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A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community

  • Thematic Section: Harnessing the Power of Materials Data
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Abstract

Although the convergence of high-performance computing, automation, and machine learning has significantly altered the materials design timeline, transformative advances in functional materials and acceleration of their design will require addressing the deficiencies that currently exist in materials informatics, particularly a lack of standardized experimental data management. The challenges associated with experimental data management are especially true for combinatorial materials science, where advancements in automation of experimental workflows have produced datasets that are often too large and too complex for human reasoning. The data management challenge is further compounded by the multimodal and multi-institutional nature of these datasets, as they tend to be distributed across multiple institutions and can vary substantially in format, size, and content. Furthermore, modern materials engineering requires the tuning of not only composition but also of phase and microstructure to elucidate processing–structure–property–performance relationships. To adequately map a materials design space from such datasets, an ideal materials data infrastructure would contain data and metadata describing (i) synthesis and processing conditions, (ii) characterization results, and (iii) property and performance measurements. Here, we present a case study for the low-barrier development of such a dashboard that enables standardized organization, analysis, and visualization of a large data lake consisting of combinatorial datasets of synthesis and processing conditions, X-ray diffraction patterns, and materials property measurements generated at several different institutions. While this dashboard was developed specifically for data-driven thermoelectric materials discovery, we envision the adaptation of this prototype to other materials applications, and, more ambitiously, future integration into an all-encompassing materials data management infrastructure.

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Code Availability

The source code for the data portal described in this work is currently available on Bitbucket at the following link: https://bitbucket.org/tecca-data-portal/workspace/snippets/rqnMyX/source-code#file-data-portal-src.zip. Note that this code is made available for reference only, as running the dashboard requires access to a private Globus endpoint.

Notes

  1. Certain commercial equipment, instruments, software, or materials are identified in this document. Such identification does not imply recommendation or endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for the purpose.

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Acknowledgements

This work was partially supported by the US Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE), specifically the Advanced Materials & Manufacturing Technologies Office (AMMTO), under contract DE-AC02-76SF00515.

Funding

Advanced Materials and Manufacturing Technologies Office, DE-AC02-76SF00515, Apurva Mehta

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Authors and Affiliations

  1. Citrine Informatics, Redwood City, CA, 94063, USA

    Sarah I. Allec, Eric S. Muckley, Christopher K. H. Borg & James E. Saal

  2. SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA

    Nathan S. Johnson & Apurva Mehta

  3. Department of Materials Science & Engineering, University of Maryland, College Park, MD, 20742, USA

    Dylan J. Kirsch, Rohit Pant & Ichiro Takeuchi

  4. Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA

    Dylan J. Kirsch & Joshua Martin

  5. Department of Materials Science & Engineering, Northwestern University, Evanston, IL, 60208, USA

    Andrew S. Lee

  6. Argonne National Laboratory, Lemont, IL, 60439, USA

    Andrew S. Lee & Logan Ward

  7. 11:59, Sacramento, CA, 95822, USA

    Eric S. Muckley

  8. ZEISS Microscopy, Dublin, CA, 94568, USA

    Nathan S. Johnson

  9. NobleAI, San Francisco, CA, 94104, USA

    Christopher K. H. Borg

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  1. Sarah I. Allec
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Correspondence to James E. Saal.

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Allec, S.I., Muckley, E.S., Johnson, N.S. et al. A Case Study of Multimodal, Multi-institutional Data Management for the Combinatorial Materials Science Community. Integr Mater Manuf Innov (2024). https://doi.org/10.1007/s40192-024-00345-7

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