contingency activities. Additional staff was added to the NCSA Business Office to complete the backlog of activities and restore the productivity to the expected level. To improve communicating the status of invoice processing, we have implemented a near real-time feed of invoice processing to the individual sub-award wiki pages. 9.5. 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 will be 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. Area metrics for Strategy, Planning, Policy, Evaluation & Organizational Improvement (SP&E) are listed in the table below. Additional information about these metrics can be found on the XSEDE KPIs & Metrics wiki page. See the Appendix for Other Metrics related to this WBS.
Table 9-5: Area Metrics Strategy, Planning, Policy, Evaluation & Organizational Improvement. Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported Percentage of RY5 Sustain — Operate recommendations RY4 an effective and addressed by productive virtual RY3 RY2 90%/qtr 23 15
RY2 IPR 4 Page 60 Report Sub-goal Area Metric Target RP1 RP2 RP3 RP4 Total Year Supported relevant project organization RY1 90%/qtr NA1 100 57 67 areas * (§3.3.3) Average rating of RY5 Advance — staff regarding RY4 Enhance the Array how well-prepared of Technical they feel to RY3 Expertise and 4 of 5 perform their jobs RY2 NA1 3.40 Support Services /yr (§3.2.2) 4 of 5 RY1 - - - - /yr * Number of key RY5 Sustain — Operate improvements RY4 an innovative addressed from virtual organization systematic RY3 (§3.3.4) evaluation2 RY2 10/yr NA4 NA4 RY1 * * * * 19 19 Number of key RY5 Sustain — Operate improvements RY4 an innovative addressed from RY3 virtual organization external sources2 RY2 10/yr 2 6 (§3.3.4) RY1 * * * * 11 11 Ratio of proactive RY5 Sustain — Operate to reactive RY4 an innovative improvements virtual organization RY3 (§3.3.4) RY2 4:1/yr 1:1 1:2 RY1 3:1/yr * 2:13 7:1 8:9 17:11 - Data reported annually. 1 L2 Directors are currently responding to climate study recommendations; data will be available in RY2 RP2. 2 This metric is one of two new metrics that resulted from splitting a former metric, called "Number of strategic or innovative improvements," into two. The former metric had a target of 9/yr and results were 3 for RP2 and 8 for RP3 in RY1. These two new metrics each have a target of 10/yr and will be reported annually. 3 This metric was reported incorrectly in IPR1. It has been corrected for this report. 4 L2 Directors will be responding to climate study recommendations prior to the IPR for RY2 RP3. The “Percentage of recommendations addressed by relevant project areas” metric continues to report low due to the time required to review, discuss, and implement changes received. After further review, it has been determined that the definition and data collection timeliness of the metric does not account for the inherent processing of the recommendations received. The SP&E team is discussing alternative measures and the possibility of adjusting the metric definition and data collection to more accurately reflect the recommendations addressed. Staff sentiment regarding job preparedness was under target. The L2 Directors and L3 Managers have been provided their group’s rating for this metric and will be required to provide a response and plan to address this during the December quarterly meeting. The “Number of key improvements addressed from systematic evaluation” metric will be addressed during the December quarterly meeting as part of the L2 functional area updates. The “Number of key improvements addressed from external sources” are projected to meet or exceed the PY7 target. The “Ratio of proactive to reactive improvements” metric is lower than expected. We believe this is due to the manual entry needed from all XSEDE leaders to record all improvements made. This leaves room for possible inaccuracies. SP&E are discussing alternative measures and data collection possibilities.
RY2 IPR 4 Page 61 10. Financial Information The XSEDE Business Operations team (WBS 2.6.4) tracks and manages the financial aspect of the XSEDE project. This section conveys the financial status at a project level. The focus is spending against the approved budget. Note that closing out any given reporting period month could take up to nine months after the reporting period month 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. Business Operations is preparing expectations for timely submissions to the sub-award contracts through the set of amendments required for PY7. 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 RY1 RP3: Nov '16 – Jan '17 57 of 57 $5,256,230 $4,225,397 RY1 RP4: Feb '17 – Apr '17 47 of 57 $5,256,230 $4,413,634 RY2 RP1: May '17 – Jul '17 23 of 57 $5,256,230 $5,471,987 $310,375 RY2 RP2: Aug '17 – Oct '17 15 of 57 $5,460,061 $2,399,312 $1,975,520 RY2 RP3: Nov '17 – Jan '18 RY2 RP4: Feb '18 – April '18 RY3 RP1: May '18 – Jul '18 RY3 RP2: Aug '18 – Oct '18 RY3 RP3: Nov '18 – Jan '19 RY3 RP4: Feb '19 – Apr '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 RY5 RP1: May '20 – Jul '20 RY5 RP2: Aug '20 – Oct '20 RY5 RP3: Nov '20 – Jan '21 RY5 RP4: Feb '21 – Apr '21 RY5 Carryover: May '21 – Aug '21
RY2 IPR 4 Page 62 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 Invoices Partner Invoices Budgeted Spent Paid (Of Budgeted Spent Institution Paid (of 2) 3) NCSA 2 $733,278 $179,526.51 3 $1,099,917 $623,728 TACC 2 $550,304 $451,030.61 3 $825,457 $676,546 PSC/MPC 2 $532,169 $487,486.02 3 $789,253 $738,316 SDSC/UCSD 2 $463,680 $471,303.83 3 $695,520 $681,232 NICS/UTK 2 $286,575 $263,221.69 3 $429,862 $397,643 U 2 $226,231 $188,386.45 3 $339,346 $221,649 Chicago/ANL Indiana 2 $189,172 $122,602.09 3 $283,758 $293,470 University Shodor 2 $107,262 $109,862.04 3 $160,893 $137,963 Cornell 2 $105,112 $89,469.94 3 $157,668 $124,968 University NCAR/UCAR 2 $66,215 $0.00 3 $99,323 $69,224 Purdue 2 $52,897 $54,083.70 3 $79,345 $111,537 University Georgia Tech 2 $55,357 $0.00 3 $83,035 $0.00 SURA 2 $38,333 $31,026.75 3 $57,500 $55,141 OK State 2 $33,573 $8,573.85 3 $50,361 $16,321 (OSU) Ohio State 2 $18,367 $0.00 3 $27,550 $32,197 (OSC) USC-ISI 2 $13,333 $0.00 3 $20,000 $8,853 U Oklahoma 2 $11,261 $10,577.58 3 $16,892 $15,866 (OU) U Georgia 2 $10,567 $3,309.92 3 $15,850 $4,560 U Arkansas 2 $10,467 $8,748.72 3 $15,700 $16,184 Project Level 38 of 38 $3,504,153 $2,478,939.70 57/57 $5,256,230 $4,225,397
RY1 RP4: Feb ’17 – Apr ‘17 RY2 RP1: May ’17 – Jul ‘17 Partner Invoices Invoices Budgeted Spent Budgeted Spent Institution Paid (of 3) Paid (of 3) Cornell 2 $149,563.15 $ 107,949.23 2 $149,563.15 $ 83,389.50 University Georgia Tech 0 $83,034.86 $0.00 0 $83,034.86 $0.00 Indiana 3 $291,864.87 $256,475.30 1 $291,864.87 $94,752.53 University NCAR/UCAR 3 $99,322.72 $90,254.26 2 $99,322.72 $56,403.27 NICS/UTK 3 $429,861.89 $406,401.99 0 $429,861.89 $0.00 OK State 3 $38,995.39 $14,547.47 2 $38,995.39 $8,669.92 (OSU) Ohio State 3 $27,550.11 $32,471.00 2 $27,550.11 $17,632.05 (OSC)
RY2 IPR 4 Page 63 RY1 RP4: Feb ’17 – Apr ‘17 RY2 RP1: May ’17 – Jul ‘17 PSC/MPC 3 $798,252.88 $801,637.47 1 $798,252.88 $297,649.00 Purdue 3 $89,831.21 $58,688.74 2 $89,831.21 $43,396.29 University SDSC/UCSD 3 $695,519.51 $711,326.17 2 $695,519.51 $492,265.87 Shodor 3 $160,893.03 $117,830.10 2 $160,893.03 $102,500.57 SURA 3 $57,500.13 $63,772.01 1 $57,500.13 $27,027.35 TACC 0 $825,456.60 $0.00 0 $825,456.60 $0.00 U Arkansas 3 $15,700.03 $13,123.08 0 $15,700.03 $0.00 U 3 $339,345.98 $491,477.37 2 $339,345.98 $222,135.09 Chicago/ANL U Georgia 0 $15,850.13 $0.00 0 $15,850.13 $0.00 U Oklahoma 3 $16,891.50 $15,867.50 1 $16,891.50 $5,295.36 (OU) USC-ISI 3 $19,999.98 $24,288.35 2 $19,999.98 $25,369.12 NCSA 3 $1,099,916.80 $1,207,527 1 $1,099,916.80 $986,108.61 Project Level 47 of 57 $5,255,350.74 $4,413,634 23 of 57 $5,255,350.74 $2,462,594
RY2 RP2: Aug ’17 – Oct ‘17 Invoices Partner Paid (of Budgeted Spent Projected Institution 3) NCSA 2 $1,099,917 $622,263 $549,959 TACC 0 $825,484 320,977 $267,495 PSC/MPC 0 $807,748 $258,753 $269,249 SDSC/UCSD 1 $705,543 $490,836 $235,181 NICS/UTK 1 $454,710 $129,746 $151,570 U 1 $342,262 $98,767 $114,087 Chicago/ANL Indiana 1 $268,898 $103,234 $89,633 University Shodor 1 $163,118 $86,180 $54,373 Cornell 1 $178,615 $ 39,100 $59,538 University NCAR/UCAR 1 $100,813 $26,318 $33,604 Purdue 1 $95,968 $24,619 $31,989 University Georgia Tech 0 $71,113 $69,203 $23,704 SURA 1 $58,229 $13,812 $19,410 OK State 1 $54,250 $85,728 $18,083 (OSU) Ohio State 1 $27,830 $11,570 $9,277 (OSC)
RY2 IPR 4 Page 64 RY2 RP2: Aug ’17 – Oct ‘17 USC-ISI 1 $20,300 $11,436 $6,767 U Oklahoma 0 $20,456 $0.00 $20,456 (OU) U Georgia 0 $15,888 $0.00 $15,888 U Arkansas 1 $15,771 $6,769 $5,257 Project Level 15 of 57 $5,303,913 $2,399,312 $1,975,520
RY2 IPR 4 Page 65 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: Two submissions moved directly to the “Funded” status (totaling $72,744) Four advanced to Phase 2 - Planning (totaling $459,344) Three are pending responses from the submitters regarding additional information needed to determine the disposition of the submission (totaling $130,240) One submission was withdrawn after the review team recognized the significant overlap of two submissions (totaling $84,200) Three submissions moved to “Not Funded” based on recent similar efforts and the submission requested annually recurring funding (totaling $105,340) The total PY7 PIF funds have been reduced by $76K, as per PCR #14, to fund the 2017 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 SMT will determine the allocation of the PY7 Project Improvement Funds based on the planning details provided for those submissions moved to Phase 2. The complete allocation plan is expected to be determined within the initial six months of PY7.
RY2 IPR 4 Page 66 12. Appendices 12.1. 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. CB Campus Bridging Infrastructure to make XSEDE resources appear to be proximal to the researcher’s desktop CC Campus Champion CEE Community Enhancement & Engagement co-Pi Co-Principal Investigator CRI Cyberinfrastructure Resource Integration CRM Customer Relationship Management CS&E Computational Science & Engineering 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, credential delegation services. 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
RY2 IPR 4 Page 67 HPC High Performance Computing HPCU HPC University HSM Hardware Security Models I2 Internet2 IC Industry Challenge IdM Identity Management INCA/Nagios A service monitoring tool 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 OPS-SEC Operations - Cybersecurity OSG Open Science Grid OTP One Time Password PEP Program Execution Plan perfSONAR PERFormance Service Oriented Network monitoring Architecture PI Principal Investigator 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 RESTful Representational state transfer
RY2 IPR 4 Page 68 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 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 Use Case 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 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
RY2 IPR 4 Page 69 XCBC XSEDE Compatible Basic Cluster
XCDB XSEDE Central Database The XDCDB contains 24 schemas, notably the accounting, resource repository, portal, and AMIE databases. XDCDB XSEDE Central Database XCI XSEDE Cyberinfrastructure Integration XMS XD Net Metrics Services XOC XSEDE Operation Center XRAC XSEDE Resource Allocation Committee XRAS XSEDE Resource Allocations Service 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 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 XWFS XSEDE Wide File System
12.2. 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 provides 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, Formatted: Underline, Underline color: Accent 1, Font and resource utilization. Expanded information and five-year historical trends are shown in color: Accent 1 three corresponding subsections. Deleted: Table 12-1 In Q3 2017, most XSEDE user community metrics dipped, some considerably. The number of gateway users decreased to just over 10,000. The number of gateway users, thus, once again exceeded traditional users for the quarter. In addition, the number of open HPC user accounts continued to climb; nearly 1,900 new user accounts were added to SP resources; and nearly 500 institutions were represented among the users running jobs. More details are in §12.2.1.1. Project and allocation activity held strong, with resource requests 3.1 times what was available; the XRAC recommended support for 1.4 times what was available. More details are in §12.2.1.2.
RY2 IPR 4 Page 70 XSEDE resource metrics reflected the transition from Stampede to Stampede2. Total capacity climbed to 19.1 Pflops (peak) with the retirement of Stampede and production start for Stampede2. The central accounting system showed 12 compute resources reporting activity. Altogether, SP resources delivered 38.6 billion NUs of computing, a 42% increase over the previous quarter with the completion of the Stampede transition. More details are in §12.2.1.3.
Table 12-1: Quarterly activity summary.
User Community Q4 2016 Q1 2017 Q2 2017 Q3 2017 Open user accounts 9,844 11,038 11,760 11,484 Active individuals 3,207 3,716 4,156 3,378 Gateway users 11,045 11,512 12,757 10,218 New user accounts 1,408 2,181 1,729 1,891 Active fields of science 38 39 38 37 Active institutions 415 468 525 494 Projects and Allocations NUs available at XRAC 38.7B 39.7B 43.8B 53.4B NUs requested at XRAC 135.0B 126.8B 225.5B 167.9B NUs recommended by XRAC 68.0B 68.3B 72.1B 75.6B NUs awarded at XRAC 37.82B 38.09B 43.3B 52.5B Open projects 1,904 2,266 2,021 1,747 Active projects 1,355 1,471 1,504 1,427 Active gateways 17 19 18 18 New projects 268 284 260 269 Closed projects 247 304 373 33 Resources and Usage Resources open (all types) 25 33 24 26 Total peak petaflops 16.0 16.2 15.9 19.1 Resources reporting use 12 12 11 12 Jobs reported 2.67M 1.62M 1.83M 2.40M NUs delivered 33.8B 33.9B 27.1B 38.6
12.2.1.1. User community metrics
Figure 8 shows the five-year trend in the XSEDE user community, including open user accounts, Formatted: Font: 11 pt, Not Bold, Underline total active XSEDE users, active individual accounts, active gateway users, the number of new Deleted: Figure 8 HPC user accounts, and the total number of new XUP accounts at the end of each quarter. The quarter saw an overall decline in user activity for both traditional and gateway users. The dip is likely part of normal third quarter fluctuations; Figure 2 shows dips for active user counts in Q3 of 2016, 2014, and 2013 as well, and the larger numbers in recent quarters allow for similarly larger dips. The completion of the Stampede transition may have contributed to the reduced number of traditional users. The quarter had 11,484 open accounts, with a decrease to 3,378 traditional users charging jobs. The number of active gateway users declined to 10,218. More than 8,400 gateway users were tracked via the automated XSEDE accounting process; the Galaxy gateway’s use of Jetstream represented the most users not automatically tracked.
Figure 9 shows the activity on XSEDE resources according to field of science across program Deleted: Figure 9 years, including the relative fraction of PIs, open accounts, active users, allocations, and NUs Formatted: Underline 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 25% of all PIs, more than 50% of traditional user accounts, and nearly 40% of active users. Collectively the “other” fields of science represented 8% of total quarterly usage.
RY2 IPR 4 Page 71
Figure 8: 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.
RY2 IPR 4 Page 72
Figure 9: 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 Deleted: Table 12-2 that graduate students, postdoctoral researchers, and undergraduates consistently make up Deleted: Table 12-3 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 Formatted: Underline, Underline color: Accent 1, Font EPSCoR state institutions. Institutions with Campus Champions represent a large portion of color: Accent 1 usage because this table shows all users at Campus Champion institutions, not just those on the Formatted: Underline, Underline color: Accent 1, Font champion’s project. The table also shows XSEDE’s reach into EPSCoR states, the MSI community, color: Accent 1 and countries outside the U.S.
Table 12-2: End of quarter XSEDE open user accounts by type, excluding XSEDE staff.
Category Q4 2016 Q1 2017 Q2 2017 Q3 2017 Graduate Student 4,109 4,779 5,141 5,094 Faculty 1,758 1,854 1,941 1,962 Postdoctoral 1,353 1,419 1,456 1,434 Undergraduate Student 971 1,163 1,309 1,200 University Research Staff (excluding postdocs) 665 718 742 728 High school 108 115 95 92 Others 880 988 1,075 947 TOTALS 9,844 11,036 11,759 11,457
RY2 IPR 4 Page 73 Table 12-3: Active institutions in selected categories. Institutions may be in more than one category. Category Q4 2016 Q1 2017 Q2 2017 Q3 2017 Campus Sites 94 92 92 94 Champions Users 1,764 2,041 2,188 1,761 % total NUs 63% 58% 54% 49% EPSCoR Sites 75 90 88 78 states Users 412 491 554 582 % total NUs 12% 10% 16% 15% MSIs Sites 29 47 41 43 Users 221 278 327 245 % total NUs 2% 4.8% 3.5% 4.2% International Sites 51 63 104 84 Users 61 76 140 106 % total NUs 1% 2% 2% 2% Total Sites 415 468 525 494 Users 3,207 3,716 4,156 3,378
12.2.1.2. Project and allocation metrics
Figure 10 shows the five-year trend for requests and awards at XSEDE quarterly allocation Deleted: Figure 10 meetings. The figure shows a decline for Q3 2017, consistent with prior Q3 declines in 2015 and Formatted: Underline 2016. NUs requested were 3.1x greater than NUs available, and XRAC recommendations were 1.4x more than NUs available. Some portion of the prior quarter’s dramatic spike in demand was likely due to user confusion about the shift in allocation units from Stampede (allocated in core- hours) to Stampede2 (allocated in node-hours).
Deleted: Table 12-4
Formatted: Underline, Underline color: Accent 1, Font color: Accent 1 Figure 10: Five-year allocation history, showing NUs requested, awarded, available, and recommended. Formatted: Underline, Underline color: Accent 1, Font Table 12-4 presents a summary of overall project activity, and Table 12-5 shows projects and color: Accent 1 activity in key project categories as reflected in allocation board type. Note that Science Deleted: Table 12-5
RY2 IPR 4 Page 74 Gateways may appear under any board. In Q3 2017, the number of open projects declined, likely due to the end of the Stampede system. As a special class of projects, science gateway activity is detailed in Figure 11 showing continued high levels of usage and users from these projects. Formatted: Underline
Table 12-4: Project summary metrics. Deleted: Figure 11 Project metric Q4 2016 Q1 2017 Q2 2017 Q3 2017 XRAC requests 272 199 228 220 XRAC request success 75% 74% 70% 69% XRAC new awards 68 45 51 42 Startups requested 182 208 206 207 Startups approved 179 220 199 201 Projects new 268 284 260 269 Projects closed 247 304 373 334
Table 12-5: Project activity by allocation board type.
Q4 2016 Q1 2017 Q2 2017 Q3 2017
Open Active % Open Active % Open Active % Open Active % projects projects NUs projects projects NUs projectsprojects NUs projectsprojects NUs
Campus 133 65 0.4% 143 72 0.5% 139 71 0.3% 127 67 0.4% Champions
Discretionary 3 3 0.2% 5 5 0.4% 3 4 0.1% 3 2 0.0%
Educational 97 67 0.2% 136 79 0.6% 117 93 1.7% 100 90 1.3%
Staff 20 12 0.02% 13 10 0.11% 13 11 0.1% 12 13 0.2%
Startup 907 507 4.6% 1,140 575 5.1% 948 581 5.3% 755 555 5.6%
XRAC 744 701 94.5% 829 730 93.4% 801 744 92.4% 750 700 92.5%
Totals 1,904 1,355 100% 2,266 1,471 100% 2,021 1,504 100% 1,747 1,427 100%
RY2 IPR 4 Page 75
Figure 11: 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 38.5 billion NUs in Q3 2017, rebounding 42% from the previous quarter, due to the Stampede2 system entering full production. Table 12-6 breaks out the resource Formatted: Underline, Underline color: Accent 1, Font activity according to different resource types. Figure 12 shows the total NUs delivered by color: Accent 1 XSEDE-allocated SP computing systems, as reported to the central accounting system over the Deleted: Table 12-6 past five years. Deleted: Figure 12 Formatted: Underline
RY2 IPR 4 Page 76 Table 12-6: Resource activity, by type of resource, excluding staff projects. Note: A user will be counted for each type of resource used.
Q4 2016 Q1 2017 Q2 2017 Q3 2017
High-performance Resources 6 5 6 7 computing Jobs 2,427,658 1,625,114 1,364,113 1,627,105
Users 2,799 3,129 3,605 2,823
NUs 31,281,851,715 30,397,747,172 25,272,705,363 36,242,428,406
Data-intensive Resources 3 3 2 2 computing Jobs 76,435 72,114 14,237 48,880
Users 295 257 110 117
NUs 1,813,866,137 1,887,286,492 323,006,331 229,478,260
High-throughput Resources 1 1 1 1 computing Jobs 11,253 8,490 6,711 6,439
Users 12 11 12 10
NUs 303,094,834 294,832,055 467,668,361 464,924,060
Visualization system Resources 1 1 1 1
Jobs 4,991 7,310 5,568 10,213
Users 71 71 97 83
NUs 78,496,333 98,788,398 66,368,932 144,414,055
Cloud system Resources 1 1 1 1
Jobs 150,166 612,304 436,048 664,551
Users 228 435 474 486
NUs 345,764,113 1,181,645,135 1,003,937,978 1,458,431,188
RY2 IPR 4 Page 77
Figure 12: 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 Formatted: Font: 11 pt, Not Bold, Underline, Underline adoption over the past five years. Figure 13 shows the trends in Globus data transfer activity and color: Accent 1, Font color: Accent 1 user adoption over five years. Deleted: Table 12-7 Deleted: Figure 13 Formatted: Underline, Font color: Custom Color(RGB(46,118,161))
RY2 IPR 4 Page 78 Table 12-7: Globus data transfer activity to and from XSEDE endpoints, excluding XSEDE speed page user.
Q4 2016 RY1 RP4 RY2 RP1 RY2 RP2
51 87 95 77 87
1,495 1,262 1,472 1,840 1,262 To/from XSEDE 74 119 73 47 119 endpoint 1,689 1,594 1,724 1,536 1,594
511 593 551 540 593
7 9 4 2 9
72 35 40 27 35 To/from XSEDE 30 12 8 11 12 via Globus Connect 104 162 58 65 162
365 407 356 359 407
263 240 515 631 240 To/from 599 760 1,001 369 760 XSEDE from/to Campuses 39 43 43 48 43 54 62 62 65 62
22,097 14,617 15,972 18,028 14,617
15,335 14,119 16,036 17,211 14,119 To/from Campus 118 123 120 132 123
328 369 354 389 369
RY2 IPR 4 Page 79
Figure 13: 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.
RY2 IPR 4 Page 80
12.2.2. Other Metrics 12.2.2.1. Community Engagement & Enrichment (WBS 2.1) (Gaither) RY2 Metric RP1 TRP2 RP3 RP4 Total a Number of new users of XSEDE resources r 2,000/qtr 2,305 2,813 and services (Area Metric) g e Number of sustained users of XSEDE 5,000/qtr 3,962 3,754 resources and services (Area Metric) t Number of new users from underrepresented communities using 150/qtr 251 175 XSEDE resources and services (Area Metric) Number of sustained users from underrepresented communities using XSEDE resources and services 1,500/yr 490 408 (KPI) Number of attendees in synchronous and 5,200/yr ,1305 890 asynchronous training (Area Metric) Average impact assessment of training for attendees registered through XSEDE User 4 of 5 4.29 4.34 Portal (Area Metric) Number of pageviews to the XSEDE website 80,000/qtr 63,998 NA1 (Area Metric) Number of pageviews to the XSEDE User 250,000/ 256,482 282,722 Portal (Area Metric) qtr 1 Google Analytics was not tracking correctly so we do not have numbers for this quarter. 12.2.2.1.1. Workforce Development (WBS 2.1.2) (Houchins) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of unique attendees, 1,200/yr 325 438 synchronous training (Area Metric) Number of total attendees, synchronous training 1,400/yr 921 484 (One person can take several classes) (Area Metric) Number of unique attendees, asynchronous training (Area Metric) 1,200/yr 148 170
Number of total attendees, asynchronous training (One person can take several 4,000/yr 384 406 classes) (Area Metric) Average impact assessment of training for attendees registered through XSEDE 4 of 5 4.29 4.34 User Portal (Area Metric) Number of formal degree, minor, and certificate programs added to the 3/yr 0 0 curricula (Area Metric)
RY2 IPR 4 Page 81 RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of materials contributed to 50/yr 0 10 public repository (Area Metric) Number of materials downloaded from 62,000/ 16,545 21,340 the repository (Area Metric) yr Number of computational science 40/yr 18 - modules added to courses (Area Metric) Number of students benefiting from 950/qtr 1,722 1,478 XSEDE resources and services Percentage of under-represented students benefiting from XSEDE 50% 28 27 resources and services -Data reported annually. 12.2.2.1.2. User Engagement (WBS 2.1.3) (Hempel) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Percentage of active and new PIs 100 100 100% contacted quarterly (Area Metric) (2,040) (2,026) Percentage of user requirements resolved 78 1021 100% (Area Metric) (36/44) (47/46) Number of responses to PI emails each quarter 149 152 Number of responses to each microsurvey * * Number of annual user satisfaction survey respondents interviewed ** ** Number of XSEDE-wide tickets 10 14 Number of XSEDE-wide tickets addressed 10 14 * No microsurvey during reporting period ** Discussing future of micro survey and annual user surveys with Evaluation team 1 The metric Percentage of user requirements resolved exceeds the target of 100% as there were unresolved action items in RP1 that were resolved in RP2. 12.2.2.1.3. Broadening Participation (WBS 2.1.4) (Akli) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of new users from underrepresented communities using 150/qtr 251 175 XSEDE resources and services (Area Metric) Number of sustained users from underrepresented communities using 1,500/yr 490 408 XSEDE resources and services (Area Metric) Longitudinal Assessment of Inclusion in 5% XSEDE via the Staff Climate Study (Area improve- - 4.25 Metric) ment Longitudinal Assessment of Equity in 5% XSEDE via the Staff Climate Study (Area improve- - -5 Metric) ment
RY2 IPR 4 Page 82 -Data reported annually. 12.2.2.1.4. User Interfaces & Online Information (WBS 2.1.5) (Dahan) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of new users of XSEDE 2,000/ 2,305 2,813 resources and services (Area Metric) qtr Number of sustained users of XSEDE 3,000/ 3,962 3,754
resources and services (Area Metric) qtr Number of pageviews to the XSEDE 80,000/ 63,998 NA1
website (Area Metric) qtr Number of pageviews to the XSEDE User 250,000/ 256,482 282,722
Portal (Area Metric) qtr User satisfaction with website (Area 4.17 - 4 of 5 Metric) User satisfaction with user portal (Area 4.18 - 4 of 5 Metric) User satisfaction with user 4.07 - 4 of 5 documentation (Area Metric) 1 Google Analytics was not tracking correctly so we do not have numbers for this quarter -Data reported annually. 12.2.2.1.5. Campus Engagement (WBS 2.1.6) (Neeman, Brunson) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of Institutions with a Champion 240/yr 218 238 (Area Metric) Number of unique contributors to the Champion email list 125 129 112 ([email protected]) (Area Metric) Number of activities that (i) expand the emerging CI workforce and/or (ii) improve the extant CI workforce, 40 28 30 participated in by members of the Campus Engagement team (Area Metric) 12.2.2.2. Extended Collaborative Support Services (WBS 2.2) (Wilkins-Diehr, Sanielevici) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of completed ECSS projects 50 16 9 (ESRT + ESCC + ESSGW) (KPI) Average ECSS impact rating (KPI) 4 of 5 4.11 4.03 Average satisfaction with ECSS support 4.5 of 5 4.65 4.56 (KPI) Number of new users from non- traditional disciplines of XSEDE 500/yr 101 99 resources and services (KPI) Number of sustained users from non- traditional disciplines of XSEDE 500/qtr 700 709 resources and services (KPI)
RY2 IPR 4 Page 83 RY2 Metric Target RP1 RP2 RP3 RP4 Total
Average estimated months saved due to 12 mo/ 13.61 12.23 ECSS support project 12.2.2.2.1. Extended Support for Research Teams (WBS 2.2.2) (Crosby) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of completed ESRT projects 30 10 2 (KPI) Average ESRT impact rating (KPI) 4 of 5 3.70 4.63 Average satisfaction with ESRT support 4.5 of 5 4.60 4.75 (KPI) Number of Projects Initiated 3 6 Number of Projects Discontinued 0 0 Number of PI interviews 5 6 Number of Active Projects 29 30 Average estimated months saved due to 12 mo/ ESRT support 18.3 16 project 12.2.2.2.2. Novel & Innovative Projects (WBS 2.2.3) (Blood) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of new users from non- traditional disciplines of XSEDE 500/yr 101 99 resources and services (KPI) Number of sustained users from non- traditional disciplines of XSEDE 500/qtr 700 709 resources and services (KPI) Number of new XSEDE projects from target communities generated by NIP 30 11 10 (Area Metric) Number of successful XSEDE projects from target communities mentored by 25 24 35 NIP (Area Metric)
12.2.2.2.3. Extended Support for Community Codes (WBS 2.2.4) (Cazes) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of completed ESCC projects 10 4 4 (KPI) Average ECSS impact rating (KPI) 4 of 5 5 1 Average satisfaction with ECSS support 4.5 of 5 5 2.75 (KPI) Number of projects initiated 1 3 Number of projects discontinued 0 0 Number of active projects 14 12
RY2 IPR 4 Page 84 RY2 Metric Target RP1 RP2 RP3 RP4 Total Number of PI Interviews 9 2 2 Average estimated months saved due to 12 mo/ ESCC support 11.5 3 project 12.2.2.2.4. Extended Support for Science Gateways (WBS 2.2.5) (Pierce) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of completed ESSGW projects 10 2 3 (KPI) Average ESSGW impact rating (KPI) 4 of 5 4.25 4.00 Average satisfaction with ESSGW support 4.5 of 5 4.25 4.8 (KPI) Number of projects initiated 2 0 Number of projects discontinued 0 0 Number of active projects 14 12 Number of PI Interviews 3 5 Average estimated months saved due to 12 mo/ ESSGW support 4 11.4 project 12.2.2.2.5. Extended Support for Education Outreach, & Training (WBS 2.2.6) (Alameda) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of Campus Champions fellows 4 - 4 (Area Metric) Average Score of fellows assessment 4.5 of 5 - 4.5 (Area Metric) Number of live training events staffed 20 15 8 (Area Metric) Number of staff training events (Area 2 2 0 Metric) Attendees at staff training events (Area 40 39 0 Metric)
Attendees at ECSS Symposia (Area 300 48 92 Metric)
Live training event contact hours 29.5
Requests for Service 19 20
Training Modules Reviewed 2 4 Training Modules Produced 1 5 Meetings and BoFs 10 8 Mentoring 20 11 Talks and Presentations 24 6
RY2 IPR 4 Page 85 RY2 Metric Target RP1 RP2 RP3 RP4 Total
Education Proposals reviewed 24 75 -Data reported annually. 12.2.2.3. Cyberinfrastructure Integration (WBS 2.3) (Lifka) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Average satisfaction rating of XCI 4 of 5 4.25 4.50 services (Area Metric) Number of new capabilities made available for production deployment 7 1 2 (Area Metric) Total number of systems that use one or 700 by the more CRI provided toolkits (Area 628 643 end of RY2 Metric)1 Percentage of Level 1 SPs that fully incorporate all of the recommended 100% * 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% * 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% * 100 tools from the XSEDE Community Repository Percentage of Level 3 SPs that fully incorporate all of the recommended 100% * 100 tools from the XSEDE Community Repository2 1Number of new systems added is given in each Reporting Period. Total is the sum over a Reporting Year. 2XCI is preparing a recommendation to be made to the SP Forum that Level 3 SPs participate in the RDR (https://rdr.xsede.org/). Although there are no formal requirements for Level 3 SPs other than that they list the services they offer via XSEDE, it is already the case that 57% of the Level 3 SPs are participating in the RDR voluntarily. 12.2.2.3.1. Requirements Analysis & Capability Delivery (WBS 2.3.2) (Navarro) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of capability delivery plans (CDPs) prepared for UREP prioritization 12/yr 3 0 (Area Metric) Number of CI integration assistance 6 4 4 engagements (Area Metric) Average time from support request to 30 days or 4 days 8 solution (Area Metric) less Number of new components instrumented and tracked for usage and 4/yr 0 0 ROI analysis (Area Metric)
RY2 IPR 4 Page 86 RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of significant fixes and enhancements to production 16/yr 14 19 components (Area Metric) Number of maintenance releases and upgrades delivered of service provider 4/yr 1 1 software Number of fixes and enhancements to 12/yr 13 18 centrally operated services Average satisfaction rating of RACD 4 of 5/yr 3.73 4.30 services (Area Metric) User rating of components delivered in 4 of 5/yr * 5 production Operator rating of components 4 of 5/yr * 4.1 delivered for production deployment Software/Service Provider rating of our 4 of 5/yr 3.75 * integration assistance * No data was reported for this metric in this period. 12.2.2.3.2. XSEDE Cyberinfrastructure Resource Integration (WBS 2.3.3) (Knepper) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Total number of systems that use one 700 by the or more CRI provided toolkits (Area 628 643 end of RY2 Metric)1 User satisfaction with CRI services 4 of 5/yr 4.31 5 (Area Metric) Number of repository subscribers to CRI cluster and laptop toolkits (Area 150 102 103 Metric) Aggregate number of TeraFLOPS of +10 (752 cluster systems using SCRI toolkits 200/yr 742 aggre- (Area Metric) gate) Number of partnership interactions between XCRI and SPs, national CI 12 10 8 organizations, and campus CI providers (Area Metric) Toolkit updates (Area Metric) 4/yr 12 9 New Toolkits released (Area Metric) 2/yr 0 1 1Number of new systems added is given in each Reporting Period. Total is the sum over a Reporting Year. 12.2.2.4. XSEDE Operations (WBS 2.4) (Peterson) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Average composite availability of core services (geometric mean of critical 99.9% 99.8 99.9 services and XRAS) (Area Metric) Hours of downtime with direct user impacts from an XSEDE security 0/qtr 0 0 incident. (Area Metric)
RY2 IPR 4 Page 87 Mean time to ticket resolution by XOC and WBS ticket queues (hrs) (Area <24 26 20.1 Metric) 12.2.2.4.1. Cybersecurity (WBS 2.4.2) (Slagell, Marstellar) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Hours of downtime with direct user impacts from an XSEDE security 0/qtr 0 0 incident. (Area Metric) Hours of downtime WITHOUT direct user impacts from an XSEDE (affects central service or multiple SPs) < 24 0 0 security incident. XSEDE account exposures < 10 0 0 Time, beyond 24 hours, to disable XSEDE accounts 0 NA NA 12.2.2.4.2. Data Transfer Services (WBS 2.4.3) (Boerner) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Performance (Gbps) of instrumented, intra-XSEDE transfers > 1GB (Area 1.5 Gbps 1.8 3.7 Metric) New services added 0 0 Services retired 0 0 Total Globus Online users 551 540 Total new Globus Online users 161 185 Total transfers (Million) inbound 95 77 Total transfers (Million) outbound 73 47 Size of transfers (TBs) inbound 1,472 1,840 Size of transfers (TBs) outbound 1,724 1,536 Total number of days in which any 0 Network Interface error occurred 1 XSEDEnet maximum bandwidth used 35.9 (Gbps)1 34.8
1Note: During this reporting period, Internet2 greatly decreased the interval of measurement for peak bandwidth usage reporting, which provides more accurate (and higher) values when calculating peak usage of XSEDEnet. 12.2.2.4.3. XSEDE Operations Center (WBS 2.4.4) (Pingleton) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Mean time to resolution in XOC queue <4 4.40 0.93 (Area Metric) User satisfaction with tickets closed by 4.5 of 5 4.5 4.8 the XOC (Area Metric) Mean time to resolution in WBS queue 62.9 52.2 Number of Support tickets opened for WBS queues 371 376 Number of Support tickets closed by WBS queues 316 325
RY2 IPR 4 Page 88 RY2 Metric Target RP1 RP2 RP3 RP4 Total Number of Support tickets opened for XOC 540 542 Number of Support tickets closed by XOC 540 542 Mean time to first response by XOC < 24 hrs .070 0.33 12.2.2.4.4. System Operations Support (WBS 2.4.5) (Rogers) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Average availability of critical enterprise services (%) [geometric 99.9% 99.9 99.9 mean] (Area Metric) Total enterprise services 46 46 Core enterprise services 8 8 Services added -3 1 12.2.2.5. Resource Allocation Service (WBS 2.5) (Hart) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Average user satisfaction rating for allocations and other support services 4 of 5 4.08 4.00 (KPI) Availability of XRAS (Area Metric) 99.9% 99.7 99.8 12.2.2.5.1. XSEDE Allocations Process & Policies (WBS 2.5.2.) (Hackworth) RY2 Metric Target RP1 RP2 RP3 RP4 Total
User satisfaction with allocations process 4 of 5 4.14 3.99 (Area Metric) 14 Average time to process Startup requests calendar 11.6 10.1 (Area Metric) days or less/qtr.9 Percentage of XRAC-recommended SUs 100% 60 69 allocated (Area Metric) Percentage of research requests 85% 70 69 successful (not rejected) Continuous allocation requests processed 1,6601 785
Research allocation requests processed 228 220
1 Correction to “Continuous allocation requests processed.” These numbers are inflated due to the transfer of grants from decommissioned resources (e.g., Stampede). 12.2.2.5.2. Allocations, Accounting, & Account Management CI (WBS 2.5.3) (Schuele) RY2 Metric Target RP1 RP2 RP3 RP4 Total
User satisfaction with XRAS system (Area 4 of 5 4.03 4.00 Metric) Availability of the XRAS systems (Area 99.9% 99.7 99.8 Metric)
RY2 IPR 4 Page 89 RY2 Metric Target RP1 RP2 RP3 RP4 Total
Percentage of JIRA tasks assigned to 95% 98.5 sprints that are completed Number of XRAC client organizations 3 4 12.2.2.6. Program Office (WBS 2.6) (Payne) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of Social Media impressions (Area 300,000 69,607 55,506 Metric) Number of media hits (Area Metric) 169 42 30 Percentage of recommendations addressed by relevant project areas (Area 90% 23 15 Metric) Number of strategic or innovative 9 2 6 improvements (Area Metric) Ratio of proactive to reactive 4:1/yr 1:2 1:2 improvements (Area Metric) Number of staff publications (Area Metric) 2 2 6 12.2.2.6.1. External Relations (WBS 2.6.2) (Williamson) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Number of Social Media impressions 300,000 69,607 55,506 (Area Metric) Number of media hits (Area Metric) 169 42 30 Number of science success stories and announcements appearing in media 71 18 12 outlets (Area Metric) Monthly open and click-through rates of Open: Open: Open: 34 XSEDE’s newsletter (Area Metric) 34% 34.2 Click- Click- Click- through: through: through: 21 12% 11.2 12.2.2.6.2. Project Management, Reporting, & Risk Management (WBS 2.6.3) (Gendler) RY2 Metric Target RP1 RP2 RP3 RP4 Total Variance, in days, between relevant report submission and due date (Area 0/report 0 0 Metric) Percentage of risks reviewed (Area 100 100 100% Metric) Average number of days to execute PCR <30 13 (Area Metric) calendar 18.5 days Number of total risks 148 149 Number of active risks 136 134 Number of new risks 0 1
RY2 IPR 4 Page 90 RY2 Metric Target RP1 RP2 RP3 RP4 Total Number of risks triggered 0 4 Number of risks retired 10 4 Risk States
Monitor 132 129 Execute Contingency 4 5 Retired 11 14 Inactive 1 1 Number of PCRs submitted 3 3 KPI/Metrics 1 1 Technical 0 0 Scope 0 0 Budget 1 1 Staff 1 1 Other 1 0 12.2.2.6.3. Business Operations (WBS 2.6.4) (Payne) RY2 Metric Target RP1 RP2 RP3 RP4 Total
95% processed Percentage of sub-award amendments within 40 100 85 processed within target duration calendar days 95% processed Percentage of sub-award invoices within 42 100 85 processed within target duration calendar days 12.2.2.6.4. Strategy, Planning, Policy, Evaluation & Organizational Improvement (WBS 2.6.5) (Payne) RY2 Metric Target RP1 RP2 RP3 RP4 Total
Percentage of recommendations addressed by relevant project areas (Area 90% 23 15 Metric) Average rating of staff regarding how well-prepared they feel to perform their 4 of 5 NA1 3.40 jobs (Area Metric) Number of strategic or innovative 9 2 6 improvements (Area Metric) Ratio of proactive to reactive 4:1/yr 1:1 1:2 improvements (Area Metrics) 1 L2 Directors are currently responding to climate study recommendations; data will be available in RY2 RP3.
