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A case study of multi-modal, multi-institutional data management for the combinatorial materials science community

Published

Author(s)

Sarah Allec, Eric Muckley, Nathan Johnson, Christopher Borg, Dylan Kirsch, Joshua B. Martin, Ichiro Takeuchi, Andrew Lee, James Saal, Logan Ward, Apruva Mehta

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 multi-modal 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.
Citation
Integrating Materials and Manufacturing Innovation

Keywords

data management, machine learning, thermoelectric, characterization

Citation

Allec, S. , Muckley, E. , Johnson, N. , Borg, C. , Kirsch, D. , Martin, J. , Takeuchi, I. , Lee, A. , Saal, J. , Ward, L. and Mehta, A. (2024), A case study of multi-modal, multi-institutional data management for the combinatorial materials science community, Integrating Materials and Manufacturing Innovation, [online], https://doi.org/10.1007/s40192-024-00345-7, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957305 (Accessed April 19, 2024)
Created March 22, 2024, Updated March 25, 2024