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III. Overview of Presentations on MDA...5 Acknowledgments Materials Data Analytics: A Path-Finding Workshop Report was prepared by Nexight Group under the guidance of ASM International staff: Scott Henry, Director, Content & Knowledge-Based Solutions and Larry Berardinis, Technical Projects Manager, CMD Network. The work was sponsored by the National Institute of Standards and Technology (NIST). On behalf of ASM, we would like to express our appreciation to the participants in the Materials Data Analytics Workshop (see Appendix A) for their input and recommendations. DISCLAIMER This report represents the opinions of the workshop participants and not necessarily that of their home organizations or their affiliated professional societies. Cover graphic adapted from M.C. Flemings and R.W. Cahn, Organization and Trends in MSE Education in the US and in Europe, Acta Mater., 48, 2000, pp.371-383. Table of Contents I. Executive Summary ............................................................................................................................... 2 II. Background and Workshop Objectives ................................................................................................. 4 III. Overview of Presentations on MDA...................................................................................................... 5 IV. Challenges to Advancing MDA .............................................................................................................. 7 V. Priority Applications and Opportunities to best leverage MDA Tools .................................................. 9 VI. Near-Term Pathways for MDA Development ..................................................................................... 11 VII. Supporting Needs ................................................................................................................................ 17 VIII. Concluding Remarks and Next Steps .................................................................................................. 18 Appendix A. List of Participants .................................................................................................................. 19 Appendix B. Workshop Agenda .................................................................................................................. 20 Appendix C. Summary of Prior Work .......................................................................................................... 21 I. Executive Summary Materials data analytics (MDA)—an emerging discipline that helps researchers extract knowledge and insights from materials data—will play a critical role in enabling multiple stakeholders to discover, design, develop, and deploy new materials twice as fast at half the cost, per the goals of the Materials Genome Initiative (MGI). While many researchers across disciplines are working independently to develop and use MDA to make their work more efficient, a coordinated approach has yet to be conducted that leverages existing knowledge and outlines a path to drive MDA forward. To address this issue, ASM International convened Materials Data Analytics: A Path-Finding Workshop on October 8‐9, 2015 at The Ohio State University in Columbus, Ohio. The workshop, sponsored by the National Institute for Standards and Technology (NIST), brought together a select group of more than 30 representatives from academia, industry, and government who are currently using and contributing to the development of MDA approaches (see Appendix A for a complete list of participants). Through a series of professionally facilitated sessions, workshop participants shared their thoughts and ideas about the challenges, applications, and opportunities for the advancement of MDA. The resulting dialogue, captured in this report, provides a deeper understanding of the current state and impact potential of MDA and identifies critical pathways and actions to accelerate its development and help the MGI community more quickly achieve its goals. Key High-Level Findings 1. While the materials community actively adapts, develops, and uses MDA algorithms for their R&D activities (see Section III and Appendix C), now is the time to pursue a deeper understanding of the current pitfalls and unexplored realms of MDA for further opportunities and growth. 2. Advancing MDA to achieve the MGI goals requires more than just developing computer algorithms to solve individual materials problems. It is also necessary to establish a collaborative computational environment with shared resources and develop a clear understanding of uncertainty in materials data and information. 3. MDA requires highly coordinated and concerted efforts across academia, industry, society, and government. There is an immediate need for collaboration with communities of different disciplines (e.g., computer science, bioinformatics) to learn from their experiences in data analytics and utilize tools and techniques proven to be effective. Materials Data Analytics: A Path-Finding Workshop 2 Specifically, the workshop identified the following challenges, priorities, and near-term pathways to provide an actionable path toward the advancement and increased use of MDA in the development and deployment of new materials. Detailed Summary of Workshop Findings Top Five Challenges to Advancing MDA Understanding uncertainty in data and models Lack of data/knowledge sharing Complexity of multiscale optimization Limited decision-support resources Extracting knowledge from literature-based resources Materials and Technical Applications with High Impact Potential High-temperature structural alloys (e.g., materials for turbine engines) High entropy alloys and metallic glasses Combinatorial materials development Semi-autonomous experimentation Opportunities for Advancing MDA Tools Category Highest priority opportunity Automate mining and curation of legacy data In-service data infrastructure Develop support resources for decisions (making existing data available to MDA) (maintenance through data fusion) Integrated design and discovery cycle using Materials discovery and design feedback from materials in use and their application to development Establish quantitative engineering standards and Engineering design and manufacturing materials certification certification Integrate quality control with reliability engineering Pathways for MDA Development (see Tables 4-8 for associated action plans) Pathway Challenge addressed Establish quantitative engineering standards Understanding uncertainty in data and models and materials certification Establish in-service data infrastructure for Lack of data/knowledge sharing MDA Advance combinatorial materials science Complexity of multiscale optimization Develop support resources for decisions Limited decision-support resources (maintenance through data fusion) Automate mining and curation of legacy data Extracting knowledge from literature-based resources Materials Data Analytics: A Path-Finding Workshop 3 II. Background and Workshop Objectives Materials data analytics (MDA) Figure 1. Fleming’s Tetrahedron with MDA Perspective applies principles from materials science and engineering, physics, applied mathematics, and information/computer science to extract knowledge and insights from quantitative process‐ structure‐property‐performance relationships hidden in materials data (see Figure 1). By providing researchers with this information, MDA will play a critical role in accelerating the development and deployment of new materials and predicting how they will function in specific applications. 1 The MGI Strategic Plan released in MDA extracts knowledge and insights from quantitative process- December 2014 clearly defines the structure-property-performance relationships hidden in materials role that MDA can play in achieving data. the goals of the MGI and the need Graphic adapted from M.C. Flemings and R.W. Cahn, Organization and Trends to develop a better understanding in MSE Education in the US and in Europe, Acta Mater., 48, 2000, pp.371-383. of MDA tools and techniques: Objective 2.4 (The MGI Strategic Plan): Develop Data Analytics to Enhance the Value of Experimental and Computational Data “...The availability of high-quality experimental and computational data … presents an opportunity for data mining and analysis to expand and accelerate discovery of new materials and predictions of materials with new functionalities.” Milestone 2.4.1: Convene a pathfinding workshop focusing on the status of computational tools for data analytics for applications emerging from materials sciences and engineering. To address this need, ASM International with support from NIST convened the Materials Data Analytics: A Path-Finding Workshop on October 8–9, 2015 with the following goals: 1. Assess the state-of-the-art in MDA, identifying the current state as well as gaps in the knowledge and tools. 2. Identify opportunities and high-priority directions for research and development as well as the application of MDA tools. 3. Share the outcomes to stimulate targeted future work. 1 https://www.whitehouse.gov/sites/default/files/microsites/ostp/NSTC/mgi_strategic_plan_-_dec_2014.pdf Materials Data Analytics: A Path-Finding Workshop 4 III. Overview of Presentations on MDA A select group of experts led off the workshop, giving presentations on the incorporation of data analytics into materials genomic approaches and the benefits of using of MDA to analyze massive amounts of materials data. Table 1 provides a summary of the presentations and offers a glimpse of the broader
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