RY2 IPR 4 Page 91 12.3. Scientific Impact Metrics (SIM) and Publications Listing This appendix presents the current Scientific Impact Metrics data as of end of September 2017. This is part of the XD Metrics Service (XMS) (previously NSF Technology Audit Service (TAS)) effort. We are currently working on adapting the data and results structure so most of these results would be available as display via XDMoD in future. 12.3.1. Summary Impact Metrics Table SIM-1 shows the essential scientific summary impact metrics as of the end of Q3 2017. 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 11,738 5,111 250,666 166 301 (TG+XD) Since 2011 9,392 3,390 131,144 110 187 (XD) Change from last quarter +442 +307 +15,457 +3 +7 (TG+XD) Change from last quarter +442 +288 +12,944 +7 +10 (XD) * Data updated as of September 30, 2017.
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.
RY2 IPR 4 Page 92
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).
12.3.3. Publications Listing
Figure 14 shows the number of publications, conference papers, and presentations reported by Formatted: Underline XSEDE users each quarter, including the 839 reported by 201 projects in Q3 2017; these Deleted: Figure 14 publications are listed below according to allocated project.
RY2 IPR 4 Page 93
Figure 14: Publications, conference papers, and presentations reported by XSEDE users. 12.3.3.1. XSEDE Staff Publications The following staff publications were reported from August-October 2017 and reported via the XSEDE User Portal user profiles. 1. TG-IRI160006 1. Coulter, E., J. Fischer, B. Hallock, R. Knepper, and C. Stewart (2016), Implementation of Simple XSEDE-Like Clusters, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949570. (published) 2. Fischer, J., E. Coulter, R. Knepper, C. Peck, and C. A. Stewart (2015), XCBC and XNIT - Tools for Cluster Implementation and Management in Research and Training, 2015 IEEE International Conference on Cluster Computing, doi:10.1109/cluster.2015.143. (published) 3. Hallock, B., R. Knepper, J. Ferguson, and C. Stewart (2014), XSEDE Campus Bridging Pilot Case Study, Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment - XSEDE ’14, doi:10.1145/2616498.2616570. (published) 4. Navarro, J. P., C. A. Stewart, R. Knepper, L. Liming, D. Lifka, and M. Dahan (2017), The Community Software Repository from XSEDE, Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17, doi:10.1145/3093338.3093373. (published) [Blacklight, Bridges Large, Bridges Regular, Comet, Data Oasis, Data Supercell, FutureGrid, Globus Online, Gordon, HPSS, Jetstream, Keenland, Lonestar, Mason, Maverick, Pylon, Ranch, Stampede, SuperMIC, Trestles, Wrangler, XStream] 2. XSEDE_STAFF_PUBLICATION 5. Nakandala, S., S. Pamidighantam, S. Yodage, N. Doshi, E. Abeysinghe, C. P. Kankanamalage, S. Marru, and M. Pierce (2016), Anatomy of the SEAGrid Science Gateway, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949591. (published)
RY2 IPR 4 Page 94 6. Pamidighantam, S., S. Nakandala, E. Abeysinghe, C. Wimalasena, S. R. Yodage, S. Marru, and M. Pierce (2016), Community Science Exemplars in SEAGrid Science Gateway: Apache Airavata Based Implementation of Advanced Infrastructure, Procedia Computer Science, 80, 1927–1939, doi:10.1016/j.procs.2016.05.535. (published) 12.3.3.2. Publications from XSEDE Users Most of the following publications were gathered from Research submissions to the August 2017 XSEDE Resource Allocations Committee (XRAC) meeting. The publications are organized by the proposal with which they were associated. This quarter, 201 projects identified 839 publications and other products that were published, in press, accepted, submitted, or in preparation. Starting with the September-October 2017 submission window, we will no longer allow XRAC renewal submissions provide a file to list publications resulting from the work conducted in the prior year; all submitters will add publications to their user profiles in the XSEDE User Portal. Also note that the final set of publications are associated with “XSEDE Allocation Request” and not a specific project; we will be working to encourage users to associate such publications with the proper XSEDE project. 1. TG-ASC040044N 1. Blaisdell, G., Lyrintzis, A., Aikends, K., Dhamankar, N. 2016. Wall-Modeled Large Eddy Simulation of Nozzle Flows for Jet Aeroacoustics Applications. Invited workshop presentation (presentation slides are available to workshop participants and other interested LES community memebers). University of Maryland. https://drive.google.com/drive/folders/0B2JlaYrFPbvKNHlfQWNXVENFS00?usp=sharing. (published) [NICS, Stampede, TACC] 2. Coderoni, M., Lyrintzis, A., Blaisdell, G. 2018. LES of unheated and heated supersonic jets with fluidic injection. AIAA Aerospace Sciences Meeting (Kissimmee, Florida). (submitted) [Stampede, TACC] 2. TG-ASC050039N 3. Acun, B., P. Miller, and L. V. Kale (2016), Variation Among Processors Under Turbo Boost in HPC Systems, Proceedings of the 2016 International Conference on Supercomputing - ICS ’16, doi:10.1145/2925426.2926289. (published) 4. Chandrasekar, K., Ni, X., Kale, L. 2017. A Memory Heterogeneity-Aware Runtime System for Bandwidth- Sensitive HPC Applications. Workshop on Emerging Parallel and Distributed Runtime Systems and Middleware at IPDPS (Orlando, Florida). 1293--1300. https://doi.org/10.1109/IPDPSW.2017.168.DOI:10.1109/IPDPSW.2017.168 (Invalid?). (published) 5. Jain, N., Bohm, E., Mikida, E., Mandal, S., Kim, M., et al. 2016. OpenAtom: Scalable Ab-Initio Molecular Dynamics with Diverse Capabilities. Springer International Publishing. 139-158. http://dx.doi.org/10.1007/978-3-319- 41321-1_8.DOI:10.1007/978-3-319-41321-1_8 (Invalid?). (published) 6. Menon, H., Wesolowski, L., Zheng, G., Jetley, P., Kale, L., et al. 2015. Adaptive techniques for clustered N-body cosmological simulations. Computational Astrophysics and Cosmology 2: 1--16. (published) 7. Ni, X., Jain, N., Chandrasekar, K., Kalé, L. 2017. Runtime Techniques for Programming with Fast and Slow Memory. IEEE Cluster 17. (published) 8. Robson, M., Buch, R., Kale, L. 2016. Runtime Coordinated Heterogeneous Tasks in Charm++. Proceedings of the Second Internationsl Workshop on Extreme Scale Programming Models and Middleware (Salt Lake City, Utah). 40--43. https://doi.org/10.1109/ESPM2.2016.7.DOI:10.1109/ESPM2.2016.7 (Invalid?). (published) 3. TG-ASC130023 9. Teodoro, G., T. M. Kurç, L. F. R. Taveira, A. C. M. A. Melo, Y. Gao, J. Kong, and J. H. Saltz (2017), Algorithm sensitivity analysis and parameter tuning for tissue image segmentation pipelines, Bioinformatics, btw749, doi:10.1093/bioinformatics/btw749. (published) 10. Willian Barreiros Jr, George Teodoro, Tahsin Kurc, Jun Kong, Alba C. M. A. Melo and Joel Saltz. Accelerating Sensitivity Analysis in Microscopy Image Segmentation Workflows with Multi-level Computation and Data Reuse. IEEE Cluster, 2017 (Accepted).
RY2 IPR 4 Page 95 4. TG-ASC140014 11. Arora, R., Chen, K., Gupta, M., Clark, S., Song, C. 2015. Leveraging DiaGrid hub for interactively generating and running parallel programs. Proceedings of the 2015 XSEDE Conference: Scientific Advancements Enabled by Enhanced Cyberinfrastructure. 44. (published) 12. Thompson, C., Clark, S., Song, X. 2016. The XSEDE BLAST Gateway: Leveraging Campus Development for the XSEDE Community. Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale. 41. (published) 5. TG-ASC140026 13. Luo, Y., Tang, X. 2017. Automated diagnosis of Alzheimer’s disease with multi-atlas based whole brain segmentations. SPIE Medical Imaging. 1013712--1013712. (published) [Gordon, SDSC, Stampede, TACC] 14. Qin, Y., Zhang, S., Jiang, R., Gao, F., Tang, X., et al. 2017. Region-specific atrophy of precentral gyrus in patients with amyotrophic lateral sclerosis. Journal of Magnetic Resonance Imaging. (published) [Gordon, SDSC, Stampede, TACC] 15. Seymour, K., Tang, X., Crocetti, D., Mostofsky, S., Miller, M., et al. 2017. Anomalous subcortical morphology in boys, but not girls, with ADHD compared to typically developing controls and correlates with emotion dysregulation. Psychiatry Research: Neuroimaging 261: 20--28. (published) [Gordon, SDSC, Stampede, TACC] 16. Tang, X., Albert, M., Miller, M., Younes, L. 2016. Change Point Estimation of the Hippocampal Volumes in Alzheimer's Disease. Computer and Robot Vision (CRV), 2016 13th Conference on. 358--361. (published) [Gordon, SDSC, Stampede, TACC] 17. Tang, X., Qin, Y., Zhu, W., Miller, M. 2017. Surface-based vertexwise analysis of morphometry and microstructural integrity for white matter tracts in diffusion tensor imaging: With application to the corpus callosum in Alzheimer's disease. Human brain mapping 38: 1875--1893. (published) [Gordon, SDSC, Stampede, TACC] 18. Tang, X., Varma, V., Miller, M., Carlson, M. 2017. Education is associated with sub-regions of the hippocampus and the amygdala vulnerable to neuropathologies of Alzheimer’s disease. Brain Structure and Function 222: 1469--1479. (published) [Gordon, SDSC, Stampede, TACC] 19. Tang, X., Wu, J. 2016. Principal component analysis of the shape deformations of the hippocampus in Alzheimer's disease. Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the. 4013--4016. (published) [Gordon, SDSC, Stampede, TACC] 20. Tward, D., Lee, B., Mitra, P., Miller, M. 2017. Performance of Image Matching in the Computational Anatomy Gateway: CPU and GPU Implementations in OpenCL. Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact. 46. (published) [Gordon, SDSC, Stampede, TACC] 21. Tward, D., Miller, M., Trouve, A., Younes, L. 2017. Parametric surface diffeomorphometry for low dimensional embeddings of dense segmentations and imagery. IEEE transactions on pattern analysis and machine intelligence 39: 1195--1208. (published) [Gordon, SDSC, Stampede, TACC] 22. Tward, D., Sicat, C., Brown, T., Bakker, A., Gallagher, M., et al. 2017. Entorhinal and transentorhinal atrophy in MCI using longitudinal diffeomorphometry. Alzheimer’s & Dementia: Diagnosis & Disease Monitoring. (published) [Gordon, SDSC, Stampede, TACC] 23. Tward, D., Sicat, C., Brown, T., Bakker, A., Miller, M. 2016. Reducing Variability in Anatomical Definitions Over Time Using Longitudinal Diffeomorphic Mapping. International Workshop on Spectral and Shape Analysis in Medical Imaging. 51--62. (published) [Gordon, SDSC, Stampede, TACC] 24. Wu, D., Ceritoglu, C., Miller, M., Mori, S. 2016. Direct estimation of patient attributes from anatomical MRI based on multi-atlas voting. NeuroImage: Clinical 12: 570--581. (published) [Gordon, SDSC, Stampede, TACC] 25. Wu, D., Faria, A., Younes, L., Mori, S., Brown, T., et al. 2017. Mapping the order and pattern of brain structural MRI changes using change-point analysis in premanifest Huntington's disease. Human Brain Mapping. (published) [Gordon, SDSC, Stampede, TACC] 6. TG-ASC150002, TG-DMR140032 26. Ouaknin, G., N. Laachi, D. Bochkov, K. Delaney, G. H. Fredrickson, and F. Gibou (2017), Functional level-set derivative for a polymer self consistent field theory Hamiltonian, Journal of Computational Physics, 345, 207– 223, doi:10.1016/j.jcp.2017.05.037. (published)
RY2 IPR 4 Page 96 7. TG-ASC150024 27. Ojika, D., D. Acosta, A. Gordon-Ross, A. Carnes, and S. Gleyzer (2017), Accelerating High-energy Physics Exploration with Deep Learning, Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17, doi:10.1145/3093338.3093340. (published) [SDSC] 8. TG-ASC150024, TG-CTS170026 28. O'Dea, M., Guessous, L. 2016. Further Developments In Numerical Simulations Of Wind Turbine Flows Using The Actuator Line Method. ASME 2016 Fluids Engineering Division Summer Meeting (Washington, DC). (published) [Comet] 29. O'Dea, M., Guessous, L. 2017. Continued Development Of An Advanced Wind Turbine Actuator Line Model. CFD Society Of Canada CFD 2017 (Windsor, Ontario, Canada). (published) [Comet] 9. TG-ASC160018, TG-MCB130125 30. Abante, J., N. Ghaffari, C. D. Johnson, and A. Datta (2017), HiMMe: using genetic patterns as a proxy for genome assembly reliability assessment, BMC Genomics, 18(1), doi:10.1186/s12864-017-3965-2. (published) 10. TG-ASC160025 31. Rodriguez, P., S. Puthanveetil, J. Will, E. Wuerffel, and A. Craig (2017), Extracting, Assimilating, and Sharing the Results of Image Analysis on the FSA/OWI Photography Collection, Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17, doi:10.1145/3093338.3093365. (published) 11. TG-ASC160065 32. Kotteda, V., Kumar, V., Spotz, W., Rodriguez, A., Schiaffino, A., et al. 2017. Linear Solver Performance Analysis of MFiX Integrated with a Next Generation Computational Framework. National Energy Technology Laboratory’s (NETL) 2017 Workshop on Multiphase Flow Science. National Energy Technology Laboratory’s (NETL) 2017 Workshop on Multiphase Flow Science. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede, TACC] 12. 33. Kotteda, V., Kumar, V., Spotz, W. 2017. Numerical analysis of preconditioned iterative methods in Trilinos for multiphase flow problems. The Journal of Supercomputing. (in preparation) [Comet, SDSC] 13. TG-AST040034N 34. Vijayaraghavan, R., and P. M. Ricker (2017), The Co-evolution of a Magnetized Intracluster Medium and Hot Galactic Coronae: Magnetic Field Amplification and Turbulence Generation, The Astrophysical Journal, 841(1), 38, doi:10.3847/1538-4357/aa6eac. (published) [Ranch, Stampede, TACC] 14. TG-AST080002N 35. Calderon, V., Berlind, A. 2017. Galaxy groups in SDSS DR7. Monthly Notices of the Royal Astronomical Society. (in preparation) [TACC] 36. Calderon, V., Berlind, A., Sinha, M. 2017. Small- and large-scale galactic conformity in SDSS DR7. Monthly Notices of the Royal Astronomical Society. (in preparation) [TACC] 37. Mao, Q. et al. (2017), A Cosmic Void Catalog of SDSS DR12 BOSS Galaxies, The Astrophysical Journal, 835(2), 161, doi:10.3847/1538-4357/835/2/161. (published) [Ranch, Stampede, TACC] 15. TG-AST080005 38. “Secondary Bias, Neighbor Bias, and Dependence of Halo Properties on Neighbor Distance" Salcedo, A., Maller, A., Berlind, A. A., Sinha, M., McBride, C. K., Behroozi, P., Wechsler, R., & Weinberg, D., 2017, in preparation 39. “Small- and Large-Scale Galactic Conformity in SDSS DR7" Calderon, V. F., Berlind, A. A., & Sinha, M., 2017, in preparation 40. “Galaxy Groups in SDSS DR7" Calderon, V. F. & Berlind, A. A., 2017, in preparation 41. “Accurate Modeling of Galaxy Clustering on Small Scales: Testing the Standard ΛCDM + Halo
RY2 IPR 4 Page 97 42. Model" Sinha, M., Berlind, A. A., McBride, C. K., Scoccimarro, R., Piscionere, J. A., & Wibking, B. D., 2017, MNRAS, submitted 43. “A Cosmic Void Catalog of SDSS DR12 BOSS Galaxies Mao, Q., Berlind, A. A., Scherrer, R. J., Neyrinck, M. C., Scoccimarro, R., Tinker, J. L., McBride, C. K., & Schneider, D. P., 2017, The Astrophysical Journal, 835, 160 44. “Cosmic Voids in the SDSS DR12 BOSS Galaxy Sample: The Alcock-Paczynski Test" Mao, Q., Berlind, A. A., Scherrer, R. J., Neyrinck, M. C., Scoccimarro, R., Tinker, J. L., McBride, C. K., Schneider, D. P., Pan, K., Bizyaev, D., Malanushenko, E., & Malanushenko, V., 2017, The Astrophysical Journal, 835, 161 45. “The Spatial Distribution of Satellite Galaxies Within Halos: Measuring the Very Small Scale Angular Clustering of SDSS Galaxies" Piscionere, J. A., Berlind, A. A., McBride, C. K., Scoccimarro, R., 2015, The Astrophysical Journal, 806, 125 46. “The best fit for the observed galaxy counts-in-cell distribution function" Hurtado-Gil, Llus et al., 2017, Astronomy & Astrophysics, 601, 40 47. “Joint constraints on galaxy bias and ?8 through the N-pdf of the galaxy number density" Arnalte-Mur, P., et al., 2016, Journal of Cosmology and Astroparticle Physics, 3, 5 48. “The importance of the cosmic web and halo substructure for power spectra" Pace, F., et al., 2015, Monthly Notices of the Royal Astronomical Society, 454, 708 49. “Computing the three-point correlation function of galaxies in O(N2) time" Slepian, Z. & Eisenstein, D. J., 2015, Monthly Notices of the Royal Astronomical Society, 454, 4142 16. TG-AST090040, TG-MCA98T020 50. Egan, H., O’Shea, B., Hallman, E., Burns, J., Xu, H., et al. 2016. Length Scales and Turbulent Properties of Magnetic Fields in Simulated Galaxy Clusters. ArXiv e-prints. (published) 51. Egan, H., B. D. Smith, B. W. O’Shea, and J. M. Shull (2014), BRINGING SIMULATION AND OBSERVATION TOGETHER TO BETTER UNDERSTAND THE INTERGALACTIC MEDIUM, The Astrophysical Journal, 791(1), 64, doi:10.1088/0004-637x/791/1/64. (published) 52. Meece, G. R., B. W. O’Shea, and G. M. Voit (2015), GROWTH AND EVOLUTION OF THERMAL INSTABILITIES IN IDEALIZED GALAXY CLUSTER CORES, The Astrophysical Journal, 808(1), 43, doi:10.1088/0004- 637x/808/1/43. (published) 53. Meece, G. R., G. M. Voit, and B. W. O’Shea (2017), Triggering and Delivery Algorithms for AGN Feedback, The Astrophysical Journal, 841(2), 133, doi:10.3847/1538-4357/aa6fb1. (published) 17. TG-AST130004 54. Chang, P., J. Wadsley, and T. R. Quinn (2017), A moving-mesh hydrodynamic solver for ChaNGa, Monthly Notices of the Royal Astronomical Society, 471(3), 3577–3589, doi:10.1093/mnras/stx1809. (published) [Stampede, TACC] 55. Garg, U., and P. Chang (2017), A Semi-analytic Criterion for the Spontaneous Initiation of Carbon Detonations in White Dwarfs, The Astrophysical Journal, 836(2), 189, doi:10.3847/1538-4357/aa5d58. (published) [Stampede, TACC] 56. Murray, D. W., P. Chang, N. W. Murray, and J. Pittman (2016), Collapse in self-gravitating turbulent fluids, Monthly Notices of the Royal Astronomical Society, 465(2), 1316–1335, doi:10.1093/mnras/stw2796. (published) [Stampede, TACC] 57. Shalaby, M., A. E. Broderick, P. Chang, C. Pfrommer, A. Lamberts, and E. Puchwein (2017), SHARP: A Spatially Higher-order, Relativistic Particle-in-cell Code, The Astrophysical Journal, 841(1), 52, doi:10.3847/1538- 4357/aa6d13. (published) [Comet, SDSC] 18. TG-AST130007 58. Minor, Q., Kaplinghat, M., Li, N. 2016. A robust mass estimator for dark matter subhalo perturbations in strong gravitational lenses. ArXiv e-prints. (published) [Stampede, TACC] 19. TG-AST140064 59. Bonaca, A., Conroy, C., Wetzel, A., Hopkins, P., Keres, D. 2017. Gaia reveals a metal-rich in-situ component of the local stellar halo. ArXiv e-prints. (published) arXiv:1704.05463 60. El-Badry, K., Quataert, E., Wetzel, A., Hopkins, P., Weisz, D., et al. 2017. Gas kinematics, morphology, and angular momentum in the FIRE simulations. ArXiv e-prints. (published) arXiv:1705.10321
RY2 IPR 4 Page 98 61. El-Badry, K., A. R. Wetzel, M. Geha, E. Quataert, P. F. Hopkins, D. Kereš, T. K. Chan, and C.-A. Faucher-Giguère (2017), When the Jeans do not Fit: How Stellar Feedback Drives Stellar Kinematics and Complicates Dynamical Modeling in Low-mass Galaxies, The Astrophysical Journal, 835(2), 193, doi:10.3847/1538- 4357/835/2/193. (published) 62. Kim, J., Ma, X., Grudic, M., Hopkins, P., Hayward, C., et al. 2017. Formation of Globular Cluster Candidates in Merging Proto-galaxies at High Redshift: A View from the FIRE Cosmological Simulations. ArXiv e-prints. (published) arXiv:1704.02988 63. Ma, X., P. F. Hopkins, A. R. Wetzel, E. N. Kirby, D. Anglés-Alcázar, C.-A. Faucher-Giguère, D. Kereš, and E. Quataert (2017), The structure and dynamical evolution of the stellar disc of a simulated Milky Way-mass galaxy, Monthly Notices of the Royal Astronomical Society, 467(2), 2430–2444, doi:10.1093/mnras/stx273. (published) 64. “Reconciling Dwarf Galaxies with ΛCDM Cosmology: Simulating a Realistic Population of Satellites around a Milky 65. Way-mass Galaxy” Wetzel, Andrew R.; Hopkins, Philip F.; Kim, Ji-hoon; Faucher-Giguere, Claude-Andre; Keres, Dusan; Quataert, Eliot 2016, ApJL, 827, L23 66. “Breathing FIRE: How Stellar Feedback Drives Radial Migration, Rapid Size Fluctuations, and Population Gradients in Low-Mass Galaxies” El-Badry, Kareem; Wetzel, Andrew; Geha, Marla; Hopkins, Philip F.; Keres, Dusan; Chan, T. K.; Faucher-Giguere, Claude-Andre 2015, ApJ, 820, 131 67. “FIRE in the Field: Simulating the Threshold of Galaxy Formation” Fitts, Alex; Boylan-Kolchin, Michael; Elbert, Oliver D.; Bullock, James S.; Hopkins, Philip F.; Onorbe, Jose; Wetzel, Andrew R.; Wheeler, Coral; Faucher- Giguere, Claude-Andre; Keres, Dusan; Skillman, Evan D.; Weisz, Daniel R. 2016, ApJ, submitted, arXiv:1611.02281 68. “Not so lumpy after all: modeling the depletion of dark matter subhalos by Milky Way-like galaxies” Garrison- Kimmel, Shea; Wetzel, Andrew R.; Bullock, James S.; Hopkins, Philip F.; Boylan-Kolchin, Michael; Faucher- Giguere, Claude-Andre; Keres, Dusan; Quataert, Eliot; Sanderson, Robyn E.; Graus, Andrew S.; Kelley, Tyler 2017, MNRAS, submitted, arXiv:1701.03792 69. “FIRE-2 Simulations: Physics versus Numerics in Galaxy Formation” Hopkins, Philip F; Wetzel, Andrew; Keres, Dusan; Faucher-Giguere, Claude-Andre; Quataert, Eliot; Boylan-Kolchin, Michael; Murray, Norman; Hayward, Christopher C.; Garrison-Kimmel, Shea; Hummels, Cameron; Feldmann, Robert; Torrey, Paul; Ma, Xiangcheng; Angles-Alcazar, Daniel; Su, Kung-Yi; Orr, Matthew; Schmitz, Denise; Escala, Ivanna; Sanderson, Robyn; Grudic, Michael Y. 2017, MNRAS, submitted, arXiv:1702.06148 70. “How To Model Supernovae in Simulations of Star and Galaxy Formation” Hopkins, Philip F; Wetzel, Andrew; Keres, Dusan; Faucher-Giguere, Claude-Andre; Quataert, Eliot; Boylan-Kolchin, Michael; Murray, Norman; Hayward, Christopher C.; Martizzi, Davide; El-Badry, Kareem 2017, MNRAS, submitted 71. “Black Holes on FIRE: Stellar Feedback Limits Early Feeding of Galactic Nuclei” Daniel Angles-Alcazar, Claude- Andre Faucher-Giguere, Eliot Quataert, Philip F. Hopkins, Robert Feldmann, Paul Torrey, Andrew Wetzel, Dusan Keres 2017, MNRAS, submitted, arXiv:1707.03832 72. “An Origin for Double [α/Fe] Tracks in Milky Way-mass Galaxies” Wetzel, Andrew R.; Loebman, Sarah; Hopkins, Philip F.; Garrison-Kimmel, Shea; Sanderson, Robyn; Faucher-Giguere, Claude-Andre; Keres, Dusan; Quataert, Eliot 2017, in preparation 20. TG-AST160020 73. Fielding, D., Quataert, E., Martizzi, D., Faucher-Giguere, C. 2017. How Supernovae Launch Galactic Winds. ArXiv e-prints. (published) [Comet, Data Oasis, SDSC] 21. TG-AST160046 74. Morris, B., Hebb, L., Davenport, J., Rohn, G., Hawley, S. 2017. The Starspots of HAT-P-11: Evidence for a Solar- like Dynamo. ArXiv e-prints. (published) [OSG] 75. Morris, B., Hebb, L., Davenport, J., Rohn, G., Hawley, S. 2017. The Starspots of HAT-P-11: Evidence for a Solar- like Dynamo. ArXiv e-prints. (published) [OSG] 22. TG-AST160063 76. Sarmento, R., E. Scannapieco, and L. Pan (2016), FOLLOWING THE COSMIC EVOLUTION OF PRISTINE GAS. I. IMPLICATIONS FOR MILKY WAY HALO STARS, The Astrophysical Journal, 834(1), 23, doi:10.3847/1538- 4357/834/1/23. (published) [Stampede, TACC]
RY2 IPR 4 Page 99 23. TG-ATM090042 77. Chen, H. W., R. B. Alley, and F. Zhang (2016), Interannual Arctic sea ice variability and associated winter weather patterns: A regional perspective for 1979-2014, Journal of Geophysical Research: Atmospheres, 121(24), 14,433–14,455, doi:10.1002/2016jd024769. (published) [Ranch, Stampede, TACC] 78. Chen, X., Pauluis, O., Zhang, F. 2017. Regional Simulation of Indian summer Monsoon Intraseasonal Oscillations at Gray Zone Resolution. Journal of Climate. (in review) [Ranch, Stampede, TACC] 79. Chen, X., Pauluis, O., Zhang, F. 2017. Atmospheric overturning across multiple scales of an MJO event during the CINDY/DYNAMO Campaign. Journal of the Atmospheric Sciences. (in review) [Ranch, Stampede, TACC] 80. Chen, Y., Zhang, F., Green, B., Xu, Y. 2017. Combined Impacts of Ocean Cooling and Reduced Wind Drag on the Intensity and Structure of Hurricane Katrina (2005). . Journal of Advances in Modeling Earth Systems. (in review) [Ranch, Stampede, TACC] 81. Emanuel, K., and F. Zhang (2017), The Role of Inner-Core Moisture in Tropical Cyclone Predictability and Practical Forecast Skill, Journal of the Atmospheric Sciences, 74(7), 2315–2324, doi:10.1175/jas-d-17-0008.1. (published) [Ranch, Stampede, TACC] 82. Li, M., F. Zhang, Q. Zhang, J. Y. Harrington, and M. R. Kumjian (2017), Nonlinear response of hail precipitation rate to environmental moisture content: A real case modeling study of an episodic midlatitude severe convective event, Journal of Geophysical Research: Atmospheres, 122(13), 6729–6747, doi:10.1002/2016jd026373. (published) [Ranch, Stampede, TACC] 83. Li, M., Q. Zhang, and F. Zhang (2016), Hail Day Frequency Trends and Associated Atmospheric Circulation Patterns over China during 1960–2012, Journal of Climate, 29(19), 7027–7044, doi:10.1175/jcli-d-15-0500.1. (published) [Ranch, Stampede, TACC] 84. Melhauser, C., F. Zhang, Y. Weng, Y. Jin, H. Jin, and Q. Zhao (2017), A Multiple-Model Convection-Permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014), Weather and Forecasting, 32(2), 665–688, doi:10.1175/waf-d-16-0082.1. (published) [Ranch, Stampede, TACC] 85. Minamide, M., and F. Zhang (2017), Adaptive Observation Error Inflation for Assimilating All-Sky Satellite Radiance, Monthly Weather Review, 145(3), 1063–1081, doi:10.1175/mwr-d-16-0257.1. (published) [Ranch, Stampede, TACC] 86. Munsell, E., Zhang, F., Braun, S., Sippel, J., Didlake, A. 2017. The inner-core temperature structure of Hurricane Edouard (2014): Observations and ensemble variability. Monthly Weather Review. (in review) [Ranch, Stampede, TACC] 87. Munsell, E. B., F. Zhang, J. A. Sippel, S. A. Braun, and Y. Weng (2017), Dynamics and Predictability of the Intensification of Hurricane Edouard (2014), Journal of the Atmospheric Sciences, 74(2), 573–595, doi:10.1175/jas-d-16-0018.1. (published) [Ranch, Stampede, TACC] 88. Nystrom, R., Zhang, F., Munsell, E., Braun, S., Sippel, J., et al. 2017. Predictability and dynamics of Hurricane Joaquin (2015) explored through convection-permitting ensemble sensitivity experiments. Journal of the Atmospheric Sciences. (in review) [Ranch, Stampede, TACC] 89. Pauluis, O., Zhang, F. 2017. Reconstruction of thermodynamic cycles in a high resolution simulation of a hurricane. Journal of the Atmospheric Sciences. (accepted) [Ranch, Stampede, TACC] 90. Sun, Y. Q., R. Rotunno, and F. Zhang (2017), Contributions of Moist Convection and Internal Gravity Waves to Building the Atmospheric −5/3 Kinetic Energy Spectra, Journal of the Atmospheric Sciences, 74(1), 185–201, doi:10.1175/jas-d-16-0097.1. (published) [Ranch, Stampede, TACC] 91. Tang, X., Tan, Z., Sun, Y., Zhang, F. 2017. Impacts of diurnal radiation cycle on secondary eyewall formation. Journal of the Atmospheric Sciences. (accepted) [Ranch, Stampede, TACC] 92. Wei, J., F. Zhang, and J. H. Richter (2016), An Analysis of Gravity Wave Spectral Characteristics in Moist Baroclinic Jet–Front Systems, Journal of the Atmospheric Sciences, 73(8), 3133–3155, doi:10.1175/jas-d-15- 0316.1. (published) [Ranch, Stampede, TACC] 93. Ying, Y., Zhang, F. 2017. Practical and intrinsic predictability of multi-scale weather and convectively-coupled equatorial waves during the active phase of an MJO. Journal of the Atmospheric Sciences. (in review) [Ranch, Stampede, TACC] 94. Ying, Y., Zhang, F., Anderson, J. 2017. On the selection of localization radius in ensemble filtering for multi- scale quasi-geostrophic dynamics. Monthly Weather Review. (in review) [Ranch, Stampede, TACC] 95. Zhang, F., D. Tao, Y. Q. Sun, and J. D. Kepert (2017), Dynamics and predictability of secondary eyewall formation in sheared tropical cyclones, Journal of Advances in Modeling Earth Systems, 9(1), 89–112, doi:10.1002/2016ms000729. (published) [Ranch, Stampede, TACC]
RY2 IPR 4 Page 100 96. Zhang, F., S. Taraphdar, and S. Wang (2017), The role of global circumnavigating mode in the MJO initiation and propagation, Journal of Geophysical Research: Atmospheres, 122(11), 5837–5856, doi:10.1002/2016jd025665. (published) [Ranch, Stampede, TACC] 97. Zhang, Y., Zhang, F., Davis, C., Sun, J. 2017. Diurnal evolution and structure of long-lived mesoscale convective vortices along the Mei-yu front over the East China Plains. Journal of the Atmospheric Sciences. (in review) [Ranch, Stampede, TACC] 98. Zhu, L., Z. Meng, F. Zhang, and P. M. Markowski (2017), The influence of sea- and land-breeze circulations on the diurnal variability of precipitation over a tropical island, Atmospheric Chemistry and Physics Discussions, 1–46, doi:10.5194/acp-2017-332. (published) [Ranch, Stampede, TACC] 99. Sieron S. B., E.E. Clothiaux, F. Zhang, Y. Lu and J. Otkin, 2017: Fast Radiative Transfer Modeling for All-Sky Microwave Satellite Radiances: Modifying CRTM with Microphysics-Consistent Cloud Optical Properties. Journal of Geophysical Research - Atmosphere, accepted. 100. Chen, X., F. Zhang, K. Zhao, 2017: Influence of Monsoonal Wind Speed and Moisture Content on Intensity and Diurnal Variations of the Mei-yu Season Coastal Rainfall over South China. Journal of the Atmospheric Sciences, accepted. 101. Zhu, L., Z. Meng, F. Zhang, P. M. Markowski, 2017: The influence of sea- and landbreeze circulations on the diurnal variability of precipitation over a tropical island. Atmospheric Chemistry and Physics, in review. 102. Poterjoy, J., and F. Zhang, 2016: Comparison of Hybrid Four-Dimensional Data Assimilation Methods with and without the Tangent Linear and Adjoint Models for Predicting the Life Cycle of Hurricane Karl (2010). Monthly Weather Review, 144, 1449–1468. 103. Zhang, F., M. Minamide, E. E. Clothiaux, 2016: Potential Impacts of Assimilating Allsky Satellite Radiances from GOES-R on Convection-Permitting Analysis and Prediction of Tropical Cyclones. Geophysical Research Letters, 43, doi:10.1002/2016GL068468. 104. Sun, Y. Q., and F. Zhang, 2016: Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect. Journal of the Atmospheric Sciences, 73, 1419-1438. 105. Dong, L., and F. Zhang, 2016: OBEST: An observation-based ensemble setting technique for tropical cyclone track forecasting. Weather and Forecasting, 31, 57–70. 106. Zhang, Y. J., F. Zhang, Z. Meng, D. J. Stensrud 2016: Intrinsic Predictability of the 20 May 2013 Tornadic Thunderstorm Event in Oklahoma at Storm Scales. Monthly Weather Review, 144, 273-1298. 107. Weng, Y. and F. Zhang, 2016: Advances in Convection-permitting Tropical Cyclone Analysis and Prediction through EnKF Assimilation of Reconnaissance Aircraft Observations. Journal of Metrological Society of Japan, 94, doi:10.2151/jmsj. 2016-018. 108. Zhang, F., and K. A. Emanuel, 2016: On the role of surface fluxes and WISHE in tropical cyclone intensification. Journal of the Atmospheric Sciences, 73, 2011-2019. 109. Tang, X., and F. Zhang, 2016: Impacts of the Diurnal Radiation Cycle on the Formation, Intensity and Structure of Hurricane Edouard (2014). Journal of the Atmospheric Sciences, 73, 2871-2892. 110. Melhauser, C., and F. Zhang, 2016: Application of a Simplified Co-plane Wind Retrieval Using Dual-Beam Airborne Doppler Radar Observations for Tropical Cyclone Prediction. Monthly Weather Review, 144, 2645- 2666. 111. Fang, J. and F. Zhang, 2016: Contribution of tropical waves to the formation of Super Typhoon Megi (2010). Journal of the Atmospheric Sciences, 73, 4387-4405. 112. Emanuel, K. and F. Zhang, 2016: On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts. Journal of the Atmospheric Sciences, 73, 3739-3747. 113. Li, M., Q. Zhang and F. Zhang, 2016: Hail Frequency and its Association with Atmospheric Circulation Patterns in Mainland China during 1960-2012. Journal of Climate, 29, 7027-7044. 114. Wei, J., F. Zhang, and J. H. Richter, 2016: Toward Improving Nonorographic Gravity Wave Parameterizations: An Analysis of Gravity Wave Spectral Characteristics in Moist Baroclinic Jet-Front Systems. Journal of the Atmospheric Sciences, 73, 3133-3155. 115. Chen, H. W., F. Zhang, R. B. Alley, 2016: The Robustness of Midlatitude Weather Pattern Changes due to Arctic Sea Ice Loss. Journal of Climate, 29, 7831-7849. 24. TG-ATM090047, TG-MCA95C006 116. Gagne, D., McGovern, A., Brotzge, J., Coniglio, M., Correia, J., et al. 2015. Day-Ahead Hail Prediction Integrating Machine Learning with Storm-Scale Numerical Weather Models. Proceedings of the 27th Innovative Applications of Artificial Intelligence Conference (Austin, TX). 3954-3960.
