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

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 material...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Allec, Sarah I, Muckley, Eric S, Johnson, Nathan S, Borg, Christopher K H, Kirsch, Dylan J, Martin, Joshua, Pant, Rohit, Takeuchi, Ichiro, Lee, Andrew S, Saal, James E, Ward, Logan, Mehta, Apurva
Format: Article
Language:English
Subjects:
Automation
Case studies
Combinatorial analysis
Dashboards
Data management
Datasets
Diffraction patterns
Functional materials
Infrastructure
Machine learning
Material properties
Materials information
Materials science
Synthesis
Thermoelectric materials
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Summary: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. 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.
ISSN:2331-8422