RY2 IPR 4 Page 101 https://www.aaai.org/ocs/index.php/IAAI/IAAI15/paper/view/9724. (published) [Globus Online, Stampede, TACC] 117. Gagne, D., McGovern, A., Haupt, S., Sobash, R., Williams, J., et al. 2017. Storm-based probabilistic hail forecasting with machine learning applied to convection-allowing ensembles. Weather and Forecasting. (submitted) [Stampede, TACC] 118. McGovern, A., K. L. Elmore, D. J. Gagne, S. E. Haupt, C. D. Karstens, R. Lagerquist, T. Smith, and J. K. Williams (2017), Using Artificial Intelligence to Improve Real-Time Decision Making for High-Impact Weather, Bulletin of the American Meteorological Society, doi:10.1175/bams-d-16-0123.1. (published) [Stampede, TACC] 25. TG-ATM100022, TG-MCA95C006 119. Roberts, B., and M. Xue (2017), The Role of Surface Drag in Mesocyclone Intensification Leading to Tornadogenesis within an Idealized Supercell Simulation, Journal of the Atmospheric Sciences, 74(9), 3055– 3077, doi:10.1175/jas-d-16-0364.1. (published) 120. Roberts, B., M. Xue, A. D. Schenkman, and D. T. Dawson (2016), The Role of Surface Drag in Tornadogenesis within an Idealized Supercell Simulation, Journal of the Atmospheric Sciences, 73(9), 3371–3395, doi:10.1175/jas-d-15-0332.1. (published) 26. TG-ATM110005 121. Barber, K., Mullendore, G., Alexander, M. 2017. Out-of-Cloud Convective Turbulence: Estimation Method and Impacts of Model Resolution. J . Appl. Meteor. and Climatol.. (submitted) 122. Barber, K., Mullendore, G., Alexander, M. 2017. Regional Observations of Convectively-Induced Turbulence from Varied Resolution Full-Physics Model. Conference on Aviation, Range, and Aerospace Meteorology (Seattle, WA). (published) 123. Maddox, E., Mullendore, G. 2016. Sensitivity of cross-tropopause convective transport to tropopause definition. AGU Annual Fall Meeting (San Francisco, CA). (published) 124. Mullendore, G., Starzec, M. 2016. Forecast Model Activities for North Dakota Cloud Modification Project. Journal of Weather Modification 48: 93-98. http://www.weathermodification.org/publications/index.php/JWM/article/view/546 (published) 27. TG-ATM140033 125. Patricola, C. M., R. Saravanan, and P. Chang (2017), A teleconnection between Atlantic sea surface temperature and eastern and central North Pacific tropical cyclones, Geophysical Research Letters, 44(2), 1167–1174, doi:10.1002/2016gl071965. (published) 28. TG-ATM140042 126. Geerts, B., Wang, X., al, e. 2017. The 2015 Plains Elevated Convection At Night (PECAN) field project. Bulletin of the American Meteorological Society. (published) 127. Johnson, A., Wang, X. 2017. Design and implementation of a GSI-based convection-allowing ensemble data assimilation and forecast system for the PECAN field experiment. Part 1: Optimal configurations for nocturnal convection prediction. Weather and Forecasting. 32, 289-315. 128. Johnson, A., Wang, X., Degelia, S. 2017. Design and implementation of a GSI-based convection-allowing ensemble based data assimilation and forecast system for the PECAN field experiment. Part 2: Overview and evaluation of real-time system. Weather and Forecasting 32, 1227-1251 129. Wang, Y., Wang, X. 2017. Direct Assimilation of Radar Reflectivity without Tangent Linear and Adjoint of the Nonlinear Observation Operator in GSI-Based EnVar System: Methodology and Experiment with the May 8th 2003 Oklahoma City Tornadic Supercell.. Mon. Wea. Rev.. (published) 130. Degelia, S. K., X. Wang, D. J. Stensrud, and A. Johnson, 2017: Understanding and Predicting a Nocturnal Convection Initiation Event on 25 June 2013 using an Ensemble-based Multi-scale Data Assimilation System. Part I: Observation Impacts and Sensitivities to Physical Parameterization Schemes. Mon. Wea. Rev. In review. 131. Degelia, S. K., X. Wang, and D. J. Stensrud, 2017: Understanding and Predicting a Nocturnal Convection Initiation Event on 25 June 2013 using an Ensemble-based Multi-scale Data Assimilation System. Part II: Analysis of Mechanisms Responsible for Initiation. Mon. Wea. Rev. In review. 132. Duda, J., X. Wang, F. Kong, M. Xue, and J. Berner, 2015: Impact of a Stochastic Kinetic Energy Backscatter Scheme on Warm season Convection-Allowing Ensemble Forecasts. Mon. Wea. Rev., 144, 1887-1908. 133. Duda, J., X. Wang and M. Xue, 2016: Sensitivity of Convection-Allowing Forecasts to Land-Surface Model Perturbations and Implications for Ensemble Design. Mon. Wea. Rev., to be submitted
RY2 IPR 4 Page 102 134. Johnson, A., X. Wang, J. R. Carley, L. J. Wicker, C. Karstens, 2015: A comparison of multi-scale GSI-based EnKF and 3DVar data assimilation using radar and conventional observations for mid-latitude convective-scale precipitation forecasts. Mon. Wea. Rev., 143, 3087–3108. 135. Johnson, A. and X. Wang, 2016: A study of multi-scale initial condition perturbation methods for convection- permitting ensemble forecasts Mon. Wea. Rev., in Press 136. Johnson, A., X. Wang, 2017: Impacts of inconsistencies between initial and lateral boundary condition perturbations in a multi-scale ensemble forecast system. Mon. Wea. Rev., In Preparation 29. TG-ATM160014 137. Hu, X., Xue, M. 2017. Precipitation dynamical downscaling over the Great Plains and its hydrological application. J. Appl. Meteor. Climatol.. (in preparation) 138. Hu, X.-M., M. Xue, and R. A. McPherson (2017), The Importance of Soil-Type Contrast in Modulating August Precipitation Distribution Near the Edwards Plateau and Balcones Escarpment in Texas, Journal of Geophysical Research: Atmospheres, doi:10.1002/2017jd027035. (submitted) [Comet, SDSC] 139. Wang, J., Klein, P., Xue, M., Hu, X. 2017. Comparison of different urban schemes’ performance on Dallas-Fort Worth during two summers. Journal of Applied Meteorology and Climatology. (in preparation) [SDSC] 140. Yang, Y., Hu, X. 2017. Sensitivity of WRF Simulations with the YSU PBL Scheme to the Lowest Model Level Height for a Sea Fog Event over the Yellow Sea. Atmospheric Research. (in preparation) [Comet, SDSC] 30. TG-ATM160026, TG-MCA95C006 141. Putnam, B., Xue, M., Jung, Y., Snook, N., Zhang, G. 2017. Ensemble probabilistic prediction of a mesoscale convective system and associated polarimetric radar variables using single-moment and double-moment microphysics schemes and EnKF radar data assimilation. Monthly Weather Review 145: 2257-2279. http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-16-0162.1?journalCode=mwre (accepted) [Stampede, TACC] 31. TG-ATM160027 142. CHASTANG, J., Ramamurthy, M. 2017. Unidata Science Gateway on the XSEDE Jetstream Cloud. Gateways 2017 (Ann Arbor, Michigan, USA). (submitted) [ECSS, IU, Jetstream, Science Gateways, TACC, Wrangler] 143. Chastang, J., Signell, R. 2017. Met/Ocean Modeling Workflows on XSEDE via HPC & Cloud. https://figshare.com/articles/Met_Ocean_Modeling_Workflows_on_XSEDE_via_HPC_Cloud/5249845.DOI:10.6 084/m9.figshare.5249845.v1 (Invalid?). (published) [ECSS, IU, Jetstream, Science Gateways, TACC, Wrangler] 144. CHASTANG, J., Signell, R., Fischer, J. 2017. Reducing Time to Science: Unidata and JupyterHub Technology Using the Jetstream Cloud. 2017 AGU Fall Meeting (New Orleans, Louisiana USA). (submitted) [ECSS, IU, Jetstream, Science Gateways, TACC, Wrangler] 145. CHASTANG, J., Signell, R., Fischer, J. 2018. A Unidata JupyterHub Server: An Online PyAOS Resource for Students and Educators. 97th AMS Annual Meeting (Austin, Texas USA). (submitted) [ECSS, IU, Jetstream, Science Gateways, TACC, Wrangler] 146. Ramamurthy, M., J., C., May, R., M., J. 2017. Unidata and data-proximate analysis and visualization in the cloud. https://figshare.com/articles/Unidata_and_data- proximate_analysis_and_visualization_in_the_cloud/5249839.DOI:10.6084/m9.figshare.5249839.v1 (Invalid?). (published) [ECSS, IU, Jetstream, Science Gateways, TACC, Wrangler] 32. TG-BIO150061 147. Christodoulides, N., Van Dam, A., Peterson, D., Frandsen, R., Mortensen, U., et al. 2017. Gene expression plasticity across hosts of an invasive scale insect species. PLOS ONE 5: e0176956. http://dx.doi.org/10.1371/journal.pone.0176956 DOI:10.1371/journal.pone.0176956 (Invalid?). (published) [Bridges Large, Bridges Regular, PSC, Pylon] 33. TG-BIO160026 148. Brandon S. Razooky, Youfang Cao, Alan S. Perelson, Michael L. Simpson, and Leor S. Weinberger (2017). Non- latching positive feedback enables robust bimodality by de-coupling stochastic extinction from mean expression. In review, PLoS Biology. 149. Youfang Cao, Jessica M. Conway, Stephen Mason, James B. Whitney, and Alan S. Perelson (2017). Control of SIV with transient anti-PD-L1 treatment. Manuscript in preparation.
RY2 IPR 4 Page 103 150. Youfang Cao, Emily Cartwright, Guido Silvestri, and Alan S. Perelson (2017). Modeling viral dyanmics of CD8+ depletion in SIV infected rhesus macaques. Manuscript in preparation. 151. Youfang Cao, Xue Lei, Ruy M. Ribeiro, Alan S. Perelson, and Jie Liang (2017). Stochastic control of latency and activation in HIV-1 infected cells. Manuscript in preparation. 152. Youfang Cao, Carrie A. Manore, and Ethan Romero-Severson (2017). High-resolution modeling for stochastic epidemiology using Accurate Chemical Master Equation (ACME) method. Manuscript in preparation. 153. Youfang Cao and Eduardo Sontag (2017). Control of stochastic transitions in genetic toggle switch. Manuscript in preparation. 154. Youfang Cao, Jieling Zhao, Luisa A. DiPietro, and Jie Liang (2017). Control of skin wound healing through ECM chemotaxis gradient buffering effect. Manuscript in preparation. 34. TG-BIO160051 155. Sanyal, N., Johnson, V., Chen, C. 2017. GWASinlps: Nonlocal prior based SNP selection tool for genome-wide association studies. Novel Bayesian SNP selection tool for GWAS combining the parsimonious uncertainty quantification provided by nonlocal priors and the computational efficiency of iterative variable selection, resulting in reduced false positives.. https://www.dropbox.com/s/bw9egx51dogut0h/manuscript_in_preparation.pdf?dl=0. (in preparation) [Comet] 35. TG-CCR120014 156. Simakov, N. A. et al. (2016), A Quantitative Analysis of Node Sharing on HPC Clusters Using XDMoD Application Kernels, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949553. (published) [Blacklight, Comet, GaTech, Gordon, IU, Lonestar, LSU, NICS, OSG, PSC, SDSC, Stampede, Standford, SuperMIC, TACC, Trestles] 36. TG-CDA100010 157. Barati Farimani, A., and N. R. Aluru (2016), Existence of Multiple Phases of Water at Nanotube Interfaces, The Journal of Physical Chemistry C, 120(41), 23763–23771, doi:10.1021/acs.jpcc.6b06156. (published) 158. Barati Farimani, A., P. Dibaeinia, and N. R. Aluru (2016), DNA Origami–Graphene Hybrid Nanopore for DNA Detection, ACS Applied Materials & Interfaces, 9(1), 92–100, doi:10.1021/acsami.6b11001. (published) 159. Farimani, A. B., M. Heiranian, and N. R. Aluru (2016), Nano-electro-mechanical pump: Giant pumping of water in carbon nanotubes, Scientific Reports, 6(1), doi:10.1038/srep26211. (published) 160. Feng, J., M. Graf, K. Liu, D. Ovchinnikov, D. Dumcenco, M. Heiranian, V. Nandigana, N. R. Aluru, A. Kis, and A. Radenovic (2016), Single-layer MoS2 nanopores as nanopower generators, Nature, 536(7615), 197–200, doi:10.1038/nature18593. (published) [Comet, SDSC] 161. Wu, Y., L. K. Wagner, and N. R. Aluru (2016), Hexagonal boron nitride and water interaction parameters, The Journal of Chemical Physics, 144(16), 164118, doi:10.1063/1.4947094. (published) [Comet, SDSC] 37. TG-CDA110003 162. Olson, L. 2017. Test2. Testit (Here). (published) [Stampede, TACC] 163. Olson, L. 2017. Test. Test conference (Here). (published) [Stampede, TACC] 164. Olson, L., Throne, R., Nolte, A. 2016. An inverse problem approach to early detection of breast cancer. Inverse Problems Symposium 2016 (Lexington, VA). (published) [Stampede, TACC] 165. Olson, L., Throne, R., Nolte, A., Griffin, K., Iovanac, N., et al. 2017. Early Detection of Breast Cancer Through an Inverse Problem Approach to Stiffness Mapping: Results from Tissue Phantom Experiments. 14th U.S. National Congress on Computational Mechanics (Montreal, Quebec, Canada). (accepted) [Stampede, TACC] 38. TG-CDA160009, TG-CDA170003 166. Thiemann, N. 2017. Using Computational Tools to Design a Moleculary Imprinted Polymer with Selectivity and High Affinity for Acetylcholine. Thesis. Trinity College. (published) [Comet, Science Gateways, SDSC] 39. TG-CHE040013N 167. Banerjee, S. et al. (2017), Ionic and Neutral Mechanisms for C–H Bond Silylation of Aromatic Heterocycles Catalyzed by Potassium tert-Butoxide, Journal of the American Chemical Society, 139(20), 6880–6887, doi:10.1021/jacs.6b13032. (published)
RY2 IPR 4 Page 104 168. Champagne, P. A., and K. N. Houk (2016), Origins of Selectivity and General Model for Chiral Phosphoric Acid- Catalyzed Oxetane Desymmetrizations, Journal of the American Chemical Society, 138(38), 12356–12359, doi:10.1021/jacs.6b08276. (published) 169. Champagne, P. A., and K. N. Houk (2017), Influence of Endo- and Exocyclic Heteroatoms on Stabilities and 1,3- Dipolar Cycloaddition Reactivities of Mesoionic Azomethine Ylides and Imines, The Journal of Organic Chemistry, 82(20), 10980–10988, doi:10.1021/acs.joc.7b01928. (published) 170. Chogii, I., P. Das, J. S. Fell, K. A. Scott, M. N. Crawford, K. N. Houk, and J. T. Njardarson (2017), New Class of Anion-Accelerated Amino-Cope Rearrangements as Gateway to Diverse Chiral Structures, Journal of the American Chemical Society, 139(37), 13141–13146, doi:10.1021/jacs.7b07319. (published) 171. Duan, A., P. Yu, F. Liu, H. Qiu, F. L. Gu, M. P. Doyle, and K. N. Houk (2017), Diazo Esters as Dienophiles in Intramolecular (4 + 2) Cycloadditions: Computational Explorations of Mechanism, Journal of the American Chemical Society, 139(7), 2766–2770, doi:10.1021/jacs.6b12371. (published) 172. Fell, J. S., S. A. Lopez, C. J. Higginson, M. G. Finn, and K. N. Houk (2017), Theoretical Analysis of the Retro-Diels– Alder Reactivity of Oxanorbornadiene Thiol and Amine Adducts, Organic Letters, 19(17), 4504–4507, doi:10.1021/acs.orglett.7b02064. (published) 173. Fell, J. S., B. N. Martin, and K. N. Houk (2017), Origins of the Unfavorable Activation and Reaction Energies of 1-Azadiene Heterocycles Compared to 2-Azadiene Heterocycles in Diels–Alder Reactions, The Journal of Organic Chemistry, 82(4), 1912–1919, doi:10.1021/acs.joc.6b02524. (published) 174. Fraley, A., Garcia-Borràs, M., Tripathi, A., Khare, D., Mercado-Marin, E., et al. 2017. Function and structure of MalA/MalA’, iterative halogenases for late-stage C-H functionalization of indole alkaloids. J. Am. Chem. Soc 139: 12060-12068 . (published) 175. Grandner, J. M., R. A. Cacho, Y. Tang, and K. N. Houk (2016), Mechanism of the P450-Catalyzed Oxidative Cyclization in the Biosynthesis of Griseofulvin, ACS Catalysis, 6(7), 4506–4511, doi:10.1021/acscatal.6b01068. (published) 176. Grandner, J. M., H. Shao, R. H. Grubbs, P. Liu, and K. N. Houk (2017), Origins of the Stereoretentive Mechanism of Olefin Metathesis with Ru-Dithiolate Catalysts, The Journal of Organic Chemistry, 82(19), 10595–10600, doi:10.1021/acs.joc.7b02129. (published) 177. Grayson, M. N., Z. Yang, and K. N. Houk (2017), Chronology of CH···O Hydrogen Bonding from Molecular Dynamics Studies of the Phosphoric Acid-Catalyzed Allylboration of Benzaldehyde, Journal of the American Chemical Society, 139(23), 7717–7720, doi:10.1021/jacs.7b03847. (published) 178. He, C. Q., A. Simon, Y. Lam, A. P. J. Brunskill, N. Yasuda, J. Tan, A. M. Hyde, E. C. Sherer, and K. N. Houk (2017), Model for the Enantioselectivity of Asymmetric Intramolecular Alkylations by Bis-Quaternized Cinchona Alkaloid-Derived Catalysts, The Journal of Organic Chemistry, 82(16), 8645–8650, doi:10.1021/acs.joc.7b01577. (published) 179. Lee, J., J. M. Grandner, K. M. Engle, K. N. Houk, and R. H. Grubbs (2016), In Situ Catalyst Modification in Atom Transfer Radical Reactions with Ruthenium Benzylidene Complexes, Journal of the American Chemical Society, 138(22), 7171–7177, doi:10.1021/jacs.6b03767. (published) 180. Lin, J., Shah, T., Goetz, A., Garg, N., Houk, K. 2017. Conjugated Trimeric Scaffolds Accessible from Indolyne Cyclotrimerizations: Synthesis, Structures, and Electronic Properties. J. Am. Chem. Soc. 139: 10447–10455. (published) 181. Liu, W.-B. et al. (2017), Potassium tert-Butoxide-Catalyzed Dehydrogenative C–H Silylation of Heteroaromatics: A Combined Experimental and Computational Mechanistic Study, Journal of the American Chemical Society, 139(20), 6867–6879, doi:10.1021/jacs.6b13031. (published) 182. Mackey, J., Yang, Z., Houk, K. 2017. Dynamically Concerted and Stepwise Trajectories of the Cope Rearrangement of 1, 5-Hexadiene. Chemical Physics Letters 683: 253-257. (published) 183. Maji, R., P. A. Champagne, K. N. Houk, and S. E. Wheeler (2017), Activation Mode and Origin of Selectivity in Chiral Phosphoric Acid-Catalyzed Oxacycle Formation by Intramolecular Oxetane Desymmetrizations, ACS Catalysis, 7(10), 7332–7339, doi:10.1021/acscatal.7b02993. (published) 184. Messina, M. S., J. H. Ko, Z. Yang, M. J. Strouse, K. N. Houk, and H. D. Maynard (2017), Effect of trehalose polymer regioisomers on protein stabilization, Polym. Chem., 8(33), 4781–4788, doi:10.1039/c7py00700k. (published) 185. Narayanam, M., Ma, G., Champagne, P., Houk, K., Murphy, J. 2017. Synthesis of [18-F]Fluoroarenes by Nucleophilic Radiofluorination of N-Arylsydnones. Angewandte Chemie International Edition. http://dx.doi.org/10.1002/anie.201707274 DOI:10.1002/anie.201707274 (Invalid?). (published) 186. Noey, E. L., Z. Yang, Y. Li, H. Yu, R. N. Richey, J. M. Merritt, D. P. Kjell, and K. N. Houk (2017), Origins of Regioselectivity in the Fischer Indole Synthesis of a Selective Androgen Receptor Modulator, The Journal of Organic Chemistry, 82(11), 5904–5909, doi:10.1021/acs.joc.7b00878. (published)
RY2 IPR 4 Page 105 187. Ohashi, M., F. Liu, Y. Hai, M. Chen, M. Tang, Z. Yang, M. Sato, K. Watanabe, K. N. Houk, and Y. Tang (2017), SAM- dependent enzyme-catalysed pericyclic reactions in natural product biosynthesis, Nature, 549(7673), 502– 506, doi:10.1038/nature23882. (published) 188. Simon, A., Lam, Y., Houk, K. 2017. Origins of Stereoselectivity of Enamine–Iminium- Activated Nazarov Cyclizations by Vicinal Diamines. J. Org. Chem. 82: 8186–8190. (published) 189. Simon, A., Yeh, A., Lam, Y., Houk, K. 2016. Origins of Stereoselectivity of Chiral Vicinal Diamine-Catalyzed Aldol Reactions. J. Org. Chem. 82: 8186-8190. (published) 190. Toutov, A. A. et al. (2017), A potassium tert-butoxide and hydrosilane system for ultra-deep desulfurization of fuels, Nature Energy, 2(3), 17008, doi:10.1038/nenergy.2017.8. (published) 191. Yang, Y.-F., G. Chen, X. Hong, J.-Q. Yu, and K. N. Houk (2017), The Origins of Dramatic Differences in Five- Membered vs Six-Membered Chelation of Pd(II) on Efficiency of C(sp3)–H Bond Activation, Journal of the American Chemical Society, 139(25), 8514–8521, doi:10.1021/jacs.7b01801. (published) 192. Yang, Y.-F., K. N. Houk, and Y.-D. Wu (2016), Computational Exploration of RhIII/RhV and RhIII/RhI Catalysis in Rhodium(III)-Catalyzed C–H Activation Reactions of N-Phenoxyacetamides with Alkynes, Journal of the American Chemical Society, 138(21), 6861–6868, doi:10.1021/jacs.6b03424. (published) 193. Yavuz, I., Lopez, S., Lin, J., Houk, K. 2016. Quantitative Prediction of Morphology and Electron Transport in Crystal and Disordered Organic Semiconductors. J. Mater. Chem. C 4: 11238-11243 . (published) 194. Yu, P., T. Q. Chen, Z. Yang, C. Q. He, A. Patel, Y. Lam, C.-Y. Liu, and K. N. Houk (2017), Mechanisms and Origins of Periselectivity of the Ambimodal [6 + 4] Cycloadditions of Tropone to Dimethylfulvene, Journal of the American Chemical Society, 139(24), 8251–8258, doi:10.1021/jacs.7b02966. (published) 195. Yu, P., W. Li, and K. N. Houk (2017), Mechanisms and Origins of Selectivities of the Lewis Acid-Catalyzed Diels–Alder Reactions between Arylallenes and Acrylates, The Journal of Organic Chemistry, 82(12), 6398– 6402, doi:10.1021/acs.joc.7b01132. (published) 196. Zhao, J., J. L. Brosmer, Q. Tang, Z. Yang, K. N. Houk, P. L. Diaconescu, and O. Kwon (2017), Intramolecular Crossed [2+2] Photocycloaddition through Visible Light-Induced Energy Transfer, Journal of the American Chemical Society, 139(29), 9807–9810, doi:10.1021/jacs.7b05277. (published) 197. Zou, Y., Garcia-Borràs, M., Tang, M., Hirayama, Y., Li, D., et al. 2017. Enzyme-Catalyzed Cationic Epoxide Rearrangements in Quinolone Alkaloid Biosynthesis. Nat. Chem. Biol. 13: 325-332. (published) 40. TG-CHE060063 198. Votapka, L.W., C.T. Lee, and R.E. Amaro, Two Relations to Estimate Membrane Permeability Using Milestoning. J Phys Chem B, 2016. 120(33): p. 8606-16. 199. Shi, K., et al., Structural basis for targeted DNA cytosine deamination and mutagenesis by APOBEC3A and APOBEC3B. Nat Struct Mol Biol, 2016. 200. Lee, C.T., et al., Simulation-Based Approaches for Determining Membrane Permeability of Small Compounds. J Chem Inf Model, 2016. 56(4): p. 721-33 201. Rajappa-Titu, L., et al., RNA Editing TUTase 1: structural foundation of substrate recognition, complex interactions and drug targeting. Nucleic Acids Res, 2016. 44(22): p. 10862-10878. 202. Ottilie, S., et al., Rapid Chagas Disease Drug Target Discovery Using Directed Evolution in Drug-Sensitive Yeast. ACS Chem Biol, 2016. 203. Schiffer, J.M., et al., Model of the Ankyrin and SOCS Box Protein, ASB9, E3 Ligase Reveals a Mechanism for Dynamic Ubiquitin Transfer. Structure, 2016. 24(8): p. 1248- 56. 204. Durrant, J.D., R.M. Bush, and R.E. Amaro, Microsecond Molecular Dynamics Simulations of Influenza Neuraminidase Suggest a Mechanism for the Increased Virulence of Stalk-Deletion Mutants. J Phys Chem B, 2016. 120(33): p. 8590-9. 205. Swift, R.V., et al., Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles. J Chem Inf Model, 2016. 56(5): p. 830-42. 206. Demir, O., P.U. Ieong, and R.E. Amaro, Full-length p53 tetramer bound to DNA and its quaternary dynamics. Oncogene, 2016. 207. Offutt, T.L., R.V. Swift, and R.E. Amaro, Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations. J Chem Inf Model, 2016. 208. Wagner, J.R., et al., Emerging Computational Methods for the Rational Discovery of Allosteric Drugs. Chem Rev, 2016. 116(11): p. 6370-90. 209. Buffalo, C.Z., et al., Conserved patterns hidden within group A Streptococcus M protein hypervariability recognize human C4b-binding protein. Nat Microbiol, 2016. 1(11): p. 16155.
RY2 IPR 4 Page 106 210. Goldgof, G.M., et al., Comparative chemical genomics reveal that the spiroindolone antimalarial KAE609 (Cipargamin) is a P-type ATPase inhibitor. Sci Rep, 2016. 6: p. 27806. 211. Schiffer, J.M., et al., Capturing Invisible Motions in the Transition from Ground to Rare Excited States of T4 Lysozyme L99A. Biophys J, 2016. 111(8): p. 1631-1640. 212. Votapka, L.W., et al., SEEKR: Simulation Enabled Estimation of Kinetic Rates, A Computational Tool to Estimate Molecular Kinetics and Its Application to Trypsin- Benzamidine Binding. J Phys Chem B, 2017. 213. P. Barros, E., et al., Electrostatic Interactions as Mediators in the Allosteric Activation of Protein Kinase A RIα. Biochemistry, 2017. 56(10): p. 1536-1545. 214. Purawat, S., et al., A Kepler Workflow Tool for Reproducible AMBER GPU Molecular Dynamics, Biophysical Journal, 2017, 112 (12) : p. 2469-2474. 215. S. P. Hirakis, R.D. Malmstrom, and R. Amaro, Molecular Simulations Reveal an Unresolved Conformation of the Type IA Protein Kinase A Regulatory Subunit and Suggest Its Role in the cAMP Regulatory Mechanism, Biochemistry, 2017. In press. 41. TG-CHE090043 216. Leonard, J., A. Haddad, O. Green, R. L. Birke, T. Kubic, A. Kocak, and J. R. Lombardi (2017), SERS, Raman, and DFT analyses of fentanyl and carfentanil: Toward detection of trace samples, Journal of Raman Spectroscopy, 48(10), 1323–1329, doi:10.1002/jrs.5220. (published) 42. TG-CHE100111, TG-CHE120058 217. Nekkanti, S., and C. B. Martin (2015), Theoretical study on the relative energies of cationic pterin tautomers, Pteridines, 26(1), doi:10.1515/pterid-2014-0011. (published) 43. TG-CHE100118 218. Driscoll, D. M., W. Tang, S. P. Burrows, D. A. Panayotov, M. Neurock, M. McEntee, and J. R. Morris (2017), Binding Sites, Geometry, and Energetics of Propene at Nanoparticulate Au/TiO2, The Journal of Physical Chemistry C, 121(3), 1683–1689, doi:10.1021/acs.jpcc.6b10997. (published) [LSU, SuperMIC] 44. TG-CHE110035, TG-DMR120029 219. Castillo-Chará, J., Ikard, T. 2016. Calculation of Vertical Electron Affinities and Vertical Detachment Energies for the [Au2-C60-Au2]-n(n = 0, 1, 2, 3) Complexes using the B3LYP/LANLDZ Method,. J. Und. Chem. Res. 15(1): 6-11. (published) [PSC, SDSC] 220. Ikard, T., Castillo-Chara, J. 2016. Calculation of Molecular Properties of the [Au2-C60-Au2]-n(n = 0, 1, 2, 3) Model Complexes. J. Und. Chem. Res 15(1): 1-5. (published) [PSC, SDSC] 45. TG-CHE110042 221. Han, Y.; Yan, C.; Nguyen, K.; Jackobel, A.; Ivanov, I.; Knutson, B.A.; He, Y. Structural mechanism of ATP- independent transcription initiation by RNA polymerase I. eLife (2017) 6, e27414, doi:10.7554/eLife.27414 222. Rashid F. et al. Single-molecule FRET unveils induced-fit mechanism for substrate selectivity in flap endonuclease 1. eLife (2017) 6, e21884, doi:10.7554/eLife.21884 223. Zhang, J., Qian, K., Yan, C., He, M., Jassim, B., Ivanov, I. & Zheng, Y. Discovery of decamidine as a new and potent PRMT1 inhibitor. Medicinal Chemistry Communications (2017) 8, 440-444, doi:10.1039/C6MD00573J 224. Laughlin, S.; Carter, E.K.; Ivanov, I* & Wilson, W.D. DNA microstructure influences selective binding of small molecules designed to target mixed-site DNA sequences. Nucleic Acids Research (2017) 45, 1297-1306, doi:10.1093/nar/gkw1232 225. He, Y., Yan, C., Inouye, C., Fang, J., Tjian, R., Ivanov, I. & Nogales E. Structural basis of transcription promoter opening using single particle cryo-EM. Nature (2016) 533, 359–365, doi:10.1038/nature17970 226. Turaga, R.C., Yin, L., Yang, J.J., Lee, H., Ivanov, I., Yan, C., Grossniklaus, H.E., Wang, S., Ma, C., Sun, L. & Liu, Z. Development of protein drug targeting integrin αvβ3 at a novel site by rational protein design. Nature Communications (2016) 7, 11675, doi:10.1038/ncomms11675 227. Xu, X., Yan, C., Kossmann, B. & Ivanov, I.* Secondary interaction interfaces with PCNA control conformational switching of DNA polymerase PolB from polymerization to editing. Journal of Physical Chemistry B (2016) 120, 8379–8388, doi:10.1021/acs.jpcb.6b02082 (Invited article for the J. Andrew McCammon Festschrift special issue)
RY2 IPR 4 Page 107 228. Kossmann, B., Marchand C, Pommier Y* & Ivanov, I* Discovery of selective inhibitors of tyrosyl-DNA phosphodiesterase 2 by targeting the enzyme DNA-binding cleft. Bioorganic and Medicinal Chemistry Letters (2016) 26, 3232-3236, doi:10.1016/j.bmcl.2016.05.065 46. TG-CHE110065 229. Shao, Q., and C. K. Hall (2017), Allosteric effects of gold nanoparticles on human serum albumin, Nanoscale, 9(1), 380–390, doi:10.1039/c6nr07665c. (published) 47. TG-CHE110085 230. Chagarov, E., K. Sardashti, M. Edmonds, M. Clemons, and A. Kummel (2016), Density functional theory simulations and experimental measurements of a-HfO2/a-Si3N4/SiGe, a-HfO2/SiO0.8N0.8/SiGe and a- HfO2/a-SiO/SiGe interfaces, 2016 IEEE International Electron Devices Meeting (IEDM), doi:10.1109/iedm.2016.7838554. (published) [Comet, SDSC] 231. Chagarov, E., K. Sardashti, R. Haight, D. B. Mitzi, and A. C. Kummel (2016), Density-functional theory computer simulations of CZTS0.25Se0.75 alloy phase diagrams, The Journal of Chemical Physics, 145(6), 064704, doi:10.1063/1.4959591. (published) [Comet, SDSC] 232. Chagarov, E., K. Sardashti, T. Kaufman-Osborn, S. Madisetti, S. Oktyabrsky, B. Sahu, and A. Kummel (2015), Density-Functional Theory Molecular Dynamics Simulations and Experimental Characterization of a- Al2O3/SiGe Interfaces, ACS Applied Materials & Interfaces, 7(47), 26275–26283, doi:10.1021/acsami.5b08727. (published) [Comet, SDSC] 233. Edmonds, M. et al. (2017), Low temperature thermal ALD of a SiNxinterfacial diffusion barrier and interface passivation layer on SixGe1− x(001) and SixGe1− x(110), The Journal of Chemical Physics, 146(5), 052820, doi:10.1063/1.4975081. (published) [Comet, SDSC] 234. Edmonds, M., S. Wolf, E. Chagarov, T. Kent, J. H. Park, R. Holmes, D. Alvarez, R. Droopad, and A. C. Kummel (2017), Self-limiting CVD of a passivating SiO x control layer on InGaAs(001)-(2x4) with the prevention of III- V oxidation, Surface Science, 660, 31–38, doi:10.1016/j.susc.2017.02.006. (published) [Comet, SDSC] 235. Haight, R., T. Gershon, O. Gunawan, P. Antunez, D. Bishop, Y. S. Lee, T. Gokmen, K. Sardashti, E. Chagarov, and A. Kummel (2017), Industrial perspectives on earth abundant, multinary thin film photovoltaics, Semiconductor Science and Technology, 32(3), 033004, doi:10.1088/1361-6641/aa5c18. (published) [Comet, SDSC] 236. Park, S. W., H. Kim, E. Chagarov, S. Siddiqui, B. Sahu, N. Yoshida, J. Kachian, R. Feenstra, and A. C. Kummel (2016), Chemically selective formation of Si–O–Al on SiGe(110) and (001) for ALD nucleation using H 2 O 2 ( g ), Surface Science, 652, 322–333, doi:10.1016/j.susc.2016.01.009. (published) [Comet, SDSC] 237. Sardashti, K., E. Chagarov, P. D. Antunez, T. S. Gershon, S. T. Ueda, T. Gokmen, D. Bishop, R. Haight, and A. C. Kummel (2017), Nanoscale Characterization of Back Surfaces and Interfaces in Thin-Film Kesterite Solar Cells, ACS Applied Materials & Interfaces, 9(20), 17024–17033, doi:10.1021/acsami.7b01838. (published) [Comet, SDSC] 238. E. Chagarov, K. Sardashti, A.C. Kummel, Y.S. Lee, R. Haight, T.S. Gershon, “Ag2ZnSn(S,Se)4: A highly promising absorber for thin film photovoltaics”, J. Chem. Phys. 144, 104704 (2016). 239. E.A. Chagarov, L. Porter, A.C. Kummel, “Density-functional theory molecular dynamics simulations of a- HfO2/Ge(100)(2 * 1) and a-ZrO2/Ge(100)(2 * 1) interface passivation.” J. Chem. Phys. 144, 084704 (2016). 240. J.H. Park, H.C.P. Movva, E. Chagarov, K. Sardashti, H. Chou, I. Kwak, K-T Hu, S.K. Fullerton-Shirey, P. Choudhury, S.K. Banerjee, A.C. Kummel, “In Situ Observation of Initial Stage in Dielectric Growth and Deposition of Ultrahigh Nucleation Density Dielectric on Two-Dimensional Surfaces”, Nano Letters 15, 6626 (2015). 241. K. Sardashti, R. Haight, T. Gokmen, W. Wang, L.-Y. Chang, D. B. Mitzi, A. C. Kummel, "Impact of Nanoscale Elemental Distribution in High-Performance Kesterite Solar Cells", Advanced Energy Materials 5, 1402180 (2015) 242. M. Edmonds, T. Kent, E. Chagarov, K. Sardashti, R. Droopad, M. Chang, J. Kachian, J.H. Park, A.C. Kummel, “Passivation of InGaAs(001)-(2 x 4) by Self-Limiting Chemical Vapor Deposition of a Silicon Hydride Control Layer”, J. Amer. Chem. Soc. 137, 8526 (2015). 243. T. Kent, E. Chagarov, M. Edmonds, R. Droopad, A.C. Kummel, “Dual Passivation of Intrinsic Defects at the Compound Semiconductor/Oxide Interface Using an Oxidant and a Reductant”, ACS Nano 9, 4843 (2015). 244. S. W. Park, T. Kaufman-Osborn, H. Kim, S. Siddiqui, B. Sahu, N. Yoshida, A. Brandt, and A. C. Kummel, “Combined Wet and Dry Cleaning of SiGe(001)” JVST A 33(4), 041403 (2015). 245. M. Edmonds, T.J. Kent, S. Wolf, K. Sardashti, M. Chang, J. Kachian, R. Droopad, E. Chagarov, A.C. Kummel, “In0.53Ga0.47As(001)-(2x4) and Si0.5Ge0.5(110) surface passivation by self-limiting deposition of silicon
RY2 IPR 4 Page 108 containing control layers”, 2016 International Symposium on VLSI Technology, Systems and Application (VLSI-TSA), pages 1-2 (2016). 48. TG-CHE120023 246. Choudhuri, J. R., D. Vanzo, P. A. Madden, M. Salanne, D. Bratko, and A. Luzar (2016), Dynamic Response in Nanoelectrowetting on a Dielectric, ACS Nano, 10(9), 8536–8544, doi:10.1021/acsnano.6b03753. (published) [Comet, Gordon, SDSC] 247. Jabes, B. S., J. Driskill, D. Vanzo, D. Bratko, and A. Luzar (2017), Metastable Vapor in a Janus Nanoconfinement, The Journal of Physical Chemistry C, 121(24), 13144–13150, doi:10.1021/acs.jpcc.7b02147. (published) [Comet, Gordon, HPSS, SDSC] 49. TG-CHE120052 248. Chen, M. Z., O. Gutierrez, and A. B. Smith (2013), Through-Bond/Through-Space Anion Relay Chemistry Exploiting Vinylepoxides as Bifunctional Linchpins, Angewandte Chemie International Edition, 53(5), 1279– 1282, doi:10.1002/anie.201309270. (published) 249. Desrosiers, J.-N. et al. (2017), Enantioselective Nickel-Catalyzed Mizoroki–Heck Cyclizations To Generate Quaternary Stereocenters, Organic Letters, 19(13), 3338–3341, doi:10.1021/acs.orglett.7b01054. (published) [Gordon, SDSC] 250. Li, M. et al. (2017), Transition-metal-free chemo- and regioselective vinylation of azaallyls, Nature Chemistry, 9(10), 997–1004, doi:10.1038/nchem.2760. (published) 251. Li, M., Gutierrez, O., Berritt, S., Pascual-Escudero, A., Yeşilçimen, A., et al. 2017. Transition-metal-free chemo- and regioselective vinylation of azaallyls. Nature Chemistry. (published) [Gordon, SDSC] 252. Metz, A. E., K. Ramalingam, and M. C. Kozlowski (2015), Xanthene-4,5-diamine derivatives: a study of anion- binding catalysis, Tetrahedron Letters, 56(37), 5180–5184, doi:10.1016/j.tetlet.2015.07.058. (published) [Gordon, SDSC] 253. Wanner, B., I. Kreituss, O. Gutierrez, M. C. Kozlowski, and J. W. Bode (2015), Catalytic Kinetic Resolution of Disubstituted Piperidines by Enantioselective Acylation: Synthetic Utility and Mechanistic Insights, Journal of the American Chemical Society, 137(35), 11491–11497, doi:10.1021/jacs.5b07201. (published) [Gordon, SDSC] 254. Wei, X. et al. (2016), Sequential C–H Arylation and Enantioselective Hydrogenation Enables Ideal Asymmetric Entry to the Indenopiperidine Core of an 11β-HSD-1 Inhibitor, Journal of the American Chemical Society, 138(47), 15473–15481, doi:10.1021/jacs.6b09764. (published) [Gordon, SDSC] 50. TG-CHE120088 255. Hermes, E., Janes, A., Schmidt, J. 2017. Mechanistic insights into solution-phase oxidative esterification of primary alcohols on Pd(111) from first-principles microkinetic modeling. ACS Catalysis. (submitted) [Stampede, TACC] 256. Van Vleet, M., Misquitta, A., Schmidt, J. 2017. New angles on standard force fields: a general approach for incorporating atomic-level anisotropy. Journal of Computational and Theoretical Chemistry. (in preparation) 257. Wu, T., Stone, M., Shearer, M., Stolt, M., Cabán-Acevedo, M., et al. 2017. Crystallographic Facet Dependence of Hydrogen Evolution Reaction Catalysis Activity by CoPS: Theory and Experiments. Journal of the American Chemical Society. (in preparation) 258. Yang, X., Hermes, E., Ying, D., Li, W., Schmidt, J., et al. 2017. Selective reduction of O2 to H2O2 over a CoS2 electrocatalyst. Journal of the American Chemical Society. (in preparation) 51. TG-CHE130010 259. Martínez, J. P., D. E. Trujillo-González, A. W. Götz, F. L. Castillo-Alvarado, and J. I. Rodríguez (2017), Effects of Dispersion Forces on Structure and Photoinduced Charge Separation in Organic Photovoltaics, The Journal of Physical Chemistry C, 121(37), 20134–20140, doi:10.1021/acs.jpcc.7b05107. (published) [Comet, Data Oasis, Gordon, SDSC, Stampede, TACC] 260. Steinmann, S. N., P. Fleurat-Lessard, A. W. Götz, C. Michel, R. Ferreira de Morais, and P. Sautet (2017), Molecular mechanics models for the image charge, a comment on “including image charge effects in the molecular dynamics simulations of molecules on metal surfaces,” Journal of Computational Chemistry, 38(24), 2127–2129, doi:10.1002/jcc.24861. (published) [Comet, Gordon, SDSC] 261. Steinmann, S., Fleurat-Lessard, P., Götz, A., Michel, C., Ferreira de Morais, R., et al. 2017. Molecular mechanics models for the image charge, a comment on "Including image charge effects in the molecular dynamics
RY2 IPR 4 Page 109 simulations of molecules on metal surfaces. Journal of Computational Chemistry. https://dx.doi.org/10.1002/jcc.24861 (published) [Comet, SDSC, Stampede, TACC] 262. T. Pirojsirikul, A. W. Götz, J. Weare, R. C. Walker, K. Kowalski, M. Valiev, “Combined quantum-mechanical molecular mechanics calculations with NWChem and AMBER: Excited state properties of green fluorescent protein chromophore in aqueous solution”, J. Comput. Chem. 38 (2017) 1631–1639. 263. J. S. M. Anderson, J. I. Rodríguez, P. W. Ayers, A. W. Götz, “Relativistic (SR-ZORA) quantum theory of atoms in molecules (QTAIM) properties”, J. Comput. Chem. 38 (2016) 81–86. 264. L. Yang, A. A. Skjevik, W.-G. Han Du, L. Noodleman, R. C. Walker, A. W. Götz, “Water exit pathways and proton pumping mechanism in B-type Cytochrome c oxidase from molecular dynamics simulations”, BBA, Bioenergetics 1857 (2016) 1594–1606. 265. L. Yang, A. A. Skjevik, W.-G. Han Du, L. Noodleman, R. C. Walker, A. W. Götz, “Data for molecular dynamics simulations of B-type Cytochrome c oxidase with the Amber force field”, Data in Brief 8 (2016) 1209–1214. 266. W.-G. Han Du, A. W. Götz, L. Yang, R. C. Walker, L. Noodleman, “A broken-symmetry density functional study of structure, energies, and protonation states along the catalytic O-O bond cleavage pathway in ba3 cytochrome c oxidase from thermus thermophiles”, Phys. Chem. Chem. Phys. 18 (2016) 21162–21171. 267. J. I. Rodríguez, C. Matta, E. Uribe, A. W. Götz, F. L. Castillo-Alvarado, B. Molina-Brito, “A QTAIM topological analysis of the P3HT–PCBM dimer”, Chem. Phys. Lett. 644 (2016) 157–162. 268. S. A. Yao, V. Martin-Diaconescu, I. Infante, K. M. Lancaster, A. W. Götz, S. DeBeer, J. F. Berry, “Electronic structure of Ni2E2 complexes (E = S, Se, Te) and a global analysis of M2E2 compounds: a case for quantized E2 n- oxidation levels with n = 2, 3, or 4”, J. Am. Chem. Soc. 137 (2015) 4993–5011. 269. L. Mones, A. Jones, A. W. Götz, T. Laino, R. C. Walker, B. Leimkuhler, G. Csányi, N. Bernstein, “The adaptive buffered force QM/MM method in the CP2K and AMBER software packages”, J. Comp. Chem. 36 (2015) 633– 648. 52. TG-CHE130094 270. Mensah, R.; Lyon, J. T. “Structures and Properties of Silicon Clusters Doped with Two Silver Atoms, SinAg2 (n = 1 – 15)” Journal of Physical Chemistry A 2017, to be submitted in August as an invited article. 271. Dore, E. M.; Lyon, J. T. “The Structures of Silicon Clusters Doped with Two Gold Atoms, SinAu2 (n = 1 - 10)” Journal of Cluster Science 2016, 27, 1365-1381. 272. Lyon, J. T.; Cho, H.-G.; Andrews, L. “Matrix Infrared Spectroscopic and Theoretical Studies of the Reactions Between Group 5 Transition Metals and CX4 Molecules (X = H, F, and Cl)” Journal of Physical Chemistry A 2015, 119, 12742-12755. 273. Li, Y.; Lyon, J. T.; Woodham, A.; Lievens, P.; Fielicke, A.; Janssens, E. “Structural Identification of Gold Doped Silicon Clusters via Far-Infrared Spectroscopy” Journal of Physical Chemistry C 2015, 119, 10896-10903. 53. TG-CHE130099 274. Drenscko, M., and S. M. Loverde (2016), Characterisation of the hydrophobic collapse of polystyrene in water using free energy techniques, Molecular Simulation, 43(3), 234–241, doi:10.1080/08927022.2016.1253840. (published) 54. TG-CHE130101 275. Uhler, B., M. V. Ivanov, D. Kokkin, N. Reilly, R. Rathore, and S. A. Reid (2017), Effect of Facial Encumbrance on Excimer Formation and Charge Resonance Stabilization in Model Bichromophoric Assemblies, The Journal of Physical Chemistry C, 121(29), 15580–15588, doi:10.1021/acs.jpcc.7b04255. (published) [Comet, SDSC] 55. TG-CHE130120 276. Kłos, J., M. H. Alexander, P. J. Dagdigian, and Q. Ma (2017), The interaction of NO(X2Π) with H2: Ab initio potential energy surfaces and bound states, The Journal of Chemical Physics, 146(11), 114301, doi:10.1063/1.4977992. (published) [Gordon, Stampede, TACC] 56. TG-CHE140052 277. Fischer, L. J., A. S. Dutton, and A. H. Winter (2017), Anomalous effect of non-alternant hydrocarbons on carbocation and carbanion electronic configurations, Chem. Sci., 8(6), 4231–4241, doi:10.1039/c7sc01047h. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede, TACC]
RY2 IPR 4 Page 110 278. Geraskina, M. R., A. S. Dutton, M. J. Juetten, S. A. Wood, and A. H. Winter (2017), The Viologen Cation Radical Pimer: A Case of Dispersion-Driven Bonding, Angewandte Chemie International Edition, 56(32), 9435–9439, doi:10.1002/anie.201704959. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede, TACC] 57. TG-CHE140070 279. Duster, A.; Wang, C.-H.; Garza, C.; Miller, D.; Lin, H. " Adaptive QM/MM: Where are we, what have we learned, and where will we go from here?" Wiley Interdisciplinary Reviews: Computational Molecular Science, 2017, in press. DOI: 10.1002/wcms.1310 280. Duster, A.; Garza, C.; Lin, H. “Adaptive partitioning QM/MM dynamics simulations for substrate uptake, product release, and solvent exchange,” In Voth, G. A. ed. Computational Approaches for Studying Enzyme Mechanism, or the "Methods in Enzymology" series, Elsevier, 2016, pp 342-358. DOI: 10.1016/bs.mie.2016.05.019. 58. TG-CHE140075 281. Wang, S., and E. Iglesia (2017), Experimental and Theoretical Evidence for the Reactivity of Bound Intermediates in Ketonization of Carboxylic Acids and Consequences of Acid–Base Properties of Oxide Catalysts, The Journal of Physical Chemistry C, 121(33), 18030–18046, doi:10.1021/acs.jpcc.7b05987. (published) [Comet, Gordon, SDSC, Stampede, TACC] 282. Wang, S., and E. Iglesia (2017), Catalytic diversity conferred by confinement of protons within porous aluminosilicates in Prins condensation reactions, Journal of Catalysis, 352, 415–435, doi:10.1016/j.jcat.2017.06.012. (published) [Comet, Gordon, SDSC, Stampede, TACC] 59. TG-CHE140101 283. Shu, Y., B. S. Fales, W.-T. Peng, and B. G. Levine (2017), Understanding Nonradiative Recombination through Defect-Induced Conical Intersections, The Journal of Physical Chemistry Letters, 8(17), 4091–4099, doi:10.1021/acs.jpclett.7b01707. (published) [Maverick, TACC] 60. TG-CHE140116 284. Cook, E., Sun, B., Kekenes-Huskey, P., Creamer, T. 2016. Electrostatic Forces Mediate Fast Association of Calmodulin and the Intrinsically Disordered Regulatory Domain of Calcineurin. Arxiv preprint arXiv:1611.04080. (submitted) 285. Siddiqui, J., Tikunova, S., Walton, S., Liu, B., Meyer, M., et al. 2016. Myofilament Calcium Sensitivity: Consequences of the Effective Concentration of Troponin I. Frontiers in Physiology 7. (accepted) 286. Stewart, B., Scott, C., McCoy, T., Yin, G., Despa, F., et al. 2017. Characterization of amylin-induced calcium dysregulation in rat ventricular cardiomyocytes. arXiv preprint arXiv:1704.03353. (submitted) 61. TG-CHE140146 287. Shelley, M. Y., M. E. Selvan, J. Zhao, V. Babin, C. Liao, J. Li, and J. C. Shelley (2017), A New Mixed All- Atom/Coarse-Grained Model: Application to Melittin Aggregation in Aqueous Solution, Journal of Chemical Theory and Computation, 13(8), 3881–3897, doi:10.1021/acs.jctc.7b00071. (published) 62. TG-CHE150078 288. Poblete, H., I. Miranda-Carvajal, and J. Comer (2017), Determinants of Alanine Dipeptide Conformational Equilibria on Graphene and Hydroxylated Derivatives, The Journal of Physical Chemistry B, 121(15), 3895– 3907, doi:10.1021/acs.jpcb.7b01130. (published) [LSU, SuperMIC] 63. TG-CHE150093 289. Sode, O., Cherry, J. 2017. Development of a flexible-monomer two-body carbon dioxide potential and its application to clusters up to (CO2)13. Journal of Computational Chemistry . (submitted) [Stampede, TACC] 64. TG-CHE160003 290. Butler, K. T., C. H. Hendon, and A. Walsh (2017), Designing porous electronic thin-film devices: band offsets and heteroepitaxy, Faraday Discuss., 201, 207–219, doi:10.1039/c7fd00019g. (published) [Comet, SDSC]
RY2 IPR 4 Page 111 291. Dou, J.-H., L. Sun, Y. Ge, W. Li, C. H. Hendon, J. Li, S. Gul, J. Yano, E. A. Stach, and M. Dincă (2017), Signature of Metallic Behavior in the Metal–Organic Frameworks M3(hexaiminobenzene)2 (M = Ni, Cu), Journal of the American Chemical Society, 139(39), 13608–13611, doi:10.1021/jacs.7b07234. (published) [Comet, SDSC] 292. Dubey, R. J.-C., R. J. Comito, Z. Wu, G. Zhang, A. J. Rieth, C. H. Hendon, J. T. Miller, and M. Dincă (2017), Highly Stereoselective Heterogeneous Diene Polymerization by Co-MFU-4l: A Single-Site Catalyst Prepared by Cation Exchange, Journal of the American Chemical Society, 139(36), 12664–12669, doi:10.1021/jacs.7b06841. (published) [Comet, SDSC] 293. Hendon, C. H., A. J. Rieth, M. D. Korzyński, and M. Dincă (2017), Grand Challenges and Future Opportunities for Metal–Organic Frameworks, ACS Central Science, 3(6), 554–563, doi:10.1021/acscentsci.7b00197. (published) [Comet, SDSC, Stampede, TACC] 65. TG-CHE160021 294. Van Dyck, C., T. J. Marks, and M. A. Ratner (2017), Chain Length Dependence of the Dielectric Constant and Polarizability in Conjugated Organic Thin Films, ACS Nano, 11(6), 5970–5981, doi:10.1021/acsnano.7b01807. (published) [Comet] 66. TG-CHE160043, TG-DMR140104 295. Starovoytov, O. N., P. Zhang, P. Cieplak, and M. S. Cheung (2017), Induced polarization restricts the conformational distribution of a light-harvesting molecular triad in the ground state, Phys. Chem. Chem. Phys., 19(34), 22969–22980, doi:10.1039/c7cp03177g. (published) 67. TG-CHE160054 296. Baxter, E. T., M.-A. Ha, A. C. Cass, A. N. Alexandrova, and S. L. Anderson (2017), Ethylene Dehydrogenation on Pt4,7,8 Clusters on Al2O3: Strong Cluster Size Dependence Linked to Preferred Catalyst Morphologies, ACS Catalysis, 7(5), 3322–3335, doi:10.1021/acscatal.7b00409. (published) [Bridges Large, Bridges Regular, Gordon, Stampede, TACC] 68. TG-CHE160082 297. Lee, W., Jun, Z., Osvaldo, G. 2017. Mechanism of Nakamura’s Iron-Catalyzed Asymmetric Cross- Coupling Reaction: The Role of Spin in Controlling Selectivity. Journal of American Chemical Society. (submitted) [Gordon, SDSC] 69. TG-CHE170020 298. Izzo, J., Macharia, J., Kim, S., Hirschi, J., Vetticatt, M. 2017. Kinetic Isotope Effects and the Mechanism of the Suzuki-Miyaura Reaction. A poster was presented detailing the work done, experimentally and computationally, on the elucidation of the historic Suzuki-Miyaura reaction.. Gordon Research Conference - Physical Organic Chemistry. (published) [Comet, Data Oasis, SDSC] 70. TG-CIE160022 299. Cao, H., Q. Ma, X. Chen, and Y. Xu (2017), DOOR: a prokaryotic operon database for genome analyses and functional inference, Briefings in Bioinformatics, doi:10.1093/bib/bbx088. (published) [Bridges Large, PSC, Pylon] 300. Cao, S., T. Sheng, X. Chen, Q. Ma, and C. Zhang (2017), A probabilistic model-based bi-clustering method for single-cell transcriptomic data analysis, , doi:10.1101/181362. (published) [Bridges Large, PSC, Pylon] 71. TG-CTS070067N 301. Akhavan, R., Rastegari, A. 2016. Effect of Interface Curvature on Turbulent Skin-Friction Drag Reduction with Super-Hydrophobic Micro-Grooves.. 69th Annual Meeting of the APS Division of Fluid Dynamics, 61(20), 2016APS..DFDL33001A. AIP. 2016APS..DFDL33001A. (published) 302. Rastegari, A., Akhavan, R. 2016. The Common Mechanism of Turbulent Skin-Friction Drag Reduction with Super-Hydrophobic Micro-Grooves and Riblets. 69th Annual Meeting of the APS Division of Fluid Dynamics, 61(20), 2016APS..DFDL33002R. AIP. 2016APS..DFDL33002R. (published)
RY2 IPR 4 Page 112 72. TG-CTS070070 303. Rastegari, A. & Akhavan, R. (2016) Structure and Dynamics of Turbulence in Super-Hydrophobic Channel Flows, In Progress in Wall Turbulence: Understanding and Modeling, M. Stanislas et al. (eds.), ERCOFTAC Series 23, 367-377, Springer. 304. Tang, Y. & Akhavan, R. (2016) Computation of high Reynolds number equilibrium and non-equilibrium wall- bounded turbulent flows using a nested-LES approach, In Progress in Wall Turbulence: Understanding and Modeling, M. Stanislas et al. (eds.), ERCOFTAC Series 23, 125-135, Springer. 305. A. Rastegari & R. Akhavan (2015) On the mechanism of turbulent drag reduction with super-hydrophobic surfaces, J. Fluid Mech. 773, R4. 306. Y. Tang & R. Akhavan (2016) Computations of equilibrium and non-equilibrium turbulent channel flows using a nested-LES approach, J. Fluid Mech. 973, 709-748. 307. A. Rastegari & R. Akhavan (2017) The common mechanism of turbulent skin-friction drag reduction with super-hydrophobic longitudinal micro-grooves and riblets, J. Fluid Mech. (submitted). 73. TG-CTS090025 308. Vo, M. D., and D. V. Papavassiliou (2017), Interaction between polymer-coated carbon nanotubes with coarse- grained computations, Chemical Physics Letters, 685, 77–83, doi:10.1016/j.cplett.2017.07.037. (published) [Stampede, TACC] 309. Vo, M. D., and D. V. Papavassiliou (2017), Effects of Temperature and Shear on the Adsorption of Surfactants on Carbon Nanotubes, The Journal of Physical Chemistry C, 121(26), 14339–14348, doi:10.1021/acs.jpcc.7b03904. (published) [Stampede, TACC] 74. TG-CTS100024 310. Dodd M.S. & Ferrante A. (2016) "On the interaction of Taylor lengthscale size droplets and turbulence" Journal of Fluid Mechanics, Vol. 806, pp. 356-412 Featured article of "Droplets in turbulence: a new perspective" by Prof. M. Maxey in Focus on Fluids of J. Fluid Mechanics, Vol. 816 (2017) 75. TG-CTS120005 311. Maeda, K., Colonius, T. 2017. Modeling and Numerical Simulations of Bubble Cloud Dynamics in a Focused Ultrasound Field. The 3rd International Conference on Numerical Methods in Multiphase Flows (ICNMMF-3) (Tokyo (Japan)). (published) 312. Maeda, K., T. Colonius, W. Kreider, A. Maxwell, and M. Bailey (2016), Modeling and experimental analysis of acoustic cavitation bubble clouds for burst-wave lithotripsy, The Journal of the Acoustical Society of America, 140(4), 3307–3307, doi:10.1121/1.4970532. (published) 313. A. Goza, S. Liska, B. Morley, T. Colonius, \Accurate computation of surface stresses and forces with immersed boundary methods," Journal of Computational Physics, vol. 321, pp. 860-873, 2016. 314. S. Liska, T. Colonius, “A fast lattice Green's function method for solving viscous incompressible flows on unbounded domains," Journal of Computational Physics, vol. 316, pp. 360-384, 2016. 315. S. Liska, T. Colonius, “A fast immersed boundary method for external incompressible viscous flows using lattice Green's functions," Journal of Computational Physics, vol. 331, pp. 257-279, 2017. 316. T. Jardin, T. Colonius, “On the lift optimal aspect ratio of a revolving wing at low Reynolds number," Physical Review Fluid, under review. 317. K. Maeda, T. Colonius, W. Kreider, A.D. Maxwell and M. Bailey, “Quantification of the shielding of kidney stones by bubble clouds during burst wave lithotripsy", The Journal of the Acoustical Society of America, vol. 140, pp. 3673, 2017 318. K. Maeda and T. Colonius, “A Source Term Approach for Generation of One-way Acoustic Waves in the Euler and Navier-Stokes equations", submitted 76. TG-CTS130034 319. Ramachandra, A. B., A. M. Kahn, and A. L. Marsden (2016), Patient-Specific Simulations Reveal Significant Differences in Mechanical Stimuli in Venous and Arterial Coronary Grafts, Journal of Cardiovascular Translational Research, 9(4), 279–290, doi:10.1007/s12265-016-9706-0. (published) [Comet, SDSC] 320. Schiavazzi, D. E., A. Baretta, G. Pennati, T.-Y. Hsia, and A. L. Marsden (2016), Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty, International Journal for
RY2 IPR 4 Page 113 Numerical Methods in Biomedical Engineering, 33(3), e02799, doi:10.1002/cnm.2799. (published) [Comet, SDSC] 321. Schiavazzi, D. E., A. Doostan, G. Iaccarino, and A. L. Marsden (2017), A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling, Computer Methods in Applied Mechanics and Engineering, 314, 196–221, doi:10.1016/j.cma.2016.09.024. (published) [Comet, SDSC] 322. Tran, J. S., D. E. Schiavazzi, A. B. Ramachandra, A. M. Kahn, and A. L. Marsden (2017), Automated tuning for parameter identification and uncertainty quantification in multi-scale coronary simulations, Computers & Fluids, 142, 128–138, doi:10.1016/j.compfluid.2016.05.015. (published) [Comet, SDSC] 323. Updegrove, A., N. M. Wilson, J. Merkow, H. Lan, A. L. Marsden, and S. C. Shadden (2016), SimVascular: An Open Source Pipeline for Cardiovascular Simulation, Annals of Biomedical Engineering, 45(3), 525–541, doi:10.1007/s10439-016-1762-8. (published) [Comet, SDSC] 77. TG-CTS130035 324. Johnson, L., Landrum, B., Zia, R. 2017. Yield of reversible colloidal gels during flow startup: the role of Brownian and glassy dynamics. In preparation for Soft Matter. (published) [Ranch, Stampede, TACC] 325. Johnson, L., Moghimi, E., Petekidis, G., Zia, R. 2017. Microstructural evolution during startup yield and flow in colloidal gels. In preparation for J. Rheol.. (published) [Ranch, Stampede, TACC] 326. Johnson, L., Moghimi, E., Petekidis, G., Zia, R. 2017. Influence of structure on the linear response rheology of colloidal gels. In preparation for J. Rheol.. (published) [Ranch, Stampede, TACC] 327. Landrum, B., Russel, W., Zia, R. 2016. Delayed yield in colloidal gels: Creep, flow, and re-entrant solid regimes. J. Rheol. 60: 783--807. (published) [Ranch, Stampede, TACC] 328. Padmanabhan, P., Zia, R. 2017. Gravitational collapse of reversible colloidal gels: a non-equilibrium phase transition. In preparation for Soft Matter. (published) [Ranch, Stampede, TACC] 329. Peng, X., Li, Q., McKenna, G., Wang, J., Zia, R. 2017. Time-concentration superposition in colloidal dispersion after volume-fraction jumps. In preparation for Phys. Rev. Lett.. (published) [Ranch, Stampede, TACC] 330. Wang, J., Peng, X., Li, Q., McKenna, G., Zia, R. 2017. Dynamics of colloidal glass transition via novel volume- fraction jumps: towards the non-existence of an ideal glass transition. In preparation for Phys. Rev. Lett.. (published) [Ranch, Stampede, TACC] 331. Wang, J., Zia, R. 2017. A detailed structural description of colloidal glass transition. In preparation for Soft Matter. (published) [Ranch, Stampede, TACC] 332. Zia, R., Landrum, B., Russel, W. 2014. A micro-mechanical study of coarsening and rheology of colloidal gels: Cage building, cage hopping, and Smoluchowski’s ratchet. Journal of Rheology 58: 1121--1157. (published) [Ranch, Stampede, TACC] 78. TG-CTS140009 333. He, Y., and S. Laursen (2017), Trends in the Surface and Catalytic Chemistry of Transition-Metal Ceramics in the Deoxygenation of a Woody Biomass Pyrolysis Model Compound, ACS Catalysis, 7(5), 3169–3180, doi:10.1021/acscatal.6b02806. (published) [Stampede, TACC] 79. TG-CTS140025 334. Fujioka, H. (2013), A continuum model of interfacial surfactant transport for particle methods, Journal of Computational Physics, 234, 280–294, doi:10.1016/j.jcp.2012.09.041. (published) [GaTech, Keenland] 80. TG-CTS150005 335. P. Deshlahra and E. Iglesia, “Reactivity and Selectivity Descriptors for the Activation of C-H Bonds in Hydrocarbons and Oxygenates on Metal Oxides,” J. Phys. Chem. C., 2016, 120, 16741-16760. 336. P. Deshlahra and E. Iglesia, “Towards More Complete Reactivity Descriptors for Acid Catalysis,” ACS. Catal. 2016, 6, 5386-5392. 337. P. Deshlahra, R. Carr, S.-H. Chai and E. Iglesia*, “Mechanistic Details and Reactivity Descriptors for Acid- Oxidation Reactions of Methanol on Bifunctional Keggin Polyoxometalate Clusters,” ACS Catal. 2015, 5, 666- 682.
RY2 IPR 4 Page 114 81. TG-CTS150038 338. Taifan, W., A. A. Arvidsson, E. Nelson, A. Hellman, and J. Baltrusaitis (2017), CH4 and H2S reforming to CH3SH and H2 catalyzed by metal-promoted Mo6S8 clusters: a first-principles micro-kinetic study, Catal. Sci. Technol., 7(16), 3546–3554, doi:10.1039/c7cy00857k. (published) [Comet, SDSC] 339. Taifan, W. E., G. X. Yan, and J. Baltrusaitis (2017), Surface chemistry of MgO/SiO2 catalyst during the ethanol catalytic conversion to 1,3-butadiene: in-situ DRIFTS and DFT study, Catal. Sci. Technol., 7(20), 4648–4668, doi:10.1039/c7cy01556a. (published) [Comet] 82. TG-CTS150057 340. Akhade, S. A., N. J. Bernstein, M. R. Esopi, M. J. Regula, and M. J. Janik (2017), A simple method to approximate electrode potential-dependent activation energies using density functional theory, Catalysis Today, 288, 63– 73, doi:10.1016/j.cattod.2017.01.050. (published) [Stampede, TACC] 341. McCrum, I. T., M. A. Hickner, and M. J. Janik (2017), First-Principles Calculation of Pt Surface Energies in an Electrochemical Environment: Thermodynamic Driving Forces for Surface Faceting and Nanoparticle Reconstruction, Langmuir, 33(28), 7043–7052, doi:10.1021/acs.langmuir.7b01530. (published) [Stampede, TACC] 342. McCrum, I. T., and M. J. Janik (2017), Deconvoluting Cyclic Voltammograms To Accurately Calculate Pt Electrochemically Active Surface Area, The Journal of Physical Chemistry C, 121(11), 6237–6245, doi:10.1021/acs.jpcc.7b01617. (published) [Stampede, TACC] 343. Spanjers, C. S., A. Dasgupta, M. Kirkham, B. A. Burger, G. Kumar, M. J. Janik, and R. M. Rioux (2017), Determination of Bulk and Surface Atomic Arrangement in Ni–Zn γ-Brass Phase at Different Ni to Zn Ratios, Chemistry of Materials, 29(2), 504–512, doi:10.1021/acs.chemmater.6b01769. (published) [Stampede, TACC] 83. TG-CTS150062 344. Kanani, Y., S. Acharya, and F. Ames (2017), Simulations of Slot Film-Cooling With Freestream Acceleration and Turbulence, Volume 5A: Heat Transfer, doi:10.1115/gt2017-65050. (published) 84. TG-CTS160041 345. Pavlo, K., Deluca, M., Hibbitts, D. 2017. Theoretical Insights into the Paring Mechanism and Its Intermediates in Formation of Light Alkenes. Journal of Catalysis. (in preparation) 346. Pavlo, K., Deluca, M., Hibbitts, D. 2017. Computational Investigations of Arene Methylation over H-MFI. ACS Catalysis. (in preparation) 85. TG-DBS160004 347. Ide, N., Pustejovsky, J., Cieri, C., Nyberg, E., Wang, D., et al. 2014. The Language Application Grid. Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14). (published) [Jetstream] 348. Ide, N., Pustejovsky, J., Keith, S., Marc, V., Cieri, C., et al. 2016. The Language Application Grid and Galaxy. Language Resources and Evaluation Conference (Potoroz, Slovenia). n/a. n/a. http://www.lrec- conf.org/proceedings/lrec2016/pdf/1228_Paper.pdf. (published) [Jetstream] 86. TG-DDM160002 349. Bucher, T., Young, A., Zhang, M., Chen, C., Yao, Y. 2017. Bending mechanism analysis for laser forming of metal foam. Manufacturing Science and Engineering Conference (Los Angeles). (published) [Stampede, TACC] 87. TG-DEB160003 350. Memarzadeh, M., Boettiger, C. 2016. Measurement uncertainty matters: ecological management using POMDPs. . http://dx.doi.org/10.1101/055319 DOI:10.1101/055319 (Invalid?). (published) [IU, Jetstream, TACC] 88. TG-DMR000003 351. Boruah N. & Dimitrakopoulos P. “Motion and deformation of a droplet in a microfluidic crossjunction”, J. Colloid Interface Sci., 453 216–225 (2015). 352. Koolivand A. & Dimitrakopoulos P. “Deformation of an elastic capsule in a microfluidic T-junction: settling shape and moduli determination”, Microfluidics and Nanofluidics, 21, 89 (2017).
RY2 IPR 4 Page 115 353. Dimitrakopoulos P. “Dumbbell formation for elastic capsules in nonlinear extensional Stokes flows”, Phys. Rev. Fluids, 2, 063101 (2017). 354. Dimitrakopoulos P. “Cusp formation for capsules in nonlinear extensional Stokes flows”, J. Fluid Mech., under review (2017). 355. Wang Yiyang & Dimitrakopoulos P. “Lateral capsule migration in a converging micro-capillary”, Europhys. Lett., under review (2017). 89. TG-DMR080007 356. S.L. Moffitt, Q. Zhu, Q. Ma, A.F. Falduto, D.B. Buchholz, R.P.H. Chang, T.O. Mason, J.E. Medvedeva, T.J. Marks, and M.J. Bedzyk, Probing the unique role of gallium in amorphous oxide semiconductors through structure- property relationships, accepted to Advanced Electronic Materials 357. J.E. Medvedeva, D.B. Buchholz, R.P.H. Chang, Recent advances in understanding the structure and properties of amorphous oxide semiconductors, Advanced Electronic Materials, in press (2017) 358. N. Medvedeva, D. Van Aken, J. Medvedeva, First-principles study of phosphorus embrittlement in austenitic steels with kappa-carbide precipitates, Computational Material Science, 138, 105-110 (2017) 359. A.T. Swesi, J. Masud, W.P.R. Liyanage, E. Bohannon, J.E. Medvedeva, M. Nath, Textured NiSe2 Film: Bifunctional Electrocatalyst for Full Water Splitting at Remarkably Low Overpotential with High Energy Efficiency, Scientific Reports, 7, 2401 (2017) 360. A. Choudhury, S. Mohapatra, H. Yaghoobnejad Asl, S.H. Lee, Y.S. Hor, J.E. Medvedeva, D. L. McClane, G.E. Hilmas, M.A. McGuire, A.F. May, H. Wang, S. Dash, A. Welton, P. Boolchand, K.P. Devlin, J. Aitken, R. Herbst-Irmere, Váčlav Petříček, New Insights into the structure, chemistry and properties of Cu4SnS4, Journal of Solid State Chemistry, 253, 192-201 (2017) 361. B. J. Lawson, Paul Corbae, Gang Li, Fan Yu, Tomoya Asaba, Colin Tinsman, Y. Qiu, J. E. Medvedeva, Y. S. Hor, and Lu Li, Multiple Fermi surfaces in superconducting Nb-doped Bi2Se3, Phys. Rev. B 94, 041114(R)-Rapid Communications (2016) 90. TG-DMR080058N 362. Baldwin, A. F., T. D. Huan, R. Ma, A. Mannodi-Kanakkithodi, M. Tefferi, N. Katz, Y. Cao, R. Ramprasad, and G. A. Sotzing (2015), Rational Design of Organotin Polyesters, Macromolecules, 48(8), 2422–2428, doi:10.1021/ma502424r. (published) [Stampede, TACC] 363. Baldwin, A. F. et al. (2014), Poly(dimethyltin glutarate) as a Prospective Material for High Dielectric Applications, Advanced Materials, 27(2), 346–351, doi:10.1002/adma.201404162. (published) [Stampede, TACC] 364. Mannodi-Kanakkithodi, A., Wang, C., Ramprasad, R. 2014. Compounds based on Group 14 elements: building blocks for advanced insulator dielectrics design. Journal of Materials Science 2: 801-807. http://dx.doi.org/10.1007/s10853-014-8640-2 DOI:10.1007/s10853-014-8640-2 (Invalid?). (published) [Stampede, TACC] 365. Misra, M., A. Mannodi-Kanakkithodi, T. C. Chung, R. Ramprasad, and S. K. Kumar (2016), Critical role of morphology on the dielectric constant of semicrystalline polyolefins, The Journal of Chemical Physics, 144(23), 234905, doi:10.1063/1.4953182. (published) [Stampede, TACC] 366. Pilania, G., A. Mannodi-Kanakkithodi, B. P. Uberuaga, R. Ramprasad, J. E. Gubernatis, and T. Lookman (2016), Machine learning bandgaps of double perovskites, Scientific Reports, 6(1), doi:10.1038/srep19375. (published) [Stampede, TACC] 367. Treich, G. M., S. Nasreen, A. Mannodi Kanakkithodi, R. Ma, M. Tefferi, J. Flynn, Y. Cao, R. Ramprasad, and G. A. Sotzing (2016), Optimization of Organotin Polymers for Dielectric Applications, ACS Applied Materials & Interfaces, 8(33), 21270–21277, doi:10.1021/acsami.6b04091. (published) [Stampede, TACC] 368. Treich, G. M., M. Tefferi, S. Nasreen, A. Mannodi-Kanakkithodi, Z. Li, R. Ramprasad, G. A. Sotzing, and Y. Cao (2017), A rational co-design approach to the creation of new dielectric polymers with high energy density, IEEE Transactions on Dielectrics and Electrical Insulation, 24(2), 732–743, doi:10.1109/tdei.2017.006329. (published) [Stampede, TACC] 91. TG-DMR090023 369. Xie, W., Marianetti, C., Morgan, D. 2016. Response to letter "Electron correlation and relativity of the 5f electrons in the U-Zr alloy system". Journal of Nuclear Materials 476. (published) 370. Xie, W., Marianetti, C., Morgan, D. 2016. Reply to "Comment on 'Correlation and relativistic effects in U metal and U-Zr alloy: Validation of ab initio approaches'". Physical Review B 93. (published)
RY2 IPR 4 Page 116 92. TG-DMR100005 371. Borys, N. J. et al. (2017), Anomalous Above-Gap Photoexcitations and Optical Signatures of Localized Charge Puddles in Monolayer Molybdenum Disulfide, ACS Nano, 11(2), 2115–2123, doi:10.1021/acsnano.6b08278. (published) [Stampede, TACC] 372. Deng, B. et al. (2017), Efficient electrical control of thin-film black phosphorus bandgap, Nature Communications, 8, 14474, doi:10.1038/ncomms14474. (published) [Stampede, TACC] 373. Koo, J., S. Gao, H. Lee, and L. Yang (2017), Vertical dielectric screening of few-layer van der Waals semiconductors, Nanoscale, 9(38), 14540–14547, doi:10.1039/c7nr04134a. (published) [Stampede, TACC] 374. Pan, Y. et al. (2017), Schottky Barriers in Bilayer Phosphorene Transistors, ACS Applied Materials & Interfaces, 9(14), 12694–12705, doi:10.1021/acsami.6b16826. (published) [Stampede, TACC] 93. TG-DMR100117 375. Zhang, W., A. R. Oganov, A. F. Goncharov, Q. Zhu, S. E. Boulfelfel, A. O. Lyakhov, E. Stavrou, M. Somayazulu, V. B. Prakapenka, and Z. Konopkova (2013), Unexpected Stable Stoichiometries of Sodium Chlorides, Science, 342(6165), 1502–1505, doi:10.1126/science.1244989. (published) 376. Dong, X. et al. (2017), A stable compound of helium and sodium at high pressure, Nature Chemistry, 9(5), 440–445, doi:10.1038/nchem.2716. (published) [Stampede, TACC]
94. TG-DMR110037 377. Ievlev, A. V., J. Jakowski, M. J. Burch, V. Iberi, H. Hysmith, D. C. Joy, B. G. Sumpter, A. Belianinov, R. R. Unocic, and O. S. Ovchinnikova (2017), Building with ions: towards direct write of platinum nanostructures using in situ liquid cell helium ion microscopy, Nanoscale, 9(35), 12949–12956, doi:10.1039/c7nr04417h. (published) [GaTech, IU, LSU, NICS, OSG, PSC, SDSC, Standford, TACC] 378. Jakowski, J., J. Huang, S. Garashchuk, Y. Luo, K. Hong, J. Keum, and B. G. Sumpter (2017), Deuteration as a Means to Tune Crystallinity of Conducting Polymers, The Journal of Physical Chemistry Letters, 8(18), 4333– 4340, doi:10.1021/acs.jpclett.7b01803. (published) [Blacklight, Bridges Large, Bridges Regular, Comet, Data Oasis, Data Supercell, FutureGrid, GaTech, Globus Online, Gordon, HPSS, IU, Jetstream, Keenland, Lonestar, LSU, Mason, Maverick, NICS, OSG, PSC, Pylon, Ranch, SDSC, Stampede, Standford, SuperMIC, TACC, Trestles, Wrangler, XStream] 379. B.Yang, Chance C. Brown, Jingsong Huang, Liam Collins, Xiahan Sang, Raymond R. Unocic, Stephen Jesse, Sergei V. Kalinin, Alex Belianinov, Jacek Jakowski, David B. Geohegan, Bobby G. Sumpter, Kai Xiao, Olga S. Ovchinnikova, Enhancing Ion Migration in Grain Boundaries of Hybrid Organic–Inorganic Perovskites by Chlorine, Adv. Func. Mater. 1700749 (2017), DOI: 10.1002/adfm.201700749 380. C Hu, X Zeng, Y Liu, M Zhou, H Zhao, TM Tritt, J He, J Jakowski, Paul RC Kent, Jingsong Huang, Bobby G Sumpter, Effects of partial La filling and Sb vacancy defects on CoSb3 skutterudites, Physical Review B 95 (16), 165204 (2017) 381. I Savchenko, B Gu, T Heine, J Jakowski, S Garashchuk, Nuclear Quantum Effects on Adsorption of H 2 and Isotopologues on Metal Ions, Chemical Physics Letters, 670, 64-70 (2017) 382. L. Wang, J. Jakowski, S. Garashchuk, B.G. Sumpter, “Understanding how Isotopes Affect Charge Transfer in P3HT/PCBM: A Quantum Trajectory-Electronic Structure Study with Nonlinear Quantum Correction”, J. Chem. Theory and Comput. 12 (9), 4487-450 (2016) 383. Humble, Travis; Ericson, Milton; Jakowski, Jacek; Huang, Jingsong; Britton, Charles; Curtis, Franklin; Dumitrescu, Eugene; Mohiyaddin, Fahd; Sumpter, Bobby, A Computational Workflow for Designing Silicon Donor Qubits, Nanotechnology 27 (42), 424002 (2016) 384. Bing Gu and Sophya Garashchuk, Quantum Dynamics with Gaussian Bases Defined by the Quantum Trajectories, J. Phys. Chem. A, 2016, 120 (19), pp 3023–303 385. Bing GuVitaly RassolovSophya Garashchuk, Symmetrization of the nuclear wavefunctions defined by the quantum trajectory dynamics, Theor Chem Acc . 135:267 (2016) DOI 10.1007/s00214-016-2021-7 386. Han, Longtao, P.S. Krstic, Igor Kaganovich and Roberto Car, "Migration of a carbon adatom on a charged single-walled carbon nanotube", Carbon 116, 174-180 (2017). 387. Novotny, Michal, F. Javier Dominguez Gutierrez, and Predrag S. Krstic,"A computational study of hydrogen detection by borophene", J. of materials chemistry C 5, 6426 (2017). DOI: 10.1039/C7TC00976C 388. Han, Longtao, and Predrag Krstić, "A Path for Synthesis of Boron-Nitride Nanostructures in Volume of Arc Plasma", Nanotechnology 28 (7), 07LT01 (2017).
RY2 IPR 4 Page 117 389. Domínguez-Gutiérrez, F.J.; F. Bedoya, P.S. Krstić, J.P. Allain, A.L. Neff, K. Luitjohan, "Studies of lithiumization and boronization of ATJ graphite PFCs in NSTXU", Nuclear Materials and Energy (Elsevier, 2016), 10.1016/j.nme.2016.12.028 95. TG-DMR110085 390. Giovannetti, G., Puggioni, D., Barone, P., Picozzi, S., Rondinelli, J., et al. 2016. Magnetoelectric coupling in the type-I multiferroic ScFeO3. Physical Review B 19. http://dx.doi.org/10.1103/PhysRevB.94.195116 DOI:10.1103/physrevb.94.195116 (Invalid?). (published) 391. Gu, M., and J. M. Rondinelli (2017), Role of orbital filling on nonlinear ionic Raman scattering in perovskite titanates, Physical Review B, 95(2), doi:10.1103/physrevb.95.024109. (published) 392. Huang, L.-F., M. J. Hutchison, R. J. Santucci, J. R. Scully, and J. M. Rondinelli (2017), Improved Electrochemical Phase Diagrams from Theory and Experiment: The Ni–Water System and Its Complex Compounds, The Journal of Physical Chemistry C, 121(18), 9782–9789, doi:10.1021/acs.jpcc.7b02771. (published) 393. Lu, X.-Z., and J. M. Rondinelli (2016), Room Temperature Electric-Field Control of Magnetism in Layered Oxides with Cation Order, Advanced Functional Materials, 27(4), 1604312, doi:10.1002/adfm.201604312. (published) 394. Hidden magnetic effect on ferroelectric polarization of type-I multiferroic ScFeO3, G. Giovannetti, D. Puggioni, P. Barone, S. Picozzi, J. M. Rondinelli, and M. Capone, PRB, 94 68-72 (2016). https://doi.org/10.1103/PhysRevB.94.195116 96. TG-DMR110087 395. Asmara, T. C. et al. (2017), Tunable and low-loss correlated plasmons in Mott-like insulating oxides, Nature Communications, 8, 15271, doi:10.1038/ncomms15271. (published) [Comet, Gordon, SDSC] 396. De Jong, M., I. Winter, D. C. Chrzan, and M. Asta (2017), Ideal strength and ductility in metals from second- and third-order elastic constants, Physical Review B, 96(1), doi:10.1103/physrevb.96.014105. (published) [Comet, SDSC] 397. Wan, D. Y. et al. (2017), Electron transport and visible light absorption in a plasmonic photocatalyst based on strontium niobate, Nature Communications, 8, 15070, doi:10.1038/ncomms15070. (published) [Comet, Gordon, SDSC] 97. TG-DMR110088 398. Ahmed, L., B. Rasulev, S. Kar, P. Krupa, M. A. Mozolewska, and J. Leszczynski (2017), Inhibitors or toxins? Large library target-specific screening of fullerene-based nanoparticles for drug design purpose, Nanoscale, 9(29), 10263–10276, doi:10.1039/c7nr00770a. (published) 399. Gooch, A., N. Sizochenko, B. Rasulev, L. Gorb, and J. Leszczynski (2017), In vivo toxicity of nitroaromatics: A comprehensive quantitative structure-activity relationship study, Environmental Toxicology and Chemistry, 36(8), 2227–2233, doi:10.1002/etc.3761. (published) 400. Gooch, A., N. Sizochenko, L. Sviatenko, L. Gorb, and J. Leszczynski (2017), A quantum chemical based toxicity study of estimated reduction potential and hydrophobicity in series of nitroaromatic compounds, SAR and QSAR in Environmental Research, 28(2), 133–150, doi:10.1080/1062936x.2017.1286687. (published) 401. Isayev, O., C. Oses, C. Toher, E. Gossett, S. Curtarolo, and A. Tropsha (2017), Universal fragment descriptors for predicting properties of inorganic crystals, Nature Communications, 8, 15679, doi:10.1038/ncomms15679. (published) 402. Kiss, E., C. D. Campbell, R. W. Driver, J. D. Jolliffe, R. Lang, T. Sergeieva, S. Okovytyy, R. S. Paton, and M. D. Smith (2016), A Counterion-Directed Approach to the Diels-Alder Paradigm: Cascade Synthesis of Tricyclic Fused Cyclopropanes, Angewandte Chemie International Edition, 55(44), 13813–13817, doi:10.1002/anie.201608534. (published) 403. Klimenko, K., V. Kuz’min, L. Ognichenko, L. Gorb, M. Shukla, N. Vinas, E. Perkins, P. Polishchuk, A. Artemenko, and J. Leszczynski (2016), Novel enhanced applications of QSPR models: Temperature dependence of aqueous solubility, Journal of Computational Chemistry, 37(22), 2045–2051, doi:10.1002/jcc.24424. (published) 404. Moot, T., O. Isayev, R. W. Call, S. M. McCullough, M. Zemaitis, R. Lopez, J. F. Cahoon, and A. Tropsha (2016), Material informatics driven design and experimental validation of lead titanate as an aqueous solar photocathode, Materials Discovery, 6, 9–16, doi:10.1016/j.md.2017.04.001. (published) 405. Sergeieva, T., M. Bilichenko, S. Holodnyak, Y. V. Monaykina, S. I. Okovytyy, S. I. Kovalenko, E. Voronkov, and J. Leszczynski (2016), Origin of Substituent Effect on Tautomeric Behavior of 1,2,4-Triazole Derivatives:
RY2 IPR 4 Page 118 Combined Spectroscopic and Theoretical Study, The Journal of Physical Chemistry A, 120(51), 10116–10122, doi:10.1021/acs.jpca.6b08317. (published) 406. Smith, J. S., O. Isayev, and A. E. Roitberg (2017), ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost, Chem. Sci., 8(4), 3192–3203, doi:10.1039/c6sc05720a. (published) 407. Sviatenko, L. K., L. Gorb, F. C. Hill, D. Leszczynska, M. K. Shukla, S. I. Okovytyy, D. Hovorun, and J. Leszczynski (2016), In Silico Alkaline Hydrolysis of Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine: Density Functional Theory Investigation, Environmental Science & Technology, 50(18), 10039–10046, doi:10.1021/acs.est.5b06130. (published) 408. Sviatenko, L. K., L. Gorb, D. Leszczynska, S. I. Okovytyy, M. K. Shukla, and J. Leszczynski (2017), In silico kinetics of alkaline hydrolysis of 1,3,5-trinitro-1,3,5-triazinane (RDX): M06-2X investigation, Environ. Sci.: Processes Impacts, 19(3), 388–394, doi:10.1039/c6em00565a. (published) 409. Vargaljuk, V., S. Okovytyy, V. Polonskyy, O. Kramska, A. Shchukin, and J. Leszczynski (2017), Copper Crystallization from Aqueous Solution: Initiation and Evolution of the Polynuclear Clusters, Journal of Cluster Science, 28(5), 2517–2528, doi:10.1007/s10876-017-1239-4. (published) 410. Supratik Kar, Juganta K. Roy and Jerzy Leszczynski. In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future. npj Computational Materials (2017) 22. doi:10.1038/s41524-017-0025-z 411. Tetiana Sergeieva, Maria Bilichenko, Sergiy Holodnyak, Yulia V. Monaykina, Sergiy I. Okovytyy, Sergiy I. Kovalenko, Eugene Voronkov, and Jerzy Leszczynski. Origin of Substituent Effect on Tautomeric Behavior of 1,2,4-Triazole Derivatives: Combined Spectroscopic and Theoretical Study. The Journal of Physical Chemistry A 2016 120 (51), 10116-10122. DOI: 10.1021/acs.jpca.6b08317 412. Supratik Kar, Juganta K. Roy, Danuta Leszczynska and Jerzy Leszczynski. Power Conversion Efficiency of Arylamine Organic Dyes for Dye-Sensitized Solar Cells (DSSCs) Explicit to Cobalt Electrolyte: Understanding the Structural Attributes Using a Direct QSPR Approach. Computation 2017, 5, 2; doi:10.3390/computation5010002 413. Hatem Labidi, Henry Pinto, Jerzy Leszczynski and Damien Riedel. Investigating charge transfer dynamics at the nanoscale. Preprint: arXiv:1705.03027 https://arxiv.org/abs/1705.03027 414. Tetiana Zubatiuk, Baharak Sajjadi, Glake Hill, Danuta Leszczynska, Wei-Yin Chen, Jerzy Leszczynski, Modeling Decomposition of biochar under ultrasonic treatment through carbon dioxide anion and hydroxide radicals, Submitted to Chemical Physics Letters (submitted, June 24, 2017) 415. Leonid Gorb, Tatiana A. Zubatiuk, Roman Zubatyuk, Dmytro Hovorun and Jerzy Leszczynski. d(A)3d(T)3 and d(G)3d(C)3 B-DNA mini-helixes: the DFT/M06-2x and DFT/B97-D3 comparison of geometrical and energetic characteristics. Submitted to J. Mol. Modeling. 98. TG-DMR120025 416. Aryanfar, A., J. Thomas, A. Van der Ven, D. Xu, M. Youssef, J. Yang, B. Yildiz, and J. Marian (2016), Integrated Computational Modeling of Water Side Corrosion in Zirconium Metal Clad Under Nominal LWR Operating Conditions, JOM, 68(11), 2900–2911, doi:10.1007/s11837-016-2129-1. (published) 417. Krishnamoorthy, A., M. A. Dinh, and B. Yildiz (2017), Hydrogen weakens interlayer bonding in layered transition metal sulfide Fe1+xS, J. Mater. Chem. A, 5(10), 5030–5035, doi:10.1039/c6ta10538f. (published) 418. Marrocchelli, D., L. Sun, and B. Yildiz (2015), Dislocations in SrTiO3: Easy To Reduce but Not so Fast for Oxygen Transport, Journal of the American Chemical Society, 137(14), 4735–4748, doi:10.1021/ja513176u. (published) 419. Yang, J., M. Youssef, and B. Yildiz (2017), Predicting point defect equilibria across oxide hetero-interfaces: model system of ZrO2/Cr2O3, Phys. Chem. Chem. Phys., 19(5), 3869–3883, doi:10.1039/c6cp04997d. (published) 420. Herbert, F. W., Krishnamoorthy, A., Yildiz, B. & Van Vliet, K. J. Diffusion-limited kinetics of the antiferromagnetic to ferrimagnetic λ-transition in Fe1- xS. Appl. Phys. Lett. 106, 092402 (2015). 421. Herbert, F. W., Krishnamoorthy, A., Rands, L., Van Vliet, K. J. & Yildiz, B. Magnetic diffusion anomaly at the Néel temperature of pyrrhotite, Fe 1- x S. Phys. Chem. Chem. Phys. 17, 11036–11041 (2015). 422. Sun, L., Marrocchelli, D. & Yildiz, B. Edge dislocation slows down oxide ion diffusion in doped CeO2 by segregation of charged defects. Nat. Commun. 6, (2015). 423. Mostafa Youssef, Ming Yang, and Bilge Yildiz, Doping in the valley of hydrogen solubility: a route to designing hydrogen-resistant zirconium alloys Phys. Rev. Applied, 5, 014008. 424. Aravind Krishnamoorthy and Bilge Yildiz, Quantifying the origin of inter-adsorbate interactions on reactive surfaces for catalyst screening and design Physical Chemistry Chemical Physics, 17, 22227-22234, 2015
RY2 IPR 4 Page 119 425. N. Tsvetkov, Q. Lu, L. Sun, E. J. Crumlin and B. Yildiz, Improved chemical and electrochemical stability of perovskite oxides with less reducible cations at the surface, Nature materials, 15, 1010-1016, 2016 426. Aravind Krishnamoorthy and Bilge Yildiz. Effect of local magnetic order on ionic mobility in pyrrhotite, Fe1−xS. In Preparation. 427. Aravind Krishnamoorthy and Bilge Yildiz. Intrinsic point defects catalyze hydrogen evolution reaction in layered transition metal sulfide, mackinawite (Fe1+xS). In Preparation 428. Aravind Krishnamoorthy and Bilge Yildiz. Ab initio calculation of reaction energies and barriers at the semiconductor-water interface. In Preparation. 429. Frederick Faulkner, Lixin Sun and Bilge Yildiz, “Impact of B-site dopants (V, Nb, Ti, Zr, Hf, Al and Sc) to the activity and stability of (001) surface in LaCoO3 and LaMnO3” In Preparation. 430. Lixin Sun and Bilge Yildiz, “First-Principles Study of the defect structure of Cu-doped ceria” In Preparation. 431. Lixin Sun and Bilge Yildiz, “Impact of a ½ <110>{100} edge dislocation on the catalytic performance of Cu- doped ceria” In Preparation. 432. Jing Yang, Mostafa Youssef and Bilge Yildiz, Oxygen self-diffusion in monoclinic-ZrO2: a combined density functional theory and kinetic Monte Carlo study, In Preparation 433. Jing Yang, Mostafa Youssef and Bilge Yildiz, Coupled chemical and mechanical effect of lithium incorporation in zirconium oxide, In Preparation 99. TG-DMR120078 434. D. Reuven, K. Suggs, and X. Q. Wang, Self-assembly of polyimide resins on graphene oxide nanoribbons. Science Advances Today, in press (2017). 435. George Japaridze, Dipendra Pokhrel and X-Q. Wang, No-signaling principle and Bell inequality in pt-symmetry quantum mechanics. J. Phys. A 50, 18503 (2017). 436. U. K. Wijewardena, S. E. Brown, X.-Q. Wang, Epoxy-Carbonyl Conformation of Graphene Oxides. J. Phys. Chem. C 120, 22739–22743 (2016). 437. L. Rohani, D. J. Morten, X. Q. Wang, and J. Chaudhary, Molecular Dynamics Studies of Mutations in p53, J. Comp. Biology 23, 80-89 (2016). 438. William Seffens, Fisseha Abebe, Chad Evans and X.-Q. Wang, Spatial Partitioning of miRNAs Is Related to Sequence Similarity in Overall Transcriptome. Int. J. Mol. Sci. 17, 830 (2016). 439. U. K. C. Wijewardena, G. Japaridze, and X. Q. Wang, Iterative Solutions to PT-Symmetric Potentials. Proceedings of Dynamic Systems and Applications 7, 353–358 (2016). 440. X.-Q. Wang, Fisseha Abebe and William Seffens, Dynamic System Modeling the Whole Transcriptome in a Eukaryotic Cell. Proceedings of Dynamic Systems and Applications 7, 342–344 (2016). 441. London, L. A.; Bolton, L. A.; Samarakoon, D.K.; Sannigrahi, B.S.; Wang, X.-Q.; Khan, I.M. Effect of polymer stereoregularity on polystyrene/single-walled carbon nanotube interactions. RSC Advances 5, 59186-59193 (2015). 100. TG-DMR130009 442. Le, D., and T. S. Rahman (2017), Pt–dipyridyl tetrazine metal–organic network on the Au(100) surface: insights from first principles calculations, Faraday Discuss., 204, 83–95, doi:10.1039/c7fd00097a. (published) [Stampede, TACC] 101. TG-DMR130047 443. Cheng, T.-L., Y.-H. Wen, and J. A. Hawk (2017), Modeling elasto-viscoplasticity in a consistent phase field framework, International Journal of Plasticity, 96, 242–263, doi:10.1016/j.ijplas.2017.05.006. (published) [Stampede, TACC] 102. TG-DMR130077 444. Head, A. R., R. Tsyshevsky, L. Trotochaud, Y. Yu, L. Kyhl, O. Karslıoǧlu, M. M. Kuklja, and H. Bluhm (2016), Adsorption of Dimethyl Methylphosphonate on MoO3: The Role of Oxygen Vacancies, The Journal of Physical Chemistry C, 120(51), 29077–29088, doi:10.1021/acs.jpcc.6b07340. (published) [Stampede, TACC] 445. Kuklja, M. M., O. Sharia, and R. Tsyshevsky (2017), Manifestations of two-dimensional electron gas in molecular crystals, Surface Science, 657, 20–27, doi:10.1016/j.susc.2016.11.001. (published) [Stampede, TACC]
RY2 IPR 4 Page 120 446. Kuklja, M. M., R. V. Tsyshevsky, and S. Rashkeev (2017), Achieving tunable sensitivity in composite high- energy density materials, , doi:10.1063/1.4971519. (published) [Stampede, TACC] 447. Kuklja, M. M., R. V. Tsyshevsky, and O. Sharia (2017), Elucidation of high sensitivity of δ-HMX: New insight from first principles simulations, , doi:10.1063/1.4971595. (published) [Stampede, TACC] 448. Tsyshevsky, R. V., P. Pagoria, and M. M. Kuklja (2017), Searching for new energetic materials: Computational design of novel nitro-substituted heterocyclic explosives, , doi:10.1063/1.4971486. (published) [Stampede, TACC] 449. Tsyshevsky, R., A. S. Zverev, A. Y. Mitrofanov, N. N. Ilyakova, M. V. Kostyanko, S. V. Luzgarev, G. G. Garifzianova, and M. M. Kuklja (2016), Role of Hydrogen Abstraction Reaction in Photocatalytic Decomposition of High Energy Density Materials, The Journal of Physical Chemistry C, 120(43), 24835–24846, doi:10.1021/acs.jpcc.6b08042. (published) [Stampede, TACC] 450. Wang, F., R. Tsyshevsky, A. Zverev, A. Mitrofanov, and M. M. Kuklja (2017), Can a Photosensitive Oxide Catalyze Decomposition of Energetic Materials?, The Journal of Physical Chemistry C, 121(2), 1153–1161, doi:10.1021/acs.jpcc.6b10127. (published) [Stampede, TACC] 451. A.R. Head, R. Tsyshevsky, L. Trotochaud, B. Eichhorn, M. M. Kuklja, H. Bluhm, Electron Spectroscopy and Computational Studies of Dimethyl Methylphosphonate, J. Phys. Chem. A 120 (2016) 1985−1991. 452. R.V. Tsyshevsky, A. Zverev, A. Mitrofanov, S.N. Rashkeev, M.M. Kuklja, Photochemistry of the α-Al2O3-PETN Interface, Molecules 21 (2016) 289-1-13. 453. R.V. Tsyshevsky, O. Sharia, M.M. Kuklja, Molecular Theory of Detonation Initiation: Insight from First Principles Modeling of the Decomposition Mechanisms of Organic Nitro Energetic Materials, Molecules 21 (2016) 236-1-22. 454. R. V. Tsyshevsky, P. Pagoria, M. M. Kuklja, Computational Design of Novel Energetic Materials: Dinitro-Bis- Triazolo-Tetrazine (DNBTT), J. Phys. Chem. C 119 (2015) 8512–8521. 455. R.V. Tsyshevsky, S.N. Rashkeev, M.M. Kuklja, Defect states at organic–inorganic interfaces: Insight from first principles calculations for pentaerythritol tetranitrate on MgO surface, Surf. Sci. 637-638 (2015) 19-28. 456. R. Tsyshevsky, P. Pagoria, M. Zhang, A. Racoveanu, A. DeHope, D. Parrish, M. M. Kuklja, Searching for Low- Sensitivity Cast-Melt High-Energy-Density Materials: Synthesis, Characterization, and Decomposition Kinetics of 3,4-Bis(4-nitro-1,2,5-oxadiazol-3-yl)-1,2,5-oxadiazole-2-oxide, J. Phys. Chem. C 119 (2015) 3509-3521. 103. TG-DMR130132 457. Anderson, J. A., J. Antonaglia, J. A. Millan, M. Engel, and S. C. Glotzer (2017), Shape and Symmetry Determine Two-Dimensional Melting Transitions of Hard Regular Polygons, Physical Review X, 7(2), doi:10.1103/physrevx.7.021001. (published) [Comet, SDSC] 458. Ye, X., J. Chen, M. Eric Irrgang, M. Engel, A. Dong, S. C. Glotzer, and C. B. Murray (2016), Quasicrystalline nanocrystal superlattice with partial matching rules, Nature Materials, 16(2), 214–219, doi:10.1038/nmat4759. (published) [Comet, SDSC] 104. TG-DMR140067 459. Fan, L., H. L. Zhuang, L. Gao, Y. Lu, and L. A. Archer (2017), Regulating Li deposition at artificial solid electrolyte interphases, J. Mater. Chem. A, 5(7), 3483–3492, doi:10.1039/c6ta10204b. (published) [Stampede, TACC] 105. TG-DMR140068 460. Cao, L., and T. Mueller (2016), Theoretical Insights into the Effects of Oxidation and Mo-Doping on the Structure and Stability of Pt–Ni Nanoparticles, Nano Letters, 16(12), 7748–7754, doi:10.1021/acs.nanolett.6b03867. (published) 461. Raciti, D. et al. (2017), Low-Overpotential Electroreduction of Carbon Monoxide Using Copper Nanowires, ACS Catalysis, 7(7), 4467–4472, doi:10.1021/acscatal.7b01124. (published)
106. TG-DMR140129 462. Du, C. X., G. van Anders, R. S. Newman, and S. C. Glotzer (2017), Shape-driven solid–solid transitions in colloids, Proceedings of the National Academy of Sciences, 114(20), E3892–E3899, doi:10.1073/pnas.1621348114. (published) [Comet, SDSC]
RY2 IPR 4 Page 121 107. TG-DMR150064 463. Pinge, S., Lin, G., Baskaran, D., Padmanaban, M., Joo, Y. 2017. Designing a Cubically Packed Contact Hole Template based on a simple Flat Plate Confinement of di-Block Copolymers: A Coarse-Grained Molecular Dynamics Study. Bulletin of the American Physical Society 62. (published) [Comet, SDSC] 108. TG-DMR150099 464. Wang, T., Z. Gui, A. Janotti, C. Ni, and P. Karandikar (2017), Strong effect of electron-phonon interaction on the lattice thermal conductivity in 3C-SiC, Physical Review Materials, 1(3), doi:10.1103/physrevmaterials.1.034601. (published) [Bridges Large, Bridges Regular, Comet, PSC, SDSC, Stampede, TACC] 109. TG-DMR160023 465. Islam, M., Simanjuntak, P., Mitra, S., Sakidja, R. 2017. DFT Study on the Li Mobility in Li-Ion-Based Solid-State Electrolytes. MRS Advances First View: 1-6. https://www.cambridge.org/core/journals/mrs- advances/article/dft-study-on-the-li-mobility-in-liionbased-solidstate- electrolytes/8B92735DEBA50C6CA4996ABA5B40E40A (published) [Stampede, TACC] 110. TG-DMR160032 466. Liou, S., Hu, Z., Yang, K. 2017. Topological quantum phase transition from a fermionic integer quantum Hall phase to a bosonic fractional quantum Hall phase through a p-wave Feshbach resonance. Physical Review B 24. http://dx.doi.org/10.1103/PhysRevB.95.241106 DOI:10.1103/physrevb.95.241106 (Invalid?). (published) [Comet, Data Oasis, Ranch, SDSC, Stampede] 111. TG-DMR160052 467. Khalsa, G., Benedek, N. 2017. All-Optical Spin-State Switching in Complex Oxides from First Principles. Physical Review Letters. (in preparation) [Comet, SDSC] 468. Ritz, E., Benedek, N. 2017. Negative Thermal Expansion in Anisotropic Materials: Competition Between Poisson Effects and Mode Anharmonicity. Physical Review Letters. (in preparation) [Comet, SDSC] 469. Zhu, T., Cohen, T., Gibbs, A., Zhang, W., Halasyamani, P., et al. 2017. Theory Predicts, Neutrons Confirm: A Family of Layered Perovskites Without Inversion Symmetry. Journal of the American Chemical Society. (submitted) [Comet, SDSC] 112. TG-DMR160088, TG-DMR160101 470. Doratotaj, D., J. R. Simpson, and J.-A. Yan (2016), Probing the uniaxial strains inMoS2using polarized Raman spectroscopy: A first-principles study, Physical Review B, 93(7), doi:10.1103/physrevb.93.075401. (published) [Comet, SDSC] 471. He, R., J. van Baren, J.-A. Yan, X. Xi, Z. Ye, G. Ye, I.-H. Lu, S. M. Leong, and C. H. Lui (2016), Interlayer breathing and shear modes in NbSe2atomic layers, 2D Materials, 3(3), 031008, doi:10.1088/2053-1583/3/3/031008. (published) [Comet, SDSC] 472. He, R., J.-A. Yan, Z. Yin, Z. Ye, G. Ye, J. Cheng, J. Li, and C. H. Lui (2016), Coupling and Stacking Order of ReS2Atomic Layers Revealed by Ultralow-Frequency Raman Spectroscopy, Nano Letters, 16(2), 1404–1409, doi:10.1021/acs.nanolett.5b04925. (published) [Comet, SDSC] 473. Yap, W. C., Z. Yang, M. Mehboudi, J.-A. Yan, S. Barraza-Lopez, and W. Zhu (2017), Layered material GeSe and vertical GeSe/MoS2 p-n heterojunctions, Nano Research, doi:10.1007/s12274-017-1646-8. (published) [Comet, SDSC] 113. TG-DMR160109 474. Bendavid, L., Smith, R., Atsango, A. 2017. Interfacial Properties of Two-dimensional CdS/Graphene Nanocomposites. Poster presentation. American Conference on Theoretical Chemistry. (published) [Bridges Regular, Comet, Stampede] 114. TG-DMR160111 475. Hinkle, K. R., and F. R. Phelan (2017), Solvation of Carbon Nanoparticles in Water/Alcohol Mixtures: Using Molecular Simulation To Probe Energetics, Structure, and Dynamics, The Journal of Physical Chemistry C, 121(41), 22926–22938, doi:10.1021/acs.jpcc.7b07769. (published) [Stampede, TACC]
RY2 IPR 4 Page 122 115. TG-DMR160113 476. Hyde, E., Beck, M. 2017. Configuration and stability of adsorbed H2O on rutile {001}, {100}, {101}, {110} RuO2 surfaces. Surface Science. (in preparation) [LSU, SuperMIC] 477. Hyde, E., Beck, M. 2017. Energy and configuration of adsorbed H2O on rutile and anatase TiO2. Surface Science. (in preparation) [LSU, SuperMIC] 116. TG-DMR160114 478. Cheng, T., Y. Huang, H. Xiao, and W. A. Goddard (2017), Predicted Structures of the Active Sites Responsible for the Improved Reduction of Carbon Dioxide by Gold Nanoparticles, The Journal of Physical Chemistry Letters, 8(14), 3317–3320, doi:10.1021/acs.jpclett.7b01335. (published) [Comet] 479. Cheng, T., H. Xiao, and W. A. Goddard (2017), Nature of the Active Sites for CO Reduction on Copper Nanoparticles; Suggestions for Optimizing Performance, Journal of the American Chemical Society, 139(34), 11642–11645, doi:10.1021/jacs.7b03300. (published) [Comet] 480. Ductility in Crystalline Boron Subphosphide (B12P2) for Large Strain Indentation. Q. An and W. A. Goddard III, J. Phys. Chem. C, 2017, in press, (DOI: 10.1021/acs.jpcc.7b05429) 481. Y. Liu, J. Wu, K. P. Hackenberg, J. Zhang, Y. M. Wang, Y. Yang, K. Keyshar, J. Gu, T. Ogitsu, R. Vajtai, J. Lou, P. M. Ajayan, B. C. Wood, B. I. Yakobson “Self-optimizing layered hydrogen evolution catalyst with high basal-plane activity” Nature Energy, 2017, accepted (preprint is available at arXiv: 1608.05755) 482. Y. Liu, H. Xiao, W. A. Goddard III “Schottky-barrier-free contacts with two-dimensional semiconductors by surface-engineered MXenes” J. Am. Chem. Soc., 2016, 138, 15853 483. H. Zhou, F. Yu, Y. Liu, J. Sun, Z. Zhu, R. He, J. Bao, W. A. Goddard III, S. Chen, Z. Ren “Outstanding hydrogen evolution reaction catalyzed by porous nickel diselenide electrocatalysts” Energy Environ. Sci., 2017, 10, 1487-1492 484. X. Fan, Y. Liu, Z. Peng, Z. Zhang, H. Zhou, X. Zhang, B. I. Yakobson, W. A. Goddard, X. Guo, R. H. Hauge, J. M. Tour“Atomic H-Induced Mo2C Hybrid as an Active and Stable Bifunctional Electrocatalyst” ACS Nano, 2017, 11, pp 384–394 485. The full atomistic reaction mechanism with kinetics for CO reduction on Cu(100) from AIMD free energy calculations at 298K Cheng T; Xiao H; Goddard WA*; PNAS 2017, 114, 1795-1800. 117. TG-DMR970008S 486. Abdellahi, A., A. Urban, S. Dacek, and G. Ceder (2016), Understanding the Effect of Cation Disorder on the Voltage Profile of Lithium Transition-Metal Oxides, Chemistry of Materials, 28(15), 5373–5383, doi:10.1021/acs.chemmater.6b01438. (published) [Stampede, TACC] 487. Bianchini, M., P. Xiao, Y. Wang, and G. Ceder (2017), Additional Sodium Insertion into Polyanionic Cathodes for Higher-Energy Na-Ion Batteries, Advanced Energy Materials, 7(18), 1700514, doi:10.1002/aenm.201700514. (published) [Stampede, TACC] 488. Dacek, S. T., W. D. Richards, D. A. Kitchaev, and G. Ceder (2016), Structure and Dynamics of Fluorophosphate Na-Ion Battery Cathodes, Chemistry of Materials, 28(15), 5450–5460, doi:10.1021/acs.chemmater.6b01989. (published) [Stampede, TACC] 489. Richards, W. D., Y. Wang, L. J. Miara, J. C. Kim, and G. Ceder (2016), Design of Li1+2xZn1−xPS4, a new lithium ion conductor, Energy Environ. Sci., 9(10), 3272–3278, doi:10.1039/c6ee02094a. (published) [Stampede, TACC] 490. Vassilaras, P., D.-H. Kwon, S. T. Dacek, T. Shi, D.-H. Seo, G. Ceder, and J. C. Kim (2017), Electrochemical properties and structural evolution of O3-type layered sodium mixed transition metal oxides with trivalent nickel, J. Mater. Chem. A, 5(9), 4596–4606, doi:10.1039/c6ta09220a. (published) [Stampede, TACC] 491. L. J. Miara, W. D. Richards, Y. E. Wang, G. Ceder, “First-principles studies on cation dopants and electrolyte|cathode interphases for lithium garnets” Chem. Mater., 27 (2015) 4040. 34. J. Kim, D.‐H. Seo, H. Chen, G. Ceder, “The Effect of Antisite Disorder and Particle Size on Li Intercalation Kinetics in Monoclinic LiMnBO3” Adv. Energy Mater. 5 (2015) 1401916. 492. N. Twu, X. Li, A. Urban, M. Balasubramanian, J. Lee, L. Liu, G. Ceder, “Designing new lithium-excess cathode materials from percolation theory: Nanohighways in LixNi2-4x/3Sbx/3O2” Nano lett. 15 (2015) 596. 493. Y. Wang, W. Richard, S.P. Ong, L. Miara, J.-C. Kim, Y. Mo, and G. Ceder, “Design principles for solid-state lithium superionic conductors” Nature Mater., 14 (2015) 1026.
RY2 IPR 4 Page 123 494. D.-H. Seo, A. Urban, and G. Ceder, “Calibrating transition-metal energy levels and oxygen bands in first- principles calculations: Accurate prediction of redox potentials and charge transfer in lithium transition-metal oxides.” Phys. Rev. B, 92(11) (2015) 115118. 495. J. Lee, D.-H. Seo, M. Balasubramanian, N. Twu, X. Li, and G. Ceder, “A new class of high capacity cation- disordered oxides for rechargeable lithium batteries: Li–Ni–Ti–Mo oxides” Energy Environ. Sci., 8(11) (2015) 3255. 496. L. Miara, N. Suzuki, W. D. Richards, Y. Wang, J. C. Kim, and G. Ceder, “Li-ion conductivity in Li9S3N” J. Mater. Chem. A, 3(40) (2015) 20338. 497. S.-H. Bo, Y. Wang, J. C. Kim, W. D. Richards, and G. Ceder, “Computational and experimental investigations of Na-ion conduction in cubic Na3PSe4” Chem. Mater., 28(1) (2015) 252. 498. W. D. Richards, L. J. Miara, Y. Wang, J. C. Kim, and G. Ceder, “Interface stability in solid-state batteries”, Chem. Mater., 28(1) (2015) 266. 499. W. D. Richards, T. Tsujimura, L. J. Miara, Y. Wang, J. C. Kim, S. P. Ong, I. Uechi, N. Suzuki, and G. Ceder, “Design and synthesis of the superionic conductor Na10SnP2S12”, Nature Commun. 7 (2016) 11009. 500. D.-H. Seo, J. Lee, A. Urban, R. Malik, S. Kang, and G. Ceder, “The structural and chemical origin of the oxygen redox activity in layered and cation-disordered Li-excess cathode materials”, Nature Chem., 8 (2016) 962. 501. A. Abdellahi, A. Urban, S. Dacek, and G. Ceder, “The effect of cation disorder on the average Li intercalation voltage of transition-metal oxides”, Chem. Mater., 28(11) (2016) 3659. 502. A. Urban, I. Matts, A. Abdellahi, and G. Ceder, “Computational Design and Preparation of CationDisordered Oxides for High-Energy-Density Li-Ion Batteries”, Adv. Energy Mater., 6(15) (2016) 1600488. 503. S.-H. Bo, Y. Wang, and G. Ceder, “Structural and Na-ion conduction characteristics of Na3PSxSe4-x”, J. Mater. Chem. A., 4 (2016) 9044. 504. X. Li, Y. Wang, D. Wu, L. Liu, S.-H. Bo, G. Ceder, “Jahn–Teller Assisted Na Diffusion for High Performance Na Ion Batteries”, Chem. Mater., 28(18) (2016) 6575. 505. M. Bianchini, P. Xiao, Y. Wang, G. Ceder, “Additional Sodium Insertion into Polyanionic Cathodes for Higher- Energy Na-Ion Batteries”, Adv. Energy Mater. (2017) 1700514. 506. H. Kim, D.-H. Seo, J. C. Kim, S.-H. Bo, L. Liu, T. Shi, G. Ceder, “Investigation of potassium storage in layered P3- type K0.5MnO2 cathode”, Adv. Mater., (2017) accepted. 118. TG-DMR990019N 507. Boruah, N., and P. Dimitrakopoulos (2015), Motion and deformation of a droplet in a microfluidic cross- junction, Journal of Colloid and Interface Science, 453, 216–225, doi:10.1016/j.jcis.2015.04.067. (published) 508. Dimitrakopoulos, P. (2014), Effects of membrane hardness and scaling analysis for capsules in planar extensional flows, Journal of Fluid Mechanics, 745, 487–508, doi:10.1017/jfm.2014.66. (published) 509. Dimitrakopoulos, P. (2017), Dumbbell formation for elastic capsules in nonlinear extensional Stokes flows, Physical Review Fluids, 2(6), doi:10.1103/physrevfluids.2.063101. (published) 510. Dimitrakopoulos, P., and S. Kuriakose (2015), Determining a membrane’s shear modulus, independent of its area-dilatation modulus, via capsule flow in a converging micro-capillary, Soft Matter, 11(14), 2782–2793, doi:10.1039/c4sm02898h. (published) 511. Dodson, W. R., and P. Dimitrakopoulos (2014), Properties of the spindle-to-cusp transition in extensional capsule dynamics, EPL (Europhysics Letters), 106(4), 48003, doi:10.1209/0295-5075/106/48003. (published) 512. Koolivand, A., and P. Dimitrakopoulos (2017), Deformation of an elastic capsule in a microfluidic T-junction: settling shape and moduli determination, Microfluidics and Nanofluidics, 21(5), doi:10.1007/s10404-017- 1923-6. (published) 513. Kuriakose, S., and P. Dimitrakopoulos (2013), Deformation of an elastic capsule in a rectangular microfluidic channel, Soft Matter, 9(16), 4284, doi:10.1039/c3sm27683j. (published) 514. Park, S.-Y., and P. Dimitrakopoulos (2013), Transient dynamics of an elastic capsule in a microfluidic constriction, Soft Matter, 9(37), 8844, doi:10.1039/c3sm51516h. (published)
119. TG-DMS100004 515. Calderer, R., L. Zhu, R. Gibson, and A. Masud (2015), Residual-based turbulence models and arbitrary Lagrangian–Eulerian framework for free surface flows, Mathematical Models and Methods in Applied Sciences, 25(12), 2287–2317, doi:10.1142/s0218202515400096. (published) [Comet, NICS, SDSC, Trestles]
RY2 IPR 4 Page 124 120. TG-DMS140007 516. Yazdani, A., H. Li, J. D. Humphrey, and G. E. Karniadakis (2017), A General Shear-Dependent Model for Thrombus Formation, edited by S. L. Diamond, PLOS Computational Biology, 13(1), e1005291, doi:10.1371/journal.pcbi.1005291. (published) [Comet, SDSC, Stampede, TACC] 121. TG-DMS160021 517. Huang, M., J. Wang, E. Torre, H. Dueck, S. Shaffer, R. Bonasio, J. Murray, A. Raj, M. Li, and N. R. Zhang (2017), Gene Expression Recovery For Single Cell RNA Sequencing, , doi:10.1101/138677. (published) [Bridges Regular, PSC, Pylon] 122. TG-DPP130002 518. Ratnaswamy, V., G. Stadler, and M. Gurnis (2015), Adjoint-based estimation of plate coupling in a non-linear mantle flow model: theory and examples, Geophysical Journal International, 202(2), 768–786, doi:10.1093/gji/ggv166. (published) 519. Zhu, H., S. Li, S. Fomel, G. Stadler, and O. Ghattas (2016), A Bayesian approach to estimate uncertainty for full- waveform inversion using a priori information from depth migration, GEOPHYSICS, 81(5), R307–R323, doi:10.1190/geo2015-0641.1. (published) 520. Fernando, M., Duplyakin, D., Sundar, H. 2017. Machine and Application Aware Partitioning for Adaptive Mesh Refinement Applications. Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing. 231--242. (published) 521. Li, D., M. Gurnis, and G. Stadler (2017), Towards adjoint-based inversion of time-dependent mantle convection with non-linear viscosity, Geophysical Journal International, ggw493, doi:10.1093/gji/ggw493. (published) 522. Rudi, J., Stadler, G., Ghattas, O. 2017. Weighted BFBT Preconditioner for Stokes Flow Problems with Highly Heterogeneous Viscosity. SIAM Journal on Scientific Computing. arXiv preprint arXiv:1607.03936 (accepted) 123. TG-EAR100021 523. Petersen, R. I., D. R. Stegman, and P. J. Tackley (2017), The subduction dichotomy of strong plates and weak slabs, Solid Earth, 8(2), 339–350, doi:10.5194/se-8-339-2017. (published) [Comet, Data Oasis, Ranch, SDSC, Stampede, TACC] 124. TG-EAR100028 524. Moschetti, M., Hartzell, S., Ramírez‐Guzmán, L., Frankel, A., Angster, S., et al. 2017. 3D Ground‐Motion Simulations of M-w-7 Earthquakes on the Salt Lake City Segment of the Wasatch Fault Zone: Variability of Long‐Period (T≥1s) Ground Motions and Sensitivity to Kinematic Rupture Parameters. Bulletin of the Seismological Society of America. http://dx.doi.org/10.1785/0120160307 DOI:10.1785/0120160307 (Invalid?). (published) [Stampede, TACC] 125. TG-EAR130027 525. Andino-Nolasco, E., Welty, C. 2015. Modeling infiltration basin hydrology using Parflow.CLM. Geological Society of America 2015 Annual Meeting (Baltimore, MD). Paper No. 160-7. https://gsa.confex.com/gsa/2015AM/webprogram/Paper269392.html. (published) [Stampede, TACC] 526. Andino-Nolasco, E., Welty, C. 2016. Comparison of Modeled and Observed Pressure Heads Beneath an Infiltrating Rain Garden. ASCE World Environmental and Water Resources Congress (West Palm Beach, FL). Abstract 131402. http://bit.ly/1WP4zc8. (published) [Stampede, TACC] 527. Andino-Nolasco, E., Welty, C., McGarity, A. 2015. Evaluating green infrastructure fluxes using ParFlow.CLM. Pennsylvania Stormwater Symposium 2015, Villanova Urban Stormwater Partnership (Villanova, PA). https://www1.villanova.edu/content/dam/villanova/engineering/vcase/sym-presentations/2015/3A0.pdf. (published) [Stampede, TACC] 528. Barnes, M., Welty, C., Miller, A. 2015. High-resolution distributed hydrologic modeling across a gradient of development types. Geological Society of America 2015 Annual Meeting (Baltimore, MD). https://gsa.confex.com/gsa/2015AM/webprogram/Paper270512.html. (published) [Stampede, TACC] 529. Barnes, M., Welty, C., Miller, A. 2016. Impacts of development type and spatial pattern on the hydrologic cycle of headwater basins in urbanized Baltimore County, MD. American Geophysical Union Fall 2016 Meeting (San Francisco, CA). https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/142013.. (published) [Stampede, TACC]
RY2 IPR 4 Page 125 530. Barnes, M., Welty, C., Miller, A. 2017. Impacts of development age and type on urban groundwater flow regime. Water Resources Research. (in preparation) [Stampede, TACC] 531. Talebpour, M., Welty, C., Bou-Zeid, E. 2016. Land-Atmosphere-Hydrosphere Interactions in Urban Terrains. American Geophysical Union Fall 2016 Meeting (San Francisco, CA). https://agu.confex.com/agu/fm16/meetingapp.cgi/Paper/188199. (published) [Stampede, TACC] 126. TG-EAR160006, TG-EAR170001 532. Yuan, K., and B. Romanowicz (2017), Seismic evidence for partial melting at the root of major hot spot plumes, Science, 357(6349), 393–397, doi:10.1126/science.aan0760. (published) [Stampede, TACC] 127. TG-EAR160010 533. Song, X., Hoffman, F., Iversen, C., Yin, Y., Kumar, J., et al. 2017. Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data. Journal of Geophysical Research- Biogeosciences. (accepted) [Comet, Ranch, SDSC, Stampede, TACC] 534. Tang, D., C. Ma, Y. Wang, and X. Xu (2017), Multiscale evaluation of NCEP and CRUNCEP data sets at 90 large U.S. cities, Journal of Geophysical Research: Atmospheres, 122(14), 7433–7444, doi:10.1002/2016jd026165. (published) [NIP, SDSC, Stampede, TACC] 128. TG-EAR160024, TG-EAR160027 535. Mao, X., Gurnis, M., May, D. 2017. The geodynamics of incipient subduction along the Puysegur Trench. Geophysical Research Letters. (submitted) 536. Wang, H., Gurnis, M., Skogseid, J. 2017. Rapid Cenozoic subsidence in Gulf of Mexico and Hess Rise Conjugate Subduction. Geophysical Research Letters. (submitted) 129. TG-EAR160028 537. Chen, Y., Y. Li, A. J. Valocchi, and K. T. Christensen (2017), Lattice Boltzmann simulations of liquid CO 2 displacing water in a 2D heterogeneous micromodel at reservoir pressure conditions, Journal of Contaminant Hydrology, doi:10.1016/j.jconhyd.2017.09.005. (published) [Bridges Regular, LSU, PSC, Stampede, SuperMIC, TACC] 538. Tudek, J., D. Crandall, S. Fuchs, C. J. Werth, A. J. Valocchi, Y. Chen, and A. Goodman (2017), In situ contact angle measurements of liquid CO 2 , brine, and Mount Simon sandstone core using micro X-ray CT imaging, sessile drop, and Lattice Boltzmann modeling, Journal of Petroleum Science and Engineering, 155, 3–10, doi:10.1016/j.petrol.2017.01.047. (published) [Stampede, TACC] 130. TG-ENG150034 539. Taylor, M. G., and G. Mpourmpakis (2017), Thermodynamic stability of ligand-protected metal nanoclusters, Nature Communications, 8, 15988, doi:10.1038/ncomms15988. (published) [Comet, Gordon, SDSC] 131. TG-ENG160026 540. Kline, H. 2017. The continuous adjoint method for multi-fidelity hypersonic inlet design. PhD Dissertation. Stanford University. https://purl.stanford.edu/mm280hp6972. (published) 132. TG-HUA150001 541. Langmead, A., P. Rodriguez, S. P. Satheesan, and A. Craig (2017), Extracting Meaningful Data from Decomposing Bodies, Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact - PEARC17, doi:10.1145/3093338.3093368. (published) 133. TG-MCA01S018 542. Jiang, W., J. C. Phillips, L. Huang, M. Fajer, Y. Meng, J. C. Gumbart, Y. Luo, K. Schulten, and B. Roux (2014), Generalized scalable multiple copy algorithms for molecular dynamics simulations in NAMD, Computer Physics Communications, 185(3), 908–916, doi:10.1016/j.cpc.2013.12.014. (published) 543. Venable, R. M., Y. Luo, K. Gawrisch, B. Roux, and R. W. Pastor (2013), Simulations of Anionic Lipid Membranes: Development of Interaction-Specific Ion Parameters and Validation Using NMR Data, The Journal of Physical Chemistry B, 117(35), 10183–10192, doi:10.1021/jp401512z. (published)
RY2 IPR 4 Page 126 134. TG-MCA01S026 544. Bennett, M. C., A. H. Kulahlioglu, and L. Mitas (2017), A quantum Monte Carlo study of mono(benzene) TM and bis(benzene) TM systems, Chemical Physics Letters, 667, 74–78, doi:10.1016/j.cplett.2016.11.032. (published) [Stampede, TACC] 545. Melton, C. A., and L. Mitas (2016), Fixed-Node and Fixed-Phase Approximations and Their Relationship to Variable Spins in Quantum Monte Carlo, Recent Progress in Quantum Monte Carlo, 1–13, doi:10.1021/bk- 2016-1234.ch001. (published) [Stampede, TACC] 546. Niu, Q., J. Dinan, S. Tirukkovalur, A. Benali, J. Kim, L. Mitas, L. Wagner, and P. Sadayappan (2016), Global-view coefficients: a data management solution for parallel quantum Monte Carlo applications, Concurrency and Computation: Practice and Experience, 28(13), 3655–3671, doi:10.1002/cpe.3748. (published) [Stampede, TACC] 135. TG-MCA05S027 547. Das, S. et al. (2016), Molecularly Smooth Self-Assembled Monolayer for High-Mobility Organic Field-Effect Transistors, Nano Letters, 16(10), 6709–6715, doi:10.1021/acs.nanolett.6b03860. (published) [Stampede, TACC] 548. Levine, Z. A., and J.-E. Shea (2017), Simulations of disordered proteins and systems with conformational heterogeneity, Current Opinion in Structural Biology, 43, 95–103, doi:10.1016/j.sbi.2016.11.006. (published) [Stampede, TACC] 549. Levine, Z. A., R. M. Venable, M. C. Watson, M. G. Lerner, J.-E. Shea, R. W. Pastor, and F. L. H. Brown (2014), Determination of Biomembrane Bending Moduli in Fully Atomistic Simulations, Journal of the American Chemical Society, 136(39), 13582–13585, doi:10.1021/ja507910r. (published) [Stampede, TACC] 550. Peter, E. K., and J.-E. Shea (2017), An adaptive bias – hybrid MD/kMC algorithm for protein folding and aggregation, Phys. Chem. Chem. Phys., 19(26), 17373–17382, doi:10.1039/c7cp03035e. (published) 551. Seo, S.; Lee, D. W.; Ahn, J. S.; Cunha, K.; Ju, S. W.; Shin, E.; Kim, B.S.; Levine, Z. A.; Lins, R. D.; Israelachvili, J. N.; Waite, J. H.; Shea, J.E.; Ahn, B. K. Significant performance enhancement of polymer resins by bioinspired dynamic bonding Adv. Mater. 2017, in press. 552. Levine, Z. A.; Degen, G.; Israelachvili, J. N.; Waite, J. H.; Shea, J.E. Effect of ring hydroxylation on the adhesion of MFP‑3s‑pep to inorganic surfaces. Manuscript in preparation 2017. 553. Ganguly, P.; van der Vegt, N. F. A.; Shea, J.‑E. Hydrophobic Association in Mixed Urea‑TMAO Solutions. J. Phys. Chem. Lett. 2016, 7, 3052‑3059. 554. Ganguly, P.; Boserman, P.; van der Vegt, N. F. A.; Shea, J.‑E. Proteins in mixed protecting‑denaturing osmolytes. Submitted 2017. 555. Eschmann, N. A.; Georgieva, E. R.; Ganguly, P.; Borbat, P. P.; Rappaport, M. D.; Akdogan, Y.; Freed, J. H.; Shea, J.‑E.; Han, S. Signature of an aggregation‑prone conformation of tau. Sci. Rep. 2017, 7, 44739. 556. Levine, Z. A.; Okada, A.; Teranishi, K.; Cohen, P.; Langen, R.; Shea, J.‑E. Molecular mechanism of IAPP aggregate inhibition by mitochondrial peptides. Submitted 2017. 557. Song, B.; Charest, N.; Morriss‑Andrews, H. A.; Molinero, V.; Shea, J.‑E. Systematic derivation of implicit solvent models for the study of polymer collapse. J. Comput. Chem. 2017, 38, 1353‑1361.
136. TG-MCA07S014 558. Falkovich, G., and A. G. Kritsuk (2017), How vortices and shocks provide for a flux loop in two-dimensional compressible turbulence, Physical Review Fluids, 2(9), doi:10.1103/physrevfluids.2.092603. (published) [Comet, Data Oasis, Ranch, SDSC, Stampede, TACC] 137. TG-MCA07S015 559. Miyawaki S, Hoffman EA, Lin CL. Numerical simulations of aerosol delivery to the human lung with an idealized laryngeal model, image-based airway model, and automatic meshing algorithm. Computers & Fluids 2017;148:1-9. 560. Miyawaki S, Choi S, Hoffman EA, Lin CL. A 4DCT imaging-based breathing lung model with relative hysteresis. J Comput Phys. 2016;326:76-90. doi: 10.1016/j.jcp.2016.08.039. Epub 2016 Aug 31. 561. Miyawaki S, Hoffman EA, Lin CL. Effect of static vs. dynamic imaging on particle transport in CT-based numerical models of human central airways. J Aerosol Sci. 2016;100:129-139.
RY2 IPR 4 Page 127 562. Miyawaki S, Tawhai MH, Hoffman EA, Wenzel SE, Lin CL. Automatic construction of subject-specific human airway geometry including trifurcations based on a CT-segmented airway skeleton and surface. Biomech Model Mechanobiol. 2017;16(2):583-596. 563. Choi S, Choi J, Lin CL. Contributions of kinetic energy and viscous dissipation to airway resistance in pulmonary inspiratory vs. expiratory airflows in multiscale symmetric airway models with various bifurcation angles. J Biomed Eng 2017, under review. 564. LeBlanc LJ, Choi J, Choi S, Wenzel SE, Walenga R, Babiskin A, Hoffman EA, Lin CL. Image-based multiscale CFD simulations of air flow and particle transport differentiate inhaled drug delivery patterns in asthma sub- population clustered by quantitative CT imaging and clinical phenotypes. 2017, in preparation. 138. TG-MCA08X040 565. Ciezak-Jenkins, J. A., B. A. Steele, G. M. Borstad, and I. I. Oleynik (2017), Structural and spectroscopic studies of nitrogen-carbon monoxide mixtures: Photochemical response and observation of a novel phase, The Journal of Chemical Physics, 146(18), 184309, doi:10.1063/1.4983040. (published) [SDSC, TACC] 566. Cong, K. N., B. A. Steele, A. C. Landerville, and I. I. Oleynik (2017), First-principles investigation of iron pentacarbonyl molecular solid phases at high pressure, , doi:10.1063/1.4971606. (published) [SDSC, TACC] 567. Gonzalez, J. M., A. C. Landerville, and I. I. Oleynik (2017), Vibrational and thermophysical properties of PETN from first principles, , doi:10.1063/1.4971597. (published) [SDSC, TACC] 568. Gonzalez, J. M., and I. I. Oleynik (2016), Layer-dependent properties ofSnS2andSnSe2two-dimensional materials, Physical Review B, 94(12), doi:10.1103/physrevb.94.125443. (published) [SDSC, TACC] 569. Kozhushner, M. A., V. L. Bodneva, I. I. Oleynik, T. V. Belysheva, M. I. Ikim, and L. I. Trakhtenberg (2017), Sensor Effect in Oxide Films with a Large Concentration of Conduction Electrons, The Journal of Physical Chemistry C, 121(12), 6940–6945, doi:10.1021/acs.jpcc.6b10956. (published) [SDSC, TACC] 570. Landerville, A. C., J. C. Crowhurst, C. D. Grant, J. M. Zaug, and I. Oleynik (2017), Experimental and theoretical investigation of pressure-dependent Raman spectra of triaminotrinitrobenzene (TATB) at high pressures, , doi:10.1063/1.4971499. (published) [SDSC, TACC] 571. Landerville, A. C., and I. I. Oleynik (2017), Vibrational and thermal properties of β-HMX and TATB from dispersion corrected density functional theory, , doi:10.1063/1.4971541. (published) 572. Steele, B. A., and I. I. Oleynik (2017), Pentazole and Ammonium Pentazolate: Crystalline Hydro-Nitrogens at High Pressure, The Journal of Physical Chemistry A, 121(8), 1808–1813, doi:10.1021/acs.jpca.6b12900. (published) [SDSC, TACC] 573. Steele, B. A., and I. I. Oleynik (2017), New crystal phase of ammonium nitrate: First-principles prediction and characterization, , doi:10.1063/1.4971719. (published) [SDSC, TACC] 574. Steele, B. A., E. Stavrou, J. C. Crowhurst, J. M. Zaug, V. B. Prakapenka, and I. I. Oleynik (2017), High-Pressure Synthesis of a Pentazolate Salt, Chemistry of Materials, 29(2), 735–741, doi:10.1021/acs.chemmater.6b04538. (published) [SDSC, TACC] 575. Steele, B. A., E. Stavrous, V. B. Prakapenka, H. Radousky, J. Zaug, J. C. Crowhurst, and I. I. Oleynik (2017), Cesium pentazolate: A new nitrogen-rich energetic material, , doi:10.1063/1.4971510. (published) [SDSC, TACC] 139. TG-MCA09X001 576. H. Wu, T. Mayeshiba, and D. Morgan, High-throughput ab-initio dilute solute diffusion database, Sci. Data 3, p. 11 (2016). 577. W. Xie, Y. A. Chang, and D. Morgan, Ab initio energetics for modeling phase stability of the Np-U system, Journal of Nuclear Materials 479, p. 260-270 (2016). 578. S. Z. Xu, J. F. Lin, and D. Morgan, Iron partitioning between ferropericlase and bridgmanite in the Earth's lower mantle, Journal of Geophysical Research-Solid Earth 122, p. 1074-1087 (2017). 579. T. T. Mayeshiba and D. D. Morgan, Factors controlling oxygen migration barriers in perovskites, Solid State Ionics 296, p. 71-77 (2016). 580. S. Z. Xu, R. Jacobs, C. Wolyerton, T. Kuech, and D. Morgan, Nanoscale Voltage Enhancement at Cathode Interfaces in Li-Ion Batteries, Chemistry of Materials 29, p. 1218-1229 (2017). 581. S. Xu, G. Luo, R. Jacobs, S. Fang, M. K. Mahanthappa, R. J. Hamers, and D. Morgan, Ab Initio Modeling of Electrolyte Molecule Ethylene Carbonate Decomposition Reaction on Li(Ni,Mn,Co)O2 Cathode Surface, ACS Appl Mater Interfaces 9, p. 20545-20553 (2017). 582. G. Luo, S. Yang, G. R. Jenness, Z. Song, T. F. Kuech, and D. Morgan, Understanding and reducing deleterious defects in the metastable alloy GaAsBi, NPG Asia Materials 9, p. e345 (2017).
RY2 IPR 4 Page 128 583. H. Ko, A. Kaczmarowski, I. Szlufarska, and D. Morgan, Optimization of self-interstitial clusters in 3C-SiC with genetic algorithm, Journal of Nuclear Materials (2017). 584. W. Xie, C. A. Marianetti, and D. Morgan, Response to letter "Electron correlation and relativity of the 5f electrons in the U-Zr alloy system", Journal of Nuclear Materials 476, p. 110-112 (2016). 585. W. Xie, C. A. Marianetti, and D. Morgan, Reply to "Comment on 'Correlation and relativistic effects in U metal and U-Zr alloy: Validation of ab initio approaches'", Physical Review B 93 (2016). 586. F. Wang, J. H. Seo, G. F. Luo, M. B. Starr, Z. D. Li, D. L. Geng, X. Yin, S. Y. Wang, D. G. Fraser, D. Morgan, Z. Q. Ma, and X. D. Wang, Nanometre-thick single-crystalline nanosheets grown at the water-air interface, Nature Communications 7 (2016). 587. T. Mayeshiba and D. Morgan, Strain effects on oxygen migration in perovskites (vol 17, pg 2715, 2015), Physical Chemistry Chemical Physics 18, p. 7535-7536 (2016). 588. R. Jacobs, B. Zheng, B. Puchala, P. M. Voyles, A. B. Yankovich, and D. Morgan, Counterintuitive Reconstruction of the Polar O-Terminated ZnO Surface with Zinc Vacancies and Hydrogen, Journal of Physical Chemistry Letters 7, p. 4483-4487 (2016). 589. H. Ko, J. Deng, I. Szlufarska, D. Morgan, Ag diffusion in SiC high-energy grain boundaries: kinetic Monte Carlo study with first-principle calculations, Comp. Mat. Sci. 121 p. 248-257 (2016). 140. TG-MCA93S002 590. Bazavov, A., Bernard, C., Bouchard, C., Chang, C., DeTar, C., et al. 2017. Short-distance matrix elements for $D^0$-meson mixing for $N_f=2+1$ lattice QCD. Physical Review D. (submitted) 591. DeTar, C., Doerfler, D., Gottlieb, S., Jha, A., Kalamkar, D., et al. 2016. MILC staggered conjugate gradient performance on Intel KNL. 34th International Symposium on Lattice Field Theory (Lattice 2016) (Southampton, UK). Proceedings of Science (Lattice 2016). 270. https://arxiv.org/pdf/1611.00728.pdf. (published) 592. Gamiz, E., Bazavov, A., Bernard, C., DeTar, C., Du, D., et al. 2016. Kaon semileptonic decays with Nf=2+1+1 HISQ fermions and physical light-quark masses. 34th International Symposium on Lattice Field Theory (Lattice 2016) (Southampton, UK). Proceedings of Science (Lattice 2016). 286. https://arxiv.org/pdf/1611.04118.pdf. (published) [Globus Online, Keenland, NICS, Ranch, Stampede, TACC] 593. Komijani, J., Bazavov, A., Bernard, C., Brambilla, N., Brown, N., et al. 2016. Decay constants fB and fBs and quark masses m_b and m_c from HISQ simulations. 34th International Symposium on Lattice Field Theory (Lattice 2016) (Southampton, UK). Proceedings of Science (Lattice 2016). 2941. https://arxiv.org/pdf/1611.07411.pdf. (published) [Globus Online, Keenland, NICS, Ranch, Stampede, TACC] 594. Primer, T., Bazavov, A., Bernard, C., DeTar, C., Du, D., et al. 2016. D meson semileptonic form factors with HISQ valence and sea quarks. 34th International Symposium on Lattice Field Theory (Lattice 2016) (Southampton, UK). Proceedings of Science (Lattice 2016). 305. https://pos.sissa.it/256/305/pdf. (published) [Globus Online, Keenland, NICS, Ranch, Stampede, TACC] 141. TG-MCA93S013 595. Miao, Y., S. E. Nichols, and J. A. McCammon (2014), Free energy landscape of G-protein coupled receptors, explored by accelerated molecular dynamics, Physical Chemistry Chemical Physics, 16(14), 6398, doi:10.1039/c3cp53962h. (published) 596. Pang, Y. T., Y. Miao, Y. Wang, and J. A. McCammon (2017), Gaussian Accelerated Molecular Dynamics in NAMD, Journal of Chemical Theory and Computation, 13(1), 9–19, doi:10.1021/acs.jctc.6b00931. (published) [SDSC] 142. TG-MCA93S013, TG-MCB130048, TG-MCB140011 597. Miao, Y., J. Baudry, J. C. Smith, and J. A. McCammon (2016), General trends of dihedral conformational transitions in a globular protein, Proteins: Structure, Function, and Bioinformatics, 84(4), 501–514, doi:10.1002/prot.24996. (published) [Comet, Gordon, SDSC] 143. TG-MCA93S013, TG-MCB140011 598. Miao, Y., and J. A. McCammon (2016), Graded activation and free energy landscapes of a muscarinic G- protein–coupled receptor, Proceedings of the National Academy of Sciences, 113(43), 12162–12167, doi:10.1073/pnas.1614538113. (published) [Comet, Gordon, SDSC]
RY2 IPR 4 Page 129 144. TG-MCA93S013, TG-MCB160059 599. Palermo, G., C. G. Ricci, A. Fernando, R. Basak, M. Jinek, I. Rivalta, V. S. Batista, and J. A. McCammon (2017), Protospacer Adjacent Motif-Induced Allostery Activates CRISPR-Cas9, Journal of the American Chemical Society, doi:10.1021/jacs.7b05313. (published) [Bridges Regular, Comet, Data Oasis, PSC, SDSC] 145. TG-MCA95C006 600. Kong, R., Xue, M., Liu, C. 2017. Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments. Monthly Weather Review: 60. (submitted) 601. Supinie, T. A., N. Yussouf, Y. Jung, M. Xue, J. Cheng, and S. Wang (2017), Comparison of the Analyses and Forecasts of a Tornadic Supercell Storm from Assimilating Phased-Array Radar and WSR-88D Observations, Weather and Forecasting, 32(4), 1379–1401, doi:10.1175/waf-d-16-0159.1. (published) 602. Labriola, J., Snook, N., Putnam, B., Xue, M. 2017. Ensemble Hail Prediction for the Storms of 10 May 2011 using Single- and Double-Moment Microphysical Schemes. Monthly Weather Review. (submitted) [Stampede, TACC] 603. Loken, E. D., A. J. Clark, M. Xue, and F. Kong (2017), Comparison of Next-Day Probabilistic Severe Weather Forecasts from Coarse- and Fine-Resolution CAMs and a Convection-Allowing Ensemble, Weather and Forecasting, 32(4), 1403–1421, doi:10.1175/waf-d-16-0200.1. (published) 604. Mahale, V. N., G. Zhang, and M. Xue (2016), Characterization of the 14 June 2011 Norman, Oklahoma, Downburst through Dual-Polarization Radar Observations and Hydrometeor Classification, Journal of Applied Meteorology and Climatology, 55(12), 2635–2655, doi:10.1175/jamc-d-16-0062.1. (published) 605. Putnam, B. J., M. Xue, Y. Jung, N. A. Snook, and G. Zhang (2017), Ensemble Probabilistic Prediction of a Mesoscale Convective System and Associated Polarimetric Radar Variables Using Single-Moment and Double- Moment Microphysics Schemes and EnKF Radar Data Assimilation, Monthly Weather Review, 145(6), 2257– 2279, doi:10.1175/mwr-d-16-0162.1. (published) [Stampede, TACC] 606. Snyder, J. C., H. B. Bluestein, D. T. Dawson II, and Y. Jung (2017), Simulations of Polarimetric, X-Band Radar Signatures in Supercells. Part II: ZDR Columns and Rings and KDP Columns, Journal of Applied Meteorology and Climatology, 56(7), 2001–2026, doi:10.1175/jamc-d-16-0139.1. (published) 607. Snyder, J. C., H. B. Bluestein, D. T. Dawson II, and Y. Jung (2017), Simulations of Polarimetric, X-Band Radar Signatures in Supercells. Part I: Description of Experiment and Simulated ρhv Rings, Journal of Applied Meteorology and Climatology, 56(7), 1977–1999, doi:10.1175/jamc-d-16-0138.1. (published) 146. TG-MCA98T020 608. Crosby, B. D., B. W. O’Shea, T. C. Beers, and J. Tumlinson (2016), TRACING THE EVOLUTION OF HIGH- REDSHIFT GALAXIES USING STELLAR ABUNDANCES, The Astrophysical Journal, 820(1), 71, doi:10.3847/0004-637x/820/1/71. (published) 147. TG-MCA99S022 609. Buaria, D., P. K. Yeung, and B. L. Sawford (2016), A Lagrangian study of turbulent mixing: forward and backward dispersion of molecular trajectories in isotropic turbulence, Journal of Fluid Mechanics, 799, 352– 382, doi:10.1017/jfm.2016.359. (published) 610. Clay, M. P., and P. K. Yeung (2016), A numerical study of turbulence under temporally evolving axisymmetric contraction and subsequent relaxation, Journal of Fluid Mechanics, 805, 460–493, doi:10.1017/jfm.2016.566. (published) [Stampede, TACC] 611. Iyer, K. P., K. R. Sreenivasan, and P. K. Yeung (2017), Reynolds number scaling of velocity increments in isotropic turbulence, Physical Review E, 95(2), doi:10.1103/physreve.95.021101. (published) [Stampede, TACC] 148. TG-MCB080116N 612. Martin, D. R., and D. V. Matyushov (2017), Terahertz absorption of lysozyme in solution, The Journal of Chemical Physics, 147(8), 084502, doi:10.1063/1.4989641. (published) 613. Martin, D. R., and D. V. Matyushov (2017), Electron-transfer chain in respiratory complex I, Scientific Reports, 7(1), doi:10.1038/s41598-017-05779-y. (published)
RY2 IPR 4 Page 130 149. TG-MCB090159 614. Bu, L., P. N. Ciesielski, D. J. Robichaud, S. Kim, R. L. McCormick, T. D. Foust, and M. R. Nimlos (2017), Understanding Trends in Autoignition of Biofuels: Homologous Series of Oxygenated C5 Molecules, The Journal of Physical Chemistry A, 121(29), 5475–5486, doi:10.1021/acs.jpca.7b04000. (published) 615. Elder, T., L. Berstis, G. T. Beckham, and M. F. Crowley (2017), Density Functional Theory Study of Spirodienone Stereoisomers in Lignin, ACS Sustainable Chemistry & Engineering, 5(8), 7188–7194, doi:10.1021/acssuschemeng.7b01373. (published) 616. Ferguson, G. A., V. Vorotnikov, N. Wunder, J. Clark, K. Gruchalla, T. Bartholomew, D. J. Robichaud, and G. T. Beckham (2016), Ab Initio Surface Phase Diagrams for Coadsorption of Aromatics and Hydrogen on the Pt(111) Surface, The Journal of Physical Chemistry C, 120(46), 26249–26258, doi:10.1021/acs.jpcc.6b07057. (published) 617. Geronimo, I., S. R. Nigam, and C. M. Payne (2017), Desulfination by 2′-hydroxybiphenyl-2-sulfinate desulfinase proceeds via electrophilic aromatic substitution by the cysteine-27 proton, Chem. Sci., 8(7), 5078– 5086, doi:10.1039/c7sc00496f. (published) 618. Kognole, A. A., and C. M. Payne (2017), Inhibition of Mammalian Glycoprotein YKL-40, Journal of Biological Chemistry, 292(7), 2624–2636, doi:10.1074/jbc.m116.764985. (published) 619. Yu, Y., I. A. Fursule, L. C. Mills, D. L. Englert, B. J. Berron, and C. M. Payne (2017), CHARMM force field parameters for 2′-hydroxybiphenyl-2-sulfinate, 2-hydroxybiphenyl, and related analogs, Journal of Molecular Graphics and Modelling, 72, 32–42, doi:10.1016/j.jmgm.2016.12.005. (published) 620. Elder, T., Berstis, L., Beckham, G. T., and Crowley, M. F. (2016) Coupling and reactions of 5-hydroxyconiferyl alcohol in lignin formation. J. Agric. Food Chem. 64, 4742-4750 150. TG-MCB090163 621. Fily, Y., A. Baskaran, and M. F. Hagan (2017), Equilibrium mappings in polar-isotropic confined active particles, The European Physical Journal E, 40(6), doi:10.1140/epje/i2017-11551-3. (published) [SDSC, TACC] 622. Fily, Y., Kafri, Y., Solon, A., Tailleur, J., Turner, A. 2017. Mechanical pressure and momentum conservation in dry active matter. arXiv:1704.06499. http://arXiv.org/abs/1704.06499 (published) [TACC] 623. Lazaro, G., Mukhopadhyay, S., Hagan, M. 2017. The contribution of a nucleocapsid core to viral budding. Under Review. https://arxiv.org/abs/1706.04867 (submitted) [Maverick, Standford, TACC, XStream] 624. Sakhardande, R., Stanojeviea, S., Baskaran, A., Baskaran, A., Hagan, M., et al. 2017. Theory of microphase separation in bidisperse chiral membranes. Phys. Rev. E in press. (accepted) [TACC] 625. Wagner, C., Hagan, M., Baskaran, A. 2017. Steady-state distributions of ideal active Brownian particles under confinement and forcing. Journal of Statistical Mechanics: Theory and Experiment 2017: 043203. http://stacks.iop.org/1742-5468/2017/i=4/a=043203 (published) 626. Redner, GS; Wagner, CG; Baskaran, A; Hagan, MF, “A classical nucleation theory description of active colloid assembly”, Phys. Rev. Lett., 117, 148002, (2016) 627. Yu, N; Ghosh, A; Hagan, MF, “Faceted particles formed by the frustrated packing of anisotropic colloids on curved surfaces”, Soft Matter, 12, 8990 (2016), [cover article] http://dx.doi.org/10.1039/C6SM01498D 151. TG-MCB100099 628. Goyal, P., Yang, S., Cui, Q. 2014. Microscopic basis for kinetic gating in cytochrome c oxidase: insights from QM/MM analysis. Chem. Sci. 6: 826-841. (published) 152. TG-MCB100147 629. Reid, N. M. et al. (2017), The Landscape of Extreme Genomic Variation in the Highly Adaptable Atlantic Killifish, Genome Biology and Evolution, 9(3), 659–676, doi:10.1093/gbe/evx023. (published) [Comet, Data Oasis, Gordon, IU, Mason, SDSC, Trestles]
153. TG-MCB110014 630. Bai, H., R. Xue, H. Bao, L. Zhang, A. Yethiraj, Q. Cui, and E. R. Chapman (2016), Different states of synaptotagmin regulate evoked versus spontaneous release, Nature Communications, 7, 10971, doi:10.1038/ncomms10971. (published) [Gordon, LSU, SDSC, Stampede, SuperMIC, TACC]
RY2 IPR 4 Page 131 631. Christensen, A. S., J. C. Kromann, J. H. Jensen, and Q. Cui (2017), Intermolecular interactions in the condensed phase: Evaluation of semi-empirical quantum mechanical methods, The Journal of Chemical Physics, 147(16), 161704, doi:10.1063/1.4985605. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 632. Goldschen-Ohm, M. P., V. A. Klenchin, D. S. White, J. B. Cowgill, Q. Cui, R. H. Goldsmith, and B. Chanda (2016), Structure and dynamics underlying elementary ligand binding events in human pacemaking channels, eLife, 5, doi:10.7554/elife.20797. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 633. Hong, J., R. J. Hamers, J. A. Pedersen, and Q. Cui (2017), A Hybrid Molecular Dynamics/Multiconformer Continuum Electrostatics (MD/MCCE) Approach for the Determination of Surface Charge of Nanomaterials, The Journal of Physical Chemistry C, 121(6), 3584–3596, doi:10.1021/acs.jpcc.6b11537. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 634. Kromann, J. C., A. S. Christensen, Q. Cui, and J. H. Jensen (2016), Towards a barrier height benchmark set for biologically relevant systems, PeerJ, 4, e1994, doi:10.7717/peerj.1994. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 635. Lietz, C. B., Z. Chen, C. Yun Son, X. Pang, Q. Cui, and L. Li (2016), Multiple gas-phase conformations of proline- containing peptides: is it always cis/trans isomerization?, The Analyst, 141(16), 4863–4869, doi:10.1039/c5an00835b. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 636. Lu, X., V. Ovchinnikov, D. Demapan, D. Roston, and Q. Cui (2017), Regulation and Plasticity of Catalysis in Enzymes: Insights from Analysis of Mechanochemical Coupling in Myosin, Biochemistry, 56(10), 1482–1497, doi:10.1021/acs.biochem.7b00016. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 637. Roston, D., and Q. Cui (2016), QM/MM Analysis of Transition States and Transition State Analogues in Metalloenzymes, Computational Approaches for Studying Enzyme Mechanism Part A, 213–250, doi:10.1016/bs.mie.2016.05.016. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 638. Roston, D., and Q. Cui (2016), Substrate and Transition State Binding in Alkaline Phosphatase Analyzed by Computation of Oxygen Isotope Effects, Journal of the American Chemical Society, 138(36), 11946–11957, doi:10.1021/jacs.6b07347. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 639. Roston, D., D. Demapan, and Q. Cui (2016), Leaving Group Ability Observably Affects Transition State Structure in a Single Enzyme Active Site, Journal of the American Chemical Society, 138(23), 7386–7394, doi:10.1021/jacs.6b03156. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 640. Son, C. Y., J. G. McDaniel, J. R. Schmidt, Q. Cui, and A. Yethiraj (2016), First-Principles United Atom Force Field for the Ionic Liquid BMIM+BF4–: An Alternative to Charge Scaling, The Journal of Physical Chemistry B, 120(14), 3560–3568, doi:10.1021/acs.jpcb.5b12371. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 641. Troiano, J. M. et al. (2017), Quantifying the Electrostatics of Polycation–Lipid Bilayer Interactions, Journal of the American Chemical Society, 139(16), 5808–5816, doi:10.1021/jacs.6b12887. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 642. Zhang, L., M. Rajendram, D. B. Weibel, A. Yethiraj, and Q. Cui (2016), Ionic Hydrogen Bonds and Lipid Packing Defects Determine the Binding Orientation and Insertion Depth of RecA on Multicomponent Lipid Bilayers, The Journal of Physical Chemistry B, 120(33), 8424–8437, doi:10.1021/acs.jpcb.6b02164. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 643. Zhao, W., Z. Xu, Q. Cui, and N. Sahai (2016), Predicting the Structure–Activity Relationship of Hydroxyapatite- Binding Peptides by Enhanced-Sampling Molecular Simulation, Langmuir, 32(27), 7009–7022, doi:10.1021/acs.langmuir.6b01582. (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 644. Zheng, Y., Cui, Q. 2016. Microscopic mechanisms that govern the titration response and pK a values of buried residues in staphylococcal nuclease mutants. Proteins: Structure, Function, and Bioinformatics 2: 268-281. http://dx.doi.org/10.1002/prot.25213 DOI:10.1002/prot.25213 (Invalid?). (published) [Comet, LSU, SDSC, Stampede, SuperMIC, TACC] 154. TG-MCB110056 645. AR Pereira, J Hsin, E Krol, AC Tavares, E Hoiczyk, E Kuru, N Ng, MS VanNieuwenhze, YV Brun, T Roemer, R Carballido-Lopez, D-J Sheffers, KC Huang, MG de Pinho, “FtsZ-Dependent Elongation of a Coccoid Bacterium”, mBio 7, 5 (2016). 646. A Colavin, H Shi, KC Huang, “RodZ modulates geometric localization of the bacterial actin MreB to regulate cell shape”, in revision, Nature Microbiology. 647. N Ng, H Shi, A Colavin, KC Huang, “Conserved molecular dynamics within bacterial and archael actin homologs”, manuscript in preparation.
RY2 IPR 4 Page 132 155. TG-MCB120045 648. Wang, J., Xu, H., Beckett, D., Matysiak, S. 2017. Protein allostery through long distance modulation of disorder- to-order: an integrated experimental and computational analysis. Biochemistry. (submitted) 649. McCutchen M., Chen L.G., Bermudez H., Matysiak S., "The Interplay of Dynamical Properties Between Ionic Liquids and Ionic Surfactants: Mechanism and Aggregation", J. Phys. Chem. B, 2015, 119 (30), pp 9925–9932 650. Ganesan, S.J., Matysiak S., "Interplay between the hydrophobic effect and dipole interactions in peptide aggregation at interfaces", Phys. Chem. Chem. Phys., 2016, 18, 2449-2458 651. Ganesan S.J., Xu H., Matysiak S., Effect of lipid head group interactions on membrane roperties and membrane- induced cationic β-hairpin folding, Phys. Chem. Chem. Phys., 2016, 18, 17836-17850. Back cover. 652. Chaibva M., Jawahery S., Pilkington IV A. W. , Arndt J. R. , Sarver O., Valentine S., Matysiak S., Legleiter J., Acetylation within the first 17 amino acids of huntingtin exon 1 alters aggregation and binding to lipid membranes, Biophys. J., 2016, 111, 349-362 653. Ganesan S., Hongcheng X., Matysiak S., “Influence of Monovalent Cation Size on Nanodomain Formation in Anionic-Zwitterionic Mixed Bilayers”, J. Phys. Chem. B, 2017, 121, 787-799 654. Custer G., Das P., Matysiak S.,``Interplay between Conformational Heterogeneity and Hydration in the Folding Landscape of a Designed Three-Helix Bundle”, J. Phys. Chem. B, 2017, 121, 2731-2738 655. Xu, H. Matysiak S.,``Effect of pH on chitosan hydrogel polymer network structure”. In Press in Chem. Comm., 2017, 53, 7373-7376. Invited article in Emerging Investigators Issue 156. TG-MCB120115 656. Vallat B, Madrid-Aliste C, Fiser A Modularity of Protein Folds as a Tool for Template-Free Modeling of Structures. PLoS Comput Biol (2015) 11(8) : e1004419 PMID: 26252221 PMCID: PMC4529212 657. Yap EH, Fiser A. ProtLID, a Residue-Based Pharmacophore Approach to Identify Cognate Protein Ligands in the Immunoglobulin Superfamily. Structure. 2016 Dec 6; 24(12):2217-2226. PMID: 27889206 PMCID: PMC5444293 658. Mario Pujato, Carlos Madrid-Aliste and Andras Fiser TF2DNA database and web resource: navigating gene regulatory networks (submitted) 659. Dybas JM, Fiser A. Development of a motif-based topology-independent structure comparison method to identify evolutionarily related folds. Proteins. 2016 Dec;84(12):1859-1874. PMID: 27671894 PMCID: PMC5118133 157. TG-MCB130040 660. Lee, C., and B. Mertz (2016), Theoretical Evidence for Multiple Charge Transfer Pathways in Bacteriorhodopsin, Journal of Chemical Theory and Computation, 12(4), 1639–1646, doi:10.1021/acs.jctc.6b00033. (published) [Stampede, TACC] 158. TG-MCB130084, TG-MCB160083 661. Saladi, S., Javed, N., Muller, A., Clemons, W. 2017. Decoding sequence-level information to predict membrane protein expression. bioRxiv: 098673. (published) [Comet, SDSC, Stampede, TACC, Trestles] 159. TG-MCB130108 662. Smolin, N. and Robia, S.L.; A Structural mechanism for calcium transporter headpiece closure. J. Phys. Chem. B, 2015, 119, 1407-1415. 663. Dvornikov, A.V., Smolin, N., Zhang, M., Martin, J.L., Robia, S.L. and de Tombe, P.P.; Restrictive cardiomyopathy Troponin-I R145W mutation does not perturb myofilament length dependent activation in human cardiac sarcomeres. The Journal of Biological Chemistry 2016, 291, 21817-21828. 664. Himes, R.D., Smolin, N., Kukol, A., Bossuyt, J., Bers, D.M. and Robia, S.L.; L30A mutation of phospholemman mimics effects of cardiac glycosides on isolated cardiomyocytes. Biochemistry 2016, 55, 6196-6204. 665. Lamanchane, R., Mukherjee, S., Smolin, N., Pauszek, R., Bradley, M., Sastri, J., Robia, S.L., Millar, D. and Campbell, E.M.; Dynamic conformational changes in the rhesus TRIM5α dimer dictate the potency of HIV-1 restriction. Virology 2017, 500, 161-168. 666. Raguimova, O.N., Smolin, N. and Robia, S.L.; A small loop in SERCA N-domain facilitates headpiece closure and SERCA association with PLB (in preparation). 667. Smolin, N. and Robia, S.L.; Molecular dynamics simulations suggest multiple binding sites for phospholamban on SERCA (in preparation).
RY2 IPR 4 Page 133 668. Papadaki, M., Holewinski, R.J., Smolin, N., Stachowski, M.J., Blair, C.A., Campbell, K.S, Robia, S.L. and Kirk, J.A.; Methylglyoxal is elevated in myofilament of diabetic cardiomyopathy patients and reduces myofilamnet function (in preparation). 160. TG-MCB130131 669. Rasmussen, M. D., M. J. Hubisz, I. Gronau, and A. Siepel (2014), Genome-Wide Inference of Ancestral Recombination Graphs, edited by G. Coop, PLoS Genetics, 10(5), e1004342, doi:10.1371/journal.pgen.1004342. (published) [Stampede, TACC] 161. TG-MCB130172 670. Cheng, C. Y., F.-C. Chou, W. Kladwang, S. Tian, P. Cordero, and R. Das (2015), Consistent global structures of complex RNA states through multidimensional chemical mapping, eLife, 4, doi:10.7554/elife.07600. (published) 671. Miao, Z. et al. (2017), RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme, RNA, 23(5), 655–672, doi:10.1261/rna.060368.116. (published) 672. Miao, Z. et al. (2015), RNA-PuzzlesRound II: assessment of RNA structure prediction programs applied to three large RNA structures, RNA, 21(6), 1066–1084, doi:10.1261/rna.049502.114. (published) 162. TG-MCB130173 673. Bamert, R., Lundquist, K., Hwang, H., Webb, C., Shiota, T., et al. 2017. Structural basis for substrate selection by the translocation and assembly module of the beta-barrel assembly machinery. Molecular Microbiology. (submitted) 674. Hill, S., Nguyen, E., Donegan, R., Hazel, A., Gumbart, J., et al. 2017. Structure and misfolding of the flexible tripartite coiled coil domain of glaucoma-associated myocilin. Structure. (submitted) 675. Hwang, H., T. G. McCaslin, A. Hazel, C. V. Pagba, C. M. Nevin, A. Pavlova, B. A. Barry, and J. C. Gumbart (2017), Redox-Driven Conformational Dynamics in a Photosystem-II-Inspired β-Hairpin Maquette Determined through Spectroscopy and Simulation, The Journal of Physical Chemistry B, 121(15), 3536–3545, doi:10.1021/acs.jpcb.6b09481. (published) 676. Lundquist, K., Bakelar, J., Noinaj, N., Gumbart, J. 2017. An analysis of lateral-gate-opening energetics in BamA. Biophysical Journal. (submitted) 677. Noinaj, N., J. C. Gumbart, and S. K. Buchanan (2017), The β-barrel assembly machinery in motion, Nature Reviews Microbiology, 15(4), 197–204, doi:10.1038/nrmicro.2016.191. (published) 678. Pavlova, A., J. M. Parks, A. K. Oyelere, and J. C. Gumbart (2017), Toward the rational design of macrolide antibiotics to combat resistance, Chemical Biology & Drug Design, doi:10.1111/cbdd.13004. (published) 679. Stock, G., Cai, G., Infield, D., Hwang, H., McCarty, N., et al. 2017. ATP-dependent signaling in simulations of a revised CFTR homology model. Biochemistry. (submitted) 163. TG-MCB130178 680. Miner, J. C., and A. E. García (2017), Equilibrium Denaturation and Preferential Interactions of an RNA Tetraloop with Urea, The Journal of Physical Chemistry B, 121(15), 3734–3746, doi:10.1021/acs.jpcb.6b10767. (published) 164. TG-MCB130231 681. Feinstein, W., and M. Brylinski (2016), Structure-Based Drug Discovery Accelerated by Many-Core Devices, Current Drug Targets, 17(14), 1595–1609, doi:10.2174/1389450117666160112112854. (published) 165. TG-MCB140147 682. Goonasekera, N., Lonie, A., Taylor, J., Afgan, E. 2016. CloudBridge: a Simple Cross-Cloud Python Library. Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale. 37. (published) 683. Grüning, B., Rasche, E., Rebolledo-Jaramillo, B., Eberhard, C., Houwaart, T., et al. 2017. Jupyter and Galaxy: Easing entry barriers into complex data analyses for biomedical researchers. PLOS Computational Biology 13: e1005425. (published) 684. Stewart, C., Hancock, D., Vaughn, M., Fischer, J., Cockerill, T., et al. 2016. Jetstream: performance, early experiences, and early results. Proceedings of the XSEDE16 Conference on Diversity, Big Data, and Science at Scale. 22. (published)
RY2 IPR 4 Page 134 166. TG-MCB140215 685. Stevens, A., Sekar, G., Shah, N., Mostafavi, A., Cowburn, D., et al. 2017. A promiscuous split intein with expanded protein engineering applications. Proceedings of the Academy of Sciences US early addition: 1-5. http://www.pnas.org/content/early/2017/07/19/1701083114.full.pdf (published) [SDSC, TACC] 167. TG-MCB140228 686. Kirschner, D., E. Pienaar, S. Marino, and J. J. Linderman (2017), A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment, Current Opinion in Systems Biology, 3, 170–185, doi:10.1016/j.coisb.2017.05.014. (published) [Comet, SDSC, Stampede, TACC] 687. Linderman, J. J., and D. E. Kirschner (2015), In silico models of M. tuberculosis infection provide a route to new therapies, Drug Discovery Today: Disease Models, 15, 37–41, doi:10.1016/j.ddmod.2014.02.006. (published) [Comet, SDSC, Stampede, TACC] 688. Marino, S., and D. Kirschner (2016), A Multi-Compartment Hybrid Computational Model Predicts Key Roles for Dendritic Cells in Tuberculosis Infection, Computation, 4(4), 39, doi:10.3390/computation4040039. (published) 168. TG-MCB150005 689. Fu, J., L. Larini, A. J. Cooper, J. W. Whittaker, A. Ahmed, J. Dong, M. Lee, and T. Zhang (2017), Computational and experimental analysis of short peptide motifs for enzyme inhibition, edited by S. Bhattacharjya, PLOS ONE, 12(8), e0182847, doi:10.1371/journal.pone.0182847. (published) [Ranch, Stampede, TACC] 169. TG-MCB150084 690. Horii, M., Dumdie, J., Morey, R., Laurent, L., Parast, M. 2015. Moleculer profiling of severe preeclampsia defined by clinical and pathologic criteria.. Society for Reproductive Investigation (San Francisco). (published) [Gordon, SDSC] 170. TG-MCB150114 691. Pan, F., V. H. Man, C. Roland, and C. Sagui (2017), Structure and Dynamics of DNA and RNA Double Helices of CAG and GAC Trinucleotide Repeats, Biophysical Journal, 113(1), 19–36, doi:10.1016/j.bpj.2017.05.041. (published) [Comet, SDSC, Stampede, TACC] 171. TG-MCB150132 692. Vermaas, J.V., Pogorelov, T.V., and Tajkhorshid, E. “Extension of the Highly Mobile Membrane Mimetic to Transmembrane Systems through the Development of in silico Solvents,” J. Phys. Chem. B, 121, 3764-3776, 2017. 693. Baylon, J.L., Vermaas, J.V., Muller, M., Arcario, M.J., Pogorelov, T.V., and Tajkhorshid, E. “Atomic-Level Description of Protein--Lipid Interactions Using an Accelerated Membrane Model,” BBA - Biomembranes, 1858, 1573-1583, 2016. 694. K. M. Nuzzio, E. D. Watt, J. M. Boettcher, J. M. Gajsiewicz, J. H. Morrissey, C. M. Rienstra, “High-resolution NMR studies of human tissue factor,” PLoS One, 11, e0163206, 2016. 695. Y. Qi, X. Cheng, J. Lee, J.V. Vermaas, T.V. Pogorelov, E. Tajkhorshid, J.B. Klauda, and W. Im, "CHARMM-GUI HMMM Builder for Membrane Simulations with the Highly Mobile Membrane Mimetic Model," Biophys. J., 109, 2012-2022, 2015. 172. TG-MCB160004, TG-MCB160164, TG-MCB160173, TG-MCB170088 696. Readmond, C., and C. Wu (2017), Investigating detailed interactions between novel PAR1 antagonist F16357 and the receptor using docking and molecular dynamic simulations, Journal of Molecular Graphics and Modelling, 77, 205–217, doi:10.1016/j.jmgm.2017.08.019. (published) 173. TG-MCB160004, TG-MCB160164, TG-MCB160173 697. Borrell, K. L., C. Cancglin, B. L. Stinger, K. G. DeFrates, G. A. Caputo, C. Wu, and T. D. Vaden (2017), An Experimental and Molecular Dynamics Study of Red Fluorescent Protein mCherry in Novel Aqueous Amino Acid Ionic Liquids, The Journal of Physical Chemistry B, 121(18), 4823–4832, doi:10.1021/acs.jpcb.7b03582. (published)
RY2 IPR 4 Page 135 698. Mulholland, K., F. Siddiquei, and C. Wu (2017), Binding modes and pathway of RHPS4 to human telomeric G- quadruplex and duplex DNA probed by all-atom molecular dynamics simulations with explicit solvent, Phys. Chem. Chem. Phys., 19(28), 18685–18694, doi:10.1039/c7cp03313c. (published) 699. Sader, S., J. Cai, A. C. G. Muller, and C. Wu (2017), Can human allergy drug fexofenadine, an antagonist of histamine (H 1 ) receptor, be used to treat dog and cat? Homology modeling, docking and molecular dynamic Simulation of three H 1 receptors in complex with fexofenadine, Journal of Molecular Graphics and Modelling, 75, 106–116, doi:10.1016/j.jmgm.2017.05.010. (published) 700. Shen, Z., K. A. Mulholland, Y. Zheng, and C. Wu (2017), Binding of anticancer drug daunomycin to a TGGGGT G- quadruplex DNA probed by all-atom molecular dynamics simulations: additional pure groove binding mode and implications on designing more selective G-quadruplex ligands, Journal of Molecular Modeling, 23(9), doi:10.1007/s00894-017-3417-6. (accepted) 174. TG-MCB160005 701. Xi, W., Hansmann, U. 2017. Ring-like N-fold Models of Aβ42 fibrils. Scientific Reports. (accepted) 702. Zhang, H., W. Xi, U. H. E. Hansmann, and Y. Wei (2017), Fibril–Barrel Transitions in Cylindrin Amyloids, Journal of Chemical Theory and Computation, 13(8), 3936–3944, doi:10.1021/acs.jctc.7b00383. (published) 175. TG-MCB160012 703. Childers, M., Towse, C., Daggett, V. 2017. Molecular dynamics-derived rotamer libraries for D-amino acids in homochiral and heterochiral polypeptide chains. Molecular Systems Design and Engineering. (submitted) [Stampede] 176. TG-MCB160059 704. Palermo, G., Y. Miao, R. C. Walker, M. Jinek, and J. A. McCammon (2016), Striking Plasticity of CRISPR-Cas9 and Key Role of Non-target DNA, as Revealed by Molecular Simulations, ACS Central Science, 2(10), 756–763, doi:10.1021/acscentsci.6b00218. (published) [Comet, Gordon, SDSC] 705. Palermo, G., Y. Miao, R. C. Walker, M. Jinek, and J. A. McCammon (2017), CRISPR-Cas9 conformational activation as elucidated from enhanced molecular simulations, Proceedings of the National Academy of Sciences, 114(28), 7260–7265, doi:10.1073/pnas.1707645114. (published) [Comet, SDSC] 177. TG-MCB160101, TG-MCB160124 706. Wang, Y., J. Virtanen, Z. Xue, and Y. Zhang (2017), I-TASSER-MR: automated molecular replacement for distant-homology proteins using iterative fragment assembly and progressive sequence truncation, Nucleic Acids Research, 45(W1), W429–W434, doi:10.1093/nar/gkx349. (published) [Comet, Data Oasis, Science Gateways, SDSC] 178. TG-MCB160119 707. Alsamarah, A., A. E. LaCuran, P. Oelschlaeger, J. Hao, and Y. Luo (2015), Uncovering Molecular Bases Underlying Bone Morphogenetic Protein Receptor Inhibitor Selectivity, edited by J. Zheng, PLOS ONE, 10(7), e0132221, doi:10.1371/journal.pone.0132221. (published) 708. luo, y. 2017. Polymodal Allosteric Regulation of Type 1 Serine/Threonine Kinase Receptors via a Conserved Electrostatic Lock. PLOS Computational Biology. (submitted) 179. TG-MSS120006 709. Danelson, K., Golman, A., Kemper, A., Gayzik, F., Gabler, H., et al. 2015. Finite element comparison of human and Hybrid III responses in a frontal impact. Accident Analysis & Prevention 85: 125--156. (published) [Blacklight, PSC] 710. Gaewsky, J., Danelson, K., Weaver, C., Stitzel, J. 2013. Optimization of a simplified automobile finite element model using time varying injury metrics.. Biomedical sciences instrumentation 50: 83--91. (published) [Blacklight, PSC] 711. Golman, A., Danelson, K., Stitzel, J. 2016. Robust human body model injury prediction in simulated side impact crashes. Computer methods in biomechanics and biomedical engineering 19: 717--732. (published) [Blacklight, PSC]
RY2 IPR 4 Page 136 712. Golman, A., Danelson, K., Stitzel, J. 2016. Robust human body model injury prediction in simulated side impact crashes. Computer methods in biomechanics and biomedical engineering 19: 717--732. (published) [Blacklight, PSC] 713. Jones, D., Gaewsky, J., Kelley, M., Weaver, A., Miller, A., et al. 2016. Lumbar vertebrae fracture injury risk in finite element reconstruction of CIREN and NASS frontal motor vehicle crashes. Traffic injury prevention 17: 109--115. (published) [Blacklight, PSC] 180. TG-MSS150011 714. Noble, B. A., C. M. Mate, and B. Raeymaekers (2017), Spreading Kinetics of Ultrathin Liquid Films Using Molecular Dynamics, Langmuir, 33(14), 3476–3483, doi:10.1021/acs.langmuir.7b00334. (published) [Comet, SDSC] 181. TG-MSS170025 715. Huang, S., I. J. Beyerlein, and C. Zhou (2017), Nanograin size effects on the strength of biphase nanolayered composites, Scientific Reports, 7(1), doi:10.1038/s41598-017-10064-z. (published) 716. Huang, S., and C. Zhou (2017), Modeling and Simulation of Nanoindentation, JOM, 69(11), 2256–2263, doi:10.1007/s11837-017-2541-1. (published) 182. TG-OCE100015 717. Cheng, Z., Hsu, T., Calantoni, J. 2017. SedFoam: A multi-dimensional Eulerian two-phase model for sediment transport and its application to momentary bed failure. Coastal Engineering 119: 32-50. (published) [LSU, SuperMIC] 718. Kim, Y., Zhou, Z., Hsu, T., Puleo, J. 2017. Large eddy simulation of dam-break driven swash on a rough-planar beach. Journal of Geophysical Research: Oceans 122(2): 1274-1296. (published) [LSU, SuperMIC] 719. Zhou, Z., T.-J. Hsu, D. Cox, and X. Liu (2017), Large-eddy simulation of wave-breaking induced turbulent coherent structures and suspended sediment transport on a barred beach, Journal of Geophysical Research: Oceans, 122(1), 207–235, doi:10.1002/2016jc011884. (published) [LSU, SuperMIC] 720. Zhou, Z., Yu, X., Hsu, T., Shi, F., Geyer, W., et al. 2017. On nonhydrostatic coastal model simulations of shear instabilities in a stratified shear flow at high Reynolds number. Journal of Geophysical Research: Oceans 122: 3081-3105. (published) [LSU, SuperMIC] 183. TG-OCE150004 721. Renault, L., McWilliams, J., Penven, P. 2017. Modulation of the Agulhas Current Retroflection and Leakage by Oceanic Current Interaction with the Atmosphere in Coupled Simulations. Journal of Physical Oceanography 47: 2077--2100. (published) [Comet, Gordon, SDSC] 184. TG-OCE160013 722. Irby, I. 2018. A look at the relationship between multiple models and the Chesapeake Bay TMDL. William & Mary Policy Review. (in preparation) [Comet, Data Oasis, Globus Online, SDSC] 723. Irby, I., Friedrichs, M. 2017. The competing impacts of climate change and nutrient reduction on dissolved oxygen in Chesapeake Bay. Biogeosciences. (in preparation) [Comet, Data Oasis, Globus Online, SDSC] 724. Moriarty, J., Friedrichs, M., Harris, C. 2018. The Impact of Seabed Resuspension on Primary Productivity and Remineralization: A Numerical Modeling Study in the Chesapeake Bay. Estuaries & Coasts. (in preparation) [Comet, Data Oasis, Globus Online, SDSC] 725. Signorini, S., Mannino, A., Friedrichs, M., St-Laurent, P., Wilkin, J., et al. 2018. Estuarine Dissolved Organic Carbon Flux from Space with Application to Chesapeake and Delaware Bays. Journal of Geophysical Research Oceans. (in preparation) [Comet, Data Oasis, Globus Online, SDSC] 185. TG-PHY090002 726. Barraza-Lopez, S. 2017. Photostrictive Two-Dimensional Materials in the Monochalcogenide Family. Physical Review Letters 118: 227401. https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.118.227401 (published) [Comet, SDSC] 727. Naumis, G. G., S. Barraza-Lopez, M. Oliva-Leyva, and H. Terrones (2017), Electronic and optical properties of strained graphene and other strained 2D materials: a review, Reports on Progress in Physics, 80(9), 096501, doi:10.1088/1361-6633/aa74ef. (published) [Comet, SDSC, Stampede, TACC]
RY2 IPR 4 Page 137 186. TG-PHY090003 728. Cook, W., Sperhake, U., Berti, E., Cardoso, V. 2017. Black-hole head-on collisions in higher dimensions. Physical Review D. (submitted) [Comet, Data Oasis, SDSC, Stampede, TACC] 729. Fedrow, J., Ott, C., Sperhake, U., Blackman, J., Haas, R., et al. 2017. Gravitational Waves from Binary Black Hole Mergers Inside of Stars. Physical Review Letters. http://adsabs.harvard.edu/abs/2017arXiv170407383F (accepted) [Comet, Data Oasis, SDSC, Stampede, TACC] 730. Gerosa, D., U. Sperhake, and J. Vošmera (2017), On the equal-mass limit of precessing black-hole binaries, Classical and Quantum Gravity, 34(6), 064004, doi:10.1088/1361-6382/aa5e58. (published) [Bridges Regular, Comet, PSC, SDSC, Stampede, TACC] 731. Sperhake, U., Moore, C., Rosca, R., Agathos, M., Gerosa, D., et al. 2017. Long-lived inverse chirp signals from core collapse in massive scalar-tensor gravity. Physical Review Letters. (accepted) [Comet, Data Oasis, SDSC, Stampede, TACC] 187. TG-PHY090084 732. Bourouaine, S., and G. G. Howes (2017), The development of magnetic field line wander in gyrokinetic plasma turbulence: dependence on amplitude of turbulence, Journal of Plasma Physics, 83(03), doi:10.1017/s0022377817000319. (published) 733. Howes, G. G. (2016), THE DYNAMICAL GENERATION OF CURRENT SHEETS IN ASTROPHYSICAL PLASMA TURBULENCE, The Astrophysical Journal, 827(2), L28, doi:10.3847/2041-8205/827/2/l28. (published) 734. Howes, G. G. (2017), A prospectus on kinetic heliophysics, Physics of Plasmas, 24(5), 055907, doi:10.1063/1.4983993. (published) 735. Howes, G. G., and K. D. Nielson (2013), Alfvén wave collisions, the fundamental building block of plasma turbulence. I. Asymptotic solution, Physics of Plasmas, 20(7), 072302, doi:10.1063/1.4812805. (published) 736. Howes, G. G., K. D. Nielson, D. J. Drake, J. W. R. Schroeder, F. Skiff, C. A. Kletzing, and T. A. Carter (2013), Alfvén wave collisions, the fundamental building block of plasma turbulence. III. Theory for experimental design, Physics of Plasmas, 20(7), 072304, doi:10.1063/1.4812808. (published) 737. Li, T. C., G. G. Howes, K. G. Klein, and J. M. TenBarge (2016), ENERGY DISSIPATION AND LANDAU DAMPING IN TWO- AND THREE-DIMENSIONAL PLASMA TURBULENCE, The Astrophysical Journal, 832(2), L24, doi:10.3847/2041-8205/832/2/l24. (published) 738. TenBarge, J. M., G. G. Howes, and W. Dorland (2013), COLLISIONLESS DAMPING AT ELECTRON SCALES IN SOLAR WIND TURBULENCE, The Astrophysical Journal, 774(2), 139, doi:10.1088/0004-637x/774/2/139. (published) 739. Nonlinear energy transfer and current sheet development in localized Alfven wavepacket collisions in the strong turbulence limit, Verniero, J. L., Howes, G. G., and Klein, K. G. J. Plasma Phys., submitted (2017). 740. Diagnosing collisionless energy transfer using field-particle correlations: gyrokinetic turbulence, Klein, K. G., Howes, G. G., and TenBarge, J. M. J. Plasma Phys., submitted (2017). 741. A dynamical model of plasma turbulence in the solar wind Howes, G. G. Phil. Trans. Roy. Soc. A, 373,20140145 (2015). 742. 188. TG-PHY100033 743. Blackman, J., S. E. Field, M. A. Scheel, C. R. Galley, C. D. Ott, M. Boyle, L. E. Kidder, H. P. Pfeiffer, and B. Szilágyi (2017), Numerical relativity waveform surrogate model for generically precessing binary black hole mergers, Physical Review D, 96(2), doi:10.1103/physrevd.96.024058. (published) 744. Blackman, J., Field, S., Scheel, M., Galley, C., Ott, C., et al. 2017. A Numerical Relativity Waveform Surrogate Model for Generically Precessing Binary Black Hole Mergers. Physical Review D. http://adsabs.harvard.edu/abs/2017arXiv170507089B (submitted) [Stampede, TACC] 745. Lippuner, J., Roberts, L. 2017. SkyNet: A modular nuclear reaction network library. ArXiv e-prints. (published) [Stampede, TACC] 746. Ott, C. D. (2016), Massive Computation for Understanding Core-Collapse Supernova Explosions, Computing in Science & Engineering, 18(5), 78–92, doi:10.1109/mcse.2016.81. (published) [Stampede, TACC] 747. Renzo, M., Ott, C., Shore, S., de Mink, S. 2017. A Systematic Survey of the Effects of Wind Mass Loss Algorithms on the Evolution of Single Massive Stars. Astronomy and Astrophysics. http://adsabs.harvard.edu/abs/2017arXiv170309705R (accepted) [Stampede, TACC]
RY2 IPR 4 Page 138 748. Richers, S., Nagakura, H., Ott, C., Sumiyoshi, K., Yamada, S. 2017. A Detailed Comparison of Multi-Dimensional Boltzmann Neutrino Transport Methods in Core-Collapse Supernovae. Astrophysical Journal Supplemental Series. http://adsabs.harvard.edu/abs/2017arXiv170606187R (submitted) [Stampede, TACC] 749. Richers, S., C. D. Ott, E. Abdikamalov, E. O’Connor, and C. Sullivan (2017), Equation of state effects on gravitational waves from rotating core collapse, Physical Review D, 95(6), doi:10.1103/physrevd.95.063019. (published) [Stampede, TACC] 750. Roberts, L. F., C. D. Ott, R. Haas, E. P. O’Connor, P. Diener, and E. Schnetter (2016), GENERAL-RELATIVISTIC THREE-DIMENSIONAL MULTI-GROUP NEUTRINO RADIATION-HYDRODYNAMICS SIMULATIONS OF CORE- COLLAPSE SUPERNOVAE, The Astrophysical Journal, 831(1), 98, doi:10.3847/0004-637x/831/1/98. (published) [Stampede, TACC] 751. Schneider, A., Roberts, L., Ott, C. 2017. A New Open-Source Nuclear Equation of State Framework based on the Liquid-Drop Model with Skyrme Interaction. Physical Review C. http://adsabs.harvard.edu/abs/2017arXiv170701527D (submitted) [Stampede, TACC] 752. L. F. Roberts, C. D. Ott, R. Haas, E. P. O’Connor, P. Diener, and E. Schnetter, General-Relativistic Three- Dimensional Multi-group Neutrino Radiation-Hydrodynamics Simulations of Core-Collapse Supernovae, Astrophys. J. 831, 98 (2016). 753. J. M. Fedrow, C. D. Ott, U. Sperhake, J. Blackman, R. Haas, C. Reisswig, and A. De Felice, Gravitational Waves from Binary Black Hole Mergers Inside of Stars, submitted to Phys. Rev. Lett.; arXiv:1704.07383 (2017).
189. TG-PHY100033, TG-PHY990007N 754. Kidder, L. E. et al. (2017), SpECTRE: A task-based discontinuous Galerkin code for relativistic astrophysics, Journal of Computational Physics, 335, 84–114, doi:10.1016/j.jcp.2016.12.059. (published) 755. Smith, R., S. E. Field, K. Blackburn, C.-J. Haster, M. Pürrer, V. Raymond, and P. Schmidt (2016), Fast and accurate inference on gravitational waves from precessing compact binaries, Physical Review D, 94(4), doi:10.1103/physrevd.94.044031. (published) 190. TG-PHY100053 756. East, W. E., V. Paschalidis, and F. Pretorius (2016), Equation of state effects and one-arm spiral instability in hypermassive neutron stars formed in eccentric neutron star mergers, Classical and Quantum Gravity, 33(24), 244004, doi:10.1088/0264-9381/33/24/244004. (published) [Comet, Stampede] 757. Yang, H., K. Yagi, J. Blackman, L. Lehner, V. Paschalidis, F. Pretorius, and N. Yunes (2017), Black Hole Spectroscopy with Coherent Mode Stacking, Physical Review Letters, 118(16), doi:10.1103/physrevlett.118.161101. (published) [Comet] 191. TG-PHY130014 758. Li, Y., Wuest, T., Landau, D. 2015. Effect of surface attractive strength on structural transitions of a confined HP lattice protein. International Conference on Computational Physics (Boston). J. Phys.: Conf. Ser. 640. 012015 (6 pages). (published) 759. Shi, G., Wuest, T., Landau, D. 2017. Folding in coarse-grained lattice models of Ribonuclease A: A Replica Exchange Wang-Landau Study. Phys. Rev. E. (submitted) 192. TG-PHY150036 760. Tang, W., Wang, B., Ethier, S., Kwasniewski, G., Hoefler, T., et al. 2016. Extreme Scale Plasma Turbulence Simulations on Top Supercomputers Worldwide. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Salt Lake City, Utah). 43:1--43:12. http://dl.acm.org/citation.cfm?id=3014904.3014962. (published) 193. TG-PHY150039 761. Catterall, S., and J. Giedt (2014), Real space renormalization group for twisted lattice N $$ \mathcal{N} $$ =4 super Yang-Mills, Journal of High Energy Physics, 2014(11), doi:10.1007/jhep11(2014)050. (published) [Bridges Regular, PSC, Pylon, Ranch, Stampede, TACC, Training] 762. Giedt, J. (2016), Anomalous dimensions on the lattice, International Journal of Modern Physics A, 31(10), 1630011, doi:10.1142/s0217751x16300118. (published) [Bridges Regular, PSC, Pylon, Ranch, Stampede, TACC, Training]
RY2 IPR 4 Page 139 763. Giedt, J., Flamino, J. 2017. Berezinskii-Kosterlitz-Thouless phase transition from lattice sine-Gordon model. Lattice 2017 - 35th International Symposium on Lattice Field Theory (Granada, Spain). (in preparation) [Bridges Regular, PSC, Stampede, TACC, Training] 764. Giedt, J., Howarth, D. 2017. Stochastic propagators for multi-pion correlation functions in lattice QCD with GPUs. Physical Review D. (submitted) [Bridges Regular, PSC, Pylon, Ranch, Stampede, TACC, Training] 765. Howarth, D., Giedt, J. 2014. Scalar Mesons on the Lattice Using Stochastic Sources on GPU Architecture. Lattice 2014 - 32nd International Symposium on Lattice Field Theory (New York, USA). PoS LATTICE2014. 096. (published) [Bridges Regular, PSC, Pylon, Ranch, Stampede, TACC, Training] 194. TG-PHY150039, TG-PHY160030 766. Giedt, J., Catterall, S., Jha, R. 2017. Truncation of lattice N=4 super Yang-Mills. Lattice 2017 - 35th International Symposium on Lattice Field Theory (Granada, Spain). (in preparation) [Bridges Regular, Comet, Data Oasis, PSC, Pylon, Ranch, SDSC, Stampede, TACC, Training] 195. TG-PHY160016 767. Atmani, L., Bichara, C., Pellenq, R., Van Damme, H., van Duin, A., et al. 2017. From cellulose to kerogen: molecular simulation of a geological process. Journal of Physical Chemistry Letter. (submitted) [LSU, SuperMIC] 196. TG-PHY160030 768. Bergner, G., and S. Catterall (2016), Supersymmetry on the lattice, International Journal of Modern Physics A, 31(22), 1643005, doi:10.1142/s0217751x16430053. (published) [Comet, SDSC] 769. Catterall, S. (2015), Supersymmetry on a Lattice, Journal of Physics: Conference Series, 640, 012050, doi:10.1088/1742-6596/640/1/012050. (published) [Comet, SDSC] 770. Catterall, S. (2016), Fermion mass without symmetry breaking, Journal of High Energy Physics, 2016(1), doi:10.1007/jhep01(2016)121. (published) [Comet, SDSC] 771. Catterall, S., Jha, R., Schaich, D., Wiseman, T. 2017. Testing holography using lattice super-Yang--Mills on a 2- torus. Journal of High Energy Physics. https://arxiv.org/abs/1709.07025 (submitted) [Comet, SDSC] 772. Schaich, D., Catterall, S., Damgaard, P., Giedt, J. 2016. Latest results from lattice N=4 supersymmetric Yang– Mills. PoS LATTICE2016: 221. (published) [Comet, SDSC] 197. TG-PHY160040 773. Foucart, F., M. Chandra, C. F. Gammie, E. Quataert, and A. Tchekhovskoy (2017), How important is non-ideal physics in simulations of sub-Eddington accretion on to spinning black holes?, Monthly Notices of the Royal Astronomical Society, 470(2), 2240–2252, doi:10.1093/mnras/stx1368. (published) [Stampede, TACC] 198. TG-PHY160058 774. Rantaharju, J., C. Pica, and F. Sannino (2017), Ideal walking dynamics via a gauged NJL model, Physical Review D, 96(1), doi:10.1103/physrevd.96.014512. (published) [Bridges Regular, PSC] 199. TG-PHY170001 775. Gomez, J. A., M. Degroote, J. Zhao, Y. Qiu, and G. E. Scuseria (2017), Spin polynomial similarity transformation for repulsive Hamiltonians: interpolating between coupled cluster and spin-projected unrestricted Hartree– Fock, Phys. Chem. Chem. Phys., 19(33), 22385–22394, doi:10.1039/c7cp04075j. (published) [Bridges Regular, Comet, PSC, SDSC] 200. TG-PHY990007N 776. Blackman, J., S. E. Field, M. A. Scheel, C. R. Galley, D. A. Hemberger, P. Schmidt, and R. Smith (2017), A Surrogate model of gravitational waveforms from numerical relativity simulations of precessing binary black hole mergers, Physical Review D, 95(10), doi:10.1103/physrevd.95.104023. (published) 201. TG-TRA160031 777. Larkins, D. B., and J. Dinan (2016), Extending a Message Passing Runtime to Support Partitioned, Global Logical Address Spaces, 2016 First International Workshop on Communication Optimizations in HPC (COMHPC), doi:10.1109/comhpc.2016.007. (published) [Comet, SDSC, Stampede, TACC]
RY2 IPR 4 Page 140 202. XSEDE_ALLOCATION_REQUEST 778. Abbasi, M., Barakat, M., Vahidkhah, K., Azadani, A. 2016. Characterization of three-dimensional anisotropic heart valve tissue mechanical properties using inverse finite element analysis. Journal of the mechanical behavior of biomedical materials 62: 33--44. (published) 779. Beard, D. J., D. D. McLeod, C. L. Logan, L. A. Murtha, M. S. Imtiaz, D. F. van Helden, and N. J. Spratt (2015), Intracranial Pressure Elevation Reduces Flow through Collateral Vessels and the Penetrating Arterioles they Supply. a Possible Explanation for “Collateral Failure” and Infarct Expansion after Ischemic Stroke, Journal of Cerebral Blood Flow & Metabolism, 35(5), 861–872, doi:10.1038/jcbfm.2015.2. (published) 780. Branton, A. 2017. Assessment of Physicochemical and Thermal Properties of Energy of Consciousness Healing Treated Ferrous Sulphate Using PXRD, PSD, DSC, and TGA/DTG Analysis. Modern Chemistry 5/4: 50-59. http://www.sciencepublishinggroup.com/journal/paperinfo?journalid=121&doi=10.11648/j.mc.20170504.1 1 DOI:10.11648/j.mc.20170504.11 (Invalid?). (published) [Science Gateways] 781. Cannell, M. B., C. H. T. Kong, M. S. Imtiaz, and D. R. Laver (2013), Control of Sarcoplasmic Reticulum Ca2+ Release by Stochastic RyR Gating within a 3D Model of the Cardiac Dyad and Importance of Induction Decay for CICR Termination, Biophysical Journal, 104(10), 2149–2159, doi:10.1016/j.bpj.2013.03.058. (published) 782. Dahl, N., and M. Xue (2016), Prediction of the 14 June 2010 Oklahoma City Extreme Precipitation and Flooding Event in a Multiphysics Multi-Initial-Conditions Storm-Scale Ensemble Forecasting System, Weather and Forecasting, 31(4), 1215–1246, doi:10.1175/waf-d-15-0116.1. (published) 783. Fu, Y., Song, F. 2017. SDN helps Big-Data to Optimize Access to Data. IET Book Series on Big Data. (published) 784. Hodkinson, E. C. et al. (2016), Heritability of ECG Biomarkers in the Netherlands Twin Registry Measured from Holter ECGs, Frontiers in Physiology, 7, doi:10.3389/fphys.2016.00154. (published) 785. Imtiaz, M. S., S. K. Mohammed, F. Deeba, and K. A. Wahid (2017), Tri-Scan: A Three Stage Color Enhancement Tool for Endoscopic Images, Journal of Medical Systems, 41(6), doi:10.1007/s10916-017-0738-z. (published) 786. Imtiaz, M. S., and K. A. Wahid (2014), Image enhancement and space-variant color reproduction method for endoscopic images using adaptive sigmoid function, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, doi:10.1109/embc.2014.6944477. (published) 787. Imtiaz, M. S., and K. A. Wahid (2015), Color Enhancement in Endoscopic Images Using Adaptive Sigmoid Function and Space Variant Color Reproduction, Computational and Mathematical Methods in Medicine, 2015, 1–19, doi:10.1155/2015/607407. (published) 788. Johnson, M., Y. Jung, D. T. Dawson, and M. Xue (2016), Comparison of Simulated Polarimetric Signatures in Idealized Supercell Storms Using Two-Moment Bulk Microphysics Schemes in WRF, Monthly Weather Review, 144(3), 971–996, doi:10.1175/mwr-d-15-0233.1. (published) 789. Khan, T. H., S. K. Mohammed, M. S. Imtiaz, and K. A. Wahid (2016), Color reproduction and processing algorithm based on real-time mapping for endoscopic images, SpringerPlus, 5(1), doi:10.1186/s40064-015- 1612-4. (published) 790. Khan, T., R. Shrestha, K. A. Wahid, and M. S. Imtiaz (2015), Colour-reproduction algorithm for transmitting variable video frames and its application to capsule endoscopy, Healthcare Technology Letters, 2(2), 52–57, doi:10.1049/htl.2014.0086. (published) 791. Kovalskiy, A., Vlcek, M., Palka, K., Buzek, J., York-Winegar, J., et al. 2017. Structural origin of surface transformations in arsenic sulfide thin films upon UV-irradiation. Applied Surface Science 394: 604–612. http://www.sciencedirect.com/science/article/pii/S0169433216320979 (published) 792. Lacroix, J., Botello-Smith, W., Luo, Y. 2017. Chemical Gating of the Mechanosensitive Piezo1 Channel by Asymmetric Binding of its Agonist Yoda1. Nature Communication. (submitted) 793. Laver, D. R., C. H. T. Kong, M. S. Imtiaz, and M. B. Cannell (2013), Termination of calcium-induced calcium release by induction decay: An emergent property of stochastic channel gating and molecular scale architecture, Journal of Molecular and Cellular Cardiology, 54, 98–100, doi:10.1016/j.yjmcc.2012.10.009. (published) 794. Lee, W., S. A. Mann, M. J. Windley, M. S. Imtiaz, J. I. Vandenberg, and A. P. Hill (2016), In silico assessment of kinetics and state dependent binding properties of drugs causing acquired LQTS, Progress in Biophysics and Molecular Biology, 120(1-3), 89–99, doi:10.1016/j.pbiomolbio.2015.12.005. (published) 795. Li, F., Song, F. 2017. A Real-Time Machine Learning and Visualization Framework for Scientific Workflows. Practice & Experience in Advanced Research Computing (PEARC17). (published) 796. Li, J., M. S. Imtiaz, N. A. Beard, A. F. Dulhunty, R. Thorne, D. F. vanHelden, and D. R. Laver (2013), ß-Adrenergic Stimulation Increases RyR2 Activity via Intracellular Ca2+ and Mg2+ Regulation, edited by B. S. Launikonis, PLoS ONE, 8(3), e58334, doi:10.1371/journal.pone.0058334. (published)
RY2 IPR 4 Page 141 797. Lin, L., Xue, Y., Song, F. 2017. A Simpler and More Direct Derivation of System Reliability Using Markov Chain Usage Models. The 29th International Conference on Software Engineering and Knowledge Engineering (SEKE). (published) 798. Liu, F., Shen, F., Chau, D., Bright, N., Belgin, M. 2016. Building a Research Data Science Platform from Industrial Machines. 3rd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH) co-located with IEEE Big Data Conference. (published) 799. Lopez, W., J. Ramachandran, A. Alsamarah, Y. Luo, A. L. Harris, and J. E. Contreras (2016), Mechanism of gating by calcium in connexin hemichannels, Proceedings of the National Academy of Sciences, 113(49), E7986– E7995, doi:10.1073/pnas.1609378113. (published) 800. Luo, Y., A. R. Rossi, and A. L. Harris (2016), Computational Studies of Molecular Permeation through Connexin26 Channels, Biophysical Journal, 110(3), 584–599, doi:10.1016/j.bpj.2015.11.3528. (published) 801. martin, r. 2015. IPL 2015 Cricket Betting tips and expectations. This is most engaging Cricket occasion in India called "India Ka Tyonhar". 8 teams battle for crown. Universal players like Gayle, Miller assuming distinct advantage's part in all sessions of IPL.. rickymartin. (published) 802. Mehra, D., M. S. Imtiaz, D. F. van Helden, B. C. Knollmann, and D. R. Laver (2014), Multiple Modes of Ryanodine Receptor 2 Inhibition by Flecainide, Molecular Pharmacology, 86(6), 696–706, doi:10.1124/mol.114.094623. (published) 803. Meng, X., L. Wang, D. Liu, X. Wen, Q. Zhu, W. A. Goddard, and Q. An (2016), Discovery of Fe2P-Type Ti(Zr/Hf)2O6Photocatalysts toward Water Splitting, Chemistry of Materials, 28(5), 1335–1342, doi:10.1021/acs.chemmater.5b04256. (published) [Stampede, TACC] 804. Oo, Y. W., N. Gomez-Hurtado, K. Walweel, D. F. van Helden, M. S. Imtiaz, B. C. Knollmann, and D. R. Laver (2015), Essential Role of Calmodulin in RyR Inhibition by Dantrolene, Molecular Pharmacology, 88(1), 57–63, doi:10.1124/mol.115.097691. (published) 805. paul, p. 2015. One Day Intercountryal Match Between Charismatic Teams. One day matches have dependably been preferred by the fanatics of cricket. On Friday, seventh August, 2009 the teams of Pakistan and Sri Lanka will play their fourth one day global match against each other in Colombo. The match will begin at 10:00 as indicated by the nearby time. The cricketers of both these teams are splendid and are exceptionally eager for their game. . peterpaul. (published) [PSC] 806. Peng, W., Ranganathan, R., Keblinski, P., Ozisik, R. 2017. Viscoelastic and Dynamic Properties of Well-Mixed and Phase-Separated Binary Polymer Blends: A Molecular Dynamics Simulation Study. Macromolecules. (accepted) 807. Qu, X., Wang, J., Song, F., al., e. 2017. OptiMatch: Enabling an Optimal Match between Green Power and Various Workloads for Renewable-Energy Powered Storage Systems. ICPP 2017. (published) 808. Sharma, A. (2017), A model Scientific Computing course for freshman students at liberal arts Colleges, The Journal of Computational Science Education, 8(2), 2–9, doi:10.22369/issn.2153-4136/8/2/1. (published) 809. Shtukenberg, A. G., C. T. Hu, Q. Zhu, M. U. Schmidt, W. Xu, M. Tan, and B. Kahr (2017), The Third Ambient Aspirin Polymorph, Crystal Growth & Design, 17(6), 3562–3566, doi:10.1021/acs.cgd.7b00673. (published) [Stampede, TACC] 810. Shtukenberg, A. G. et al. (2017), Powder diffraction and crystal structure prediction identify four new coumarin polymorphs, Chem. Sci., 8(7), 4926–4940, doi:10.1039/c7sc00168a. (published) [Stampede, TACC] 811. Thomas, S., Chen, K., Han, J., Purohit, P., Srolovitz, D. 2018. Reconciling Grain Growth and Shear-Coupled Grain Boundary Migration. Nature Communications. (submitted) [Stampede, TACC] 812. Trivedi, M. 2016. Physicochemical, Thermal and Spectroscopic Characterization of Sodium Selenate Using XRD, PSD, DSC, TGA/DTG, UV-vis, and FT-IR. Marmara Pharmaceutical Journal 21:2: 311-318. https://www.trivedieffect.com/the-science/publications/pharmaceuticals-publications/physicochemical- thermal-and-spectroscopic-characterization-of-sodium-selenate-using-xrd-psd-dsc-tgadtg-uv-vis-and-ft-ir/ (published) [Science Gateways] 813. Trivedi, M. 2017. Role of Vital Trace Elements in Nanocurcumin-Centered Formulation: A Novel Approach to Resuscitate the Immune System. Biological Trace Element Research: 1-13. https://www.trivedieffect.com/the-science/publications/nutraceuticals-publications/role-of-vital-trace- elements-in-nanocurcumin-centered-formulation-a-novel-approach-to-resuscitate-the-immune-system/ (published) [Science Gateways] 814. Trivedi, M. 2017. Liquid Chromatography Tandem Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy of Magnesium (II) Gluconate Solution. Journal of Solution Chemistry 46:4: 896–907. https://www.trivedieffect.com/the-science/publications/pharmaceuticals-publications/liquid- chromatography-tandem-mass-spectrometry-and-nuclear-magnetic-resonance-spectroscopy-of-magnesium- ii-gluconate-solution/ (published) [Science Gateways]
RY2 IPR 4 Page 142 815. Trivedi, M. 2017. Effect of a novel ashwagandha-based herbomineral formulation on pro-inflammatory cytokines expression in mouse splenocyte cells: A potential immunomodulator. Pharmacognosy Magazine 13:49: 90-94. https://www.trivedieffect.com/the-science/publications/nutraceuticals-publications/effect-of- a-novel-ashwagandha-based-herbomineral-formulation-on-pro-inflammatory-cytokines-expression-in- mouse-splenocyte-cells-a-potential-immunomodulator/ (published) [Science Gateways] 816. Trivedi, M. 2017. A comprehensive physicochemical, thermal, and spectroscopic characterization of zinc (II) chloride using X-ray diffraction, particle size distribution, differential scanning calorimetry, thermogravimetric analysis/differential thermogravimetric analysis, ultraviolet-visible, and Fourier transform-infrared spectroscopy. International Journal of Pharmaceutical Investigation 7:1: 33-40. https://www.trivedieffect.com/the-science/publications/pharmaceuticals-publications/a-comprehensive- physicochemical-thermal-and-spectroscopic-characterization-of-zinc-ii-chloride-using-x-ray-diffraction- particle-size-distribution-differential-scanning-calorimetry-thermogravime/ (published) [Science Gateways] 817. Trivedi, M. 2017. Immunomodulatory properties and biomarkers characterization of novel Withania somnifera based formulation supplemented with minerals in Sprague Dawley rats. Oriental Pharmacy and Experimental Medicine 17:1: 59-69. https://www.trivedieffect.com/the-science/publications/nutraceuticals- publications/immunomodulatory-properties-and-biomarkers-characterization-of-novel-withania-somnifera- based-formulation-supplemented-with-minerals-in-sprague-dawley-rats/ (published) [Science Gateways] 818. Trivedi, M. 2017. Protective effects of tetrahydrocurcumin (THC) on fibroblast and melanoma cell lines in vitro: it’s implication for wound healing. Journal of Food Science and Technology 54:5: 1137–1145. https://www.trivedieffect.com/the-science/publications/nutraceuticals-publications/protective-effects-of- tetrahydrocurcumin-thc-on-fibroblast-and-melanoma-cell-lines-in-vitro-its-implication-for-wound-healing/ (published) [Science Gateways] 819. Vahidkhah, K., Abbasi, M., Barakat, M., Azadani, P., Tandar, A., et al. 2017. Effect of Reduced Cardiac Output on Blood Stasis on Transcatheter Aortic Valve Leaflets: Implications for Valve Thrombosis.. EuroIntervention: journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology. (published) 820. Vahidkhah, K., Abbasi, M., Javani, S., Azadani, P., Tandar, A., et al. 2017. Valve Thrombosis Following Transcatheter Aortic Valve Replacement: Implications of Reduced Cardiac Output. Journal of the American College of Cardiology 11: 1986. (published) 821. Vahidkhah, K., Azadani, A. 2016. TCT-710 Drawback of the Circumferential Extent of Paravalvular Regurgitation as a Semi-quantitative Parameter to Evaluate the Severity of Transcatheter Aortic Valve Leakage. Journal of the American College of Cardiology 68: B287. (published) 822. Vahidkhah, K., Azadani, A. 2017. Supra-annular Valve-in-Valve implantation reduces blood stasis on the transcatheter aortic valve leaflets. Journal of Biomechanics 58: 114--122. (published) 823. Vahidkhah, K., Barakat, M., Abbasi, M., Javani, S., Azadani, P., et al. 2016. TCT-656 Prolong Blood Stasis on Transcatheter Aortic Valve Leaflets as a Possible Mechanism for Thrombogenesis. Journal of the American College of Cardiology 68: B266. (published) 824. Vahidkhah, K., Barakat, M., Abbasi, M., Javani, S., Azadani, P., et al. 2017. Valve thrombosis following transcatheter aortic valve replacement: significance of blood stasis on the leaflets. European Journal of Cardio-Thoracic Surgery. (published) 825. Vahidkhah, K., Cordasco, D., Abbasi, M., Ge, L., Tseng, E., et al. 2016. Multiscale Analysis of Blood Flow through Aortic Valve Stenosis: Implications on Red Blood Cell Membrane Damage. Cardiology 134: 214--215. (published) 826. Vahidkhah, K., Javani, S., Abbasi, M., Azadani, P., Tandar, A., et al. 2016. Increased Blood Residence Time on Transcatheter Aortic Valve Leaflets as a Precursor Mechanism to Leaflet Thrombosis following Valve-in-Valve Procedure: A Patient-Specific Simulation Study. American Heart Association, Inc.. (published) 827. Vahidkhah, K., Javani, S., Abbasi, M., Azadani, P., Tandar, A., et al. 2017. Blood Stasis on Transcatheter Valve Leaflets and Implications for Valve-in-Valve Leaflet Thrombosis. The Annals of Thoracic Surgery. (published) 828. Van Helden, D. F., P. A. Thomas, P. J. Dosen, M. S. Imtiaz, D. R. Laver, and G. K. Isbister (2014), Pharmacological Approaches That Slow Lymphatic Flow As a Snakebite First Aid, edited by D. G. Lalloo, PLoS Neglected Tropical Diseases, 8(2), e2722, doi:10.1371/journal.pntd.0002722. (published) 829. Von der Weid, P.-Y., S. Lee, M. S. Imtiaz, D. C. Zawieja, and M. J. Davis (2014), Electrophysiological Properties of Rat Mesenteric Lymphatic Vessels and their Regulation by Stretch, Lymphatic Research and Biology, 12(2), 66–75, doi:10.1089/lrb.2013.0045. (published) 830. Walweel, K., J. Li, P. Molenaar, M. S. Imtiaz, A. Quail, C. G. dos Remedios, N. A. Beard, A. F. Dulhunty, D. F. van Helden, and D. R. Laver (2014), Differences in the regulation of RyR2 from human, sheep, and rat by Ca2+and
RY2 IPR 4 Page 143 Mg2+in the cytoplasm and in the lumen of the sarcoplasmic reticulum, The Journal of General Physiology, 144(3), 263–271, doi:10.1085/jgp.201311157. (published) 831. Walweel, K., P. Molenaar, M. S. Imtiaz, A. Denniss, C. dos Remedios, D. F. van Helden, A. F. Dulhunty, D. R. Laver, and N. A. Beard (2017), Ryanodine receptor modification and regulation by intracellular Ca2+ and Mg2+ in healthy and failing human hearts, Journal of Molecular and Cellular Cardiology, 104, 53–62, doi:10.1016/j.yjmcc.2017.01.016. (published) 832. Wetzel, A., J. Bakal, and M. Dittrich (2016), A Virtual File System for On-Demand Processing of Multidimensional Datasets, Proceedings of the XSEDE16 on Diversity, Big Data, and Science at Scale - XSEDE16, doi:10.1145/2949550.2949656. (published) 833. Yang, J., C. T. Hu, X. Zhu, Q. Zhu, M. D. Ward, and B. Kahr (2017), DDT Polymorphism and the Lethality of Crystal Forms, Angewandte Chemie, 129(34), 10299–10303, doi:10.1002/ange.201703028. (published) [Stampede, TACC] 834. Ye, R., de Hoop, M., Petrovitch, C., Pyrak-Nolte, L., Wilcox, L. 2016. A discontinuous Galerkin method with a modified penalty flux for the propagation and scattering of acousto-elastic waves. Geophysical Journal International 205: 1267--1289. (published) 835. Zhang, H.-M., M. S. Imtiaz, D. R. Laver, D. W. McCurdy, C. E. Offler, D. F. van Helden, and J. W. Patrick (2014), Polarized and persistent Ca2+ plumes define loci for formation of wall ingrowth papillae in transfer cells, Journal of Experimental Botany, 66(5), 1179–1190, doi:10.1093/jxb/eru460. (published) 836. Zhu, Q., A. R. Oganov, A. O. Lyakhov, and X. Yu (2015), Generalized evolutionary metadynamics for sampling the energy landscapes and its applications, Physical Review B, 92(2), doi:10.1103/physrevb.92.024106. (published) [Stampede, TACC] 837. Zhu, Q., Oganov, A., Zeng, Q. 2015. Formation of Stoichiometric CsFn Compounds. Scientific Reports 1. http://dx.doi.org/10.1038/srep07875 DOI:10.1038/srep07875 (Invalid?). (published) [Stampede, TACC] 838. Zhu, Q. et al. (2016), Resorcinol Crystallization from the Melt: A New Ambient Phase and New “Riddles,” Journal of the American Chemical Society, 138(14), 4881–4889, doi:10.1021/jacs.6b01120. (published) [Stampede, TACC] 839. Zigon, B., Zhu, L., Song, F. 2017. Interactive 3D Simulation for Fluid-Structure Interactions Using Two GPUs (submitted). Journal of Supercomputing. (published)
RY2 IPR 4 Page 144 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 follow list represents the active collaborations with NSF awardees: PI/Contact NSF Award Title Award Abstract Allen Pope EarthCube RCN: Collaborative Research: Research 1541620 Coordination Network for High Performance Distributed Computing in the Polar Sciences Kate Keahey A Large-Scale, Community-Driven Experimental Environment 1419141 for Cloud Research Shaowen Wang MRI: Acquisition of a National CyberGIS Facility for 1429699 Computing- and Data-Intensive Geospatial Research and Education Todd Martinez Acquisition of an Extreme GPU cluster for Interdisciplinary 1429830 Research Donna Cox The Centrality of Advanced Digitally-ENabled Science: 1445176 CADENS Robert Ricci CloudLab: Flexible Scientific Infrastructure to Support 1419199 Fundamental Advances in Cloud Architectures and Applications Ron Howkins CC* Compute: BioBurst in response to the Campus 1659104 Cyberinfrastructure (CC*) Program solicitation Thomas Hauser MRI Collaborative Consortium: Acquisition of a Shared 1532236 Supercomputer by the Rocky Mountain Advanced Computing Consortium Marlon Pierce Open Gateway Computing Environments Science Gateways 1339774 Platform as a Service (OGCE SciGaP) Steven Tuecke Sustaining Globus Toolkit for the NSF Community (Sustain-GT) 1339873 Edward Seidel BD Hubs: Midwest: “SEEDCorn: Sustainable Enabling 1550320 Environment for Data Collaboration that you are proposing in response to the NSF Big Data Regional Innovation Hubs (BD Hubs): Accelerating the Big Data Innovation Ecosystem (NSF 15-562) solicitation Alexander Withers Secure Data Architecture: Shared Intelligence Platform for 1547249 Protecting our National Cyberinfrastructure” that you are proposing in response to the NSF Cybersecurity Innovation for Cyberinfrastructure (NSF 15-549) solicitation James Basney CILogon 2.0 project that you are proposing in response to the 1547268 NSF Cybersecurity Innovation for Cyberinfrastructure (NSF 15-549) solicitation Farzad Mashayek CC* Networking Infrastructure: Building HPRNet (High- 1659255 Performance Research Network) for advancement of data intensive research and collaboration
RY2 IPR 4 Page 145 Kerk F. Kee & Almadena Y. RUI: CAREER Organizational Capacity and Capacity Building 1453864 Chtchelkanova for Cyberinfrastructure Diffusion Bertram Ludaescher DIBBs: Merging Science and Cyberinfrastructure Pathways: 1541450 The Whole Tale Philip J. Puxley Associated Universities, Inc. (AUI) and the National Radio 1519126 Astronomy Observatory (NRAO) Thomas Crawford Molecular Sciences Software Institute (MolSSI) that you are 1547580 proposing in response to the NSF Scientific Software Innovation Institutes (S2I2, NSF 15-553) solicitation Jerry Bernholc SI2-SSE: Multiscale Software for Quantum Simulations of 1615114 Nanostructured Materials and Devices Nancy Wilkins-Diehr Science Gateways Software Institute for NSF Scientific 1547611 Software Innovation Institutes Dirk Colbry Cybertraining:CIP – Professional Training for 1730137 CyberAmbassadors The following list represents other active formal domestic and international collaborations: Project Collaboration Summary Domestic Collaborations Marshall University - Campus Indiana University XCRI staff visited Marshall University for server build and Bridging Site (XCRI) XSEDE software toolkit installation Southern Illinois University - Indiana University XCRI staff visited Southern Illinois University for server Campus Bridging Site (XCRI) build and XSEDE software toolkit installation Bentley University - Campus Indiana University XCRI staff visited Bentley University for server build and Bridging Site (XCRI) XSEDE software toolkit installation University Texas El Paso - Campus Indiana University XCRI staff visited University Texas El Paso for server build Bridging Site (XCRI) and XSEDE software toolkit installation Brandeis University - Campus Indiana University XCRI staff visited Brandeis University for server build and Bridging Site (XCRI) 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 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 Use of XRAS by NCAR Agreement to use XRAS for manage the NCAR allocations Use of XRAS by Blue Waters Agreement to use XRAS for manage the Blue Waters allocations 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.
RY2 IPR 4 Page 146 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 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.
RY2 IPR 4 Page 147 14. Service Provider Forum Report Service Providers (SPs) are independently funded projects or organizations that provide cyberinfrastructure (CI) services to the science and engineering community. In the US academic community, there is a rich diversity of Service Providers, 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 local researchers. The Service Provider Forum is intended to facilitate this ecosystem of Service Providers, thereby advancing the science and engineering researchers that rely on these cyberinfrastructure services. The Service Provider Forum (SPF) has two primary elements of its charter: An open forum for discussion of topics of interest to the SP community. A formal communication channel between the SPF members and the XSEDE project. The SPF conducts its business primarily through regular 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://www.xsede.org/group/sp-forum/wiki). NSF Program Officers are invited and often participate. Many people from the XSEDE program routinely participate to facilitate direct interaction with the XSEDE program, including John Towns (XSEDE PI), Victor Hazlewood (XSEDE SP Coordinator), and Ken Hackworth (XSEDE Allocations Coordinator), regularly participate in the SP Forum meetings. Additional contributors from XSEDE, XD-TAS and other organizations are frequently invited to lead or support specific agenda topics. During the reporting period, the Service Provider Forum (SPF) continued in the two primary elements of its charter: Providing an open forum for discussion of topics of interest to the SP community, and providing a formal communication channel between the SPF members and the XSEDE project. Technical and XSEDE specific actions and discussions During the quarter, the SP Forum discussed a number of topics relevant to both XSEDE and the individual membership including: The onboarding of new SPs, and particularly novel resources and how XSEDE can aid in this process. This is an ongoing conversation that will result in formal feedback to XSEDE in the next quarter. The growth of the use of containers across SPs – the Comet team led a webinar (with participation from SDSC, PSC, TACC, IU, and NCAR) on this topic open to the user community. The Forum discussed within the members themselves and with the XSEDE project the potential impact of the changes to the open source model of the Globus tools, and produced an Impact Analysis report. A discussion of what SPs are doing with respect to the use of sensitive information by users on the compute resources. Working with operations, the Forum members made updates to the XSEDE RDR, and also covered updates from XCI on the tools to be covered. New Federation members The Forum continues to grow and thrive, with a new application received for Level 3 membership from North Dakota State University.
RY2 IPR 4 Page 148 15. UAC
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 meeting of the UAC during this reporting period which took place on 27 September 2017. Six UAC members were present, including Diego Donzis, Mark Jack, Mark Miller, Jeff Pummill, Deirdre Shoemaker and Chair Emre Brookes. XSEDE staff present included John Cazes, Dave Hart, Ralph Roskies, Sergiu Sanielevici and John Towns. Presenters included John Cazes, Dave Hart and John Towns. Proceedings began with an introduction of Sergiu Sanielevici as the XSEDE interim L2 director for ECSS and, as such, the XSEDE UAC contact, replacing the recently retired Ralph Roskies. The first presentation was from Dave Hart who provided an update on the XSEDE Allocation Committee’s Final Policy Report. A primary point that led subsequent discussion was how do define service units (SUs, historically defined as core-hours) in a “post-SU” era. One major resource provider switched from core hours to node hours. SUs are used for threshold determination (startup, small, medium and large requests). Threshold determination is an important factor for disposition and review of allocation requests. Pursuant discussion included how much detail about threshold calculations should be available to the PI’s during submission of allocation requests. The next presentation was an XSEDE update from John Towns. Feedback was requested from the UAC members on relevant measures of impact of XSEDE on science and the community. This has been discussed previously and many measures of impact are included in XSEDE reporting. Nevertheless, XSEDE is continually seeking fresh perspectives. Subsequent discussion included one committee member’s suggestion of personal interviews with users. The additional point of including questions related to impact on the careers of students was raised. The final presentation was from John Cazes regarding the transition to Stampede2 and its effect on users. Stampede2 is quite different than the previous Stampede or other traditional high performance computing resources as each node contains many more less yet less capable cores. Stampede2 provides an increased aggregate performance, yet code transition requires effort. This transition presents a significant challenge to the user wishing to port their code from traditional resources. Techniques recommended to maximize performance on Stampede2 nodes include threading, vectorizing and streamlining memory access. Versions of a handful of popular community codes, such as NAMD (used for molecular dynamics) have undergone this transition and are available for users. Training resources are available for users wishing to port their codes to Stampede2. Discussion followed with committee members reporting their experiences, including “...[apparently] a very high barrier to start using…”, “... [we were] not using the [right] compiler flags so … ran slower … we didn’t find any big issues”, “we have about 20% of one FTE … [the effort] isn’t possible for us right now”, and “Unfortunately we actually had to pull out of Stampede [2] [due to porting issues] - we had run on Stampede1 for years … ”. Further discussion ensued on making it easier for users, including a recommendation that pre-configured test cases
RY2 IPR 4 Page 149 be made available for users to gain familiarity. It was noted that recent allocation requests show a lack of understanding by the submitters on this transition. After the Stampede2 presentation, the meeting closed. There were no other actions of the UAC during this reporting period.
RY2 IPR 4 Page 150 16. XMS Summary 16.1. Executive Summary There was a new major release of Open XDMoD during the reporting period, namely Open XDMoD 7.0. The new version adds the following features. A new data flow chart in the dashboard was added that is intended to aide in both the initial set-up and troubleshooting of Open XDMoD installs and in the routine maintenance of Open XDMoD instances. A Gantt chart has been added to the Job Viewer to show the user all of the peer jobs that ran concurrently with the user’s job. This has proven useful in user support to determine if the job execution was affected by other peer jobs that ran on the same node as the user’s job. A new feature was implemented in the Job Viewer that allows users to publish a functional URL link to share their Job Viewer data. Minor improvements to XDMoD include: adding a center director template to the custom report generator and improvement of the Load Chart dialog box to make it easier to track XDMoD charts. There was also substantial progress on the Scientific Impact task with the XSEDE and non-XSEDE publication citations analyzed by field-of-science. The data analysis demonstrates the significant advantage that the availability of the XSEDE resources provides to XSEDE resource users. 16.2. XMS Findings 1. There was a major release of Open XDMoD, version 7.0. 2. A new Open XDMoD data flow-chart was made available to Open XDMoD developers and managers. 3. A Gantt chart has been added to the XDMoD Job Viewer that shows all of the other jobs that run concurrently with the user’s job. 4. A new feature has been added to the Job Viewer that allows the user to generate a URL link that will allow all of the data from the Job Viewer to be shared. 5. A new quarterly report template for center directors was added to the XDMoD custom report generator. 16.3. XMS Recommendations 1. The new Open XDMoD data flow-chart should be used to monitor the installation of Open XDMoD and for routine maintenance. 2. The peer job Gantt chart should be used in user support to determine if other concurrent jobs on the same node caused a problem with the user’s job. 3. The new Job Viewer data sharing feature can be used by user support personnel to share job data with the user or vice versa. 4. Center directors should take advantage of the new report template to routinely monitor their systems.
RY2 IPR 4 Page 151