Foundry-ML - Software and Services to Simplify Access
to Machine Learning Datasets in Materials Science

Schmidt, KJ; Scourtas, Aristana; Ward, Logan; Wangen, Steve; Schwarting, Marcus; Darling, Isaac; Truelove, Ethan; Ambadkar, Aadit; Bose, Ribhav; Katok, Zoa; Wei, Jingrui; Li, Xiangguo; Jacobs, Ryan; Schultz, Lane; Kim, Doyeon; Ferris, Michael; Voyles, Paul M.; Morgan, Dane; Foster, Ian; Blaiszik, Ben

doi:10.21105/joss.05467

JOSS

2024

DOI: 10.21105/joss.05467

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Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science

KJ Schmidt,

Aristana Scourtas,

Logan Ward

et al.

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Cited by 2 publications

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Machine learning in materials research: Developments over the last decade and challenges for the future

Jain

2024

Current Opinion in Solid State and Materials Science

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Machine learning in materials research: Developments over the last decade and challenges for the future

Jain

2024

Current Opinion in Solid State and Materials Science

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Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

Jacobs,

Schultz,

Scourtas

et al. 2024

Mach. Learn.: Sci. Technol.

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One compelling vision of the future of materials discovery and design involves the use of machine learning (ML) models to predict materials properties and then rapidly find materials tailored for specific applications. However, realizing this vision requires both providing detailed uncertainty quantification (model prediction errors and domain of applicability) and making models readily usable. At present, it is common practice in the community to assess ML model performance only in terms of prediction accuracy (e.g. mean absolute error), while neglecting detailed uncertainty quantification and robust model accessibility and usability. Here, we demonstrate a practical method for realizing both uncertainty and accessibility features with a large set of models. We develop random forest ML models for 33 materials properties spanning an array of data sources (computational and experimental) and property types (electrical, mechanical, thermodynamic, etc). All models have calibrated ensemble error bars to quantify prediction uncertainty and domain of applicability guidance enabled by kernel-density-estimate-based feature distance measures. All data and models are publicly hosted on the Garden-AI infrastructure, which provides an easy-to-use, persistent interface for model dissemination that permits models to be invoked with only a few lines of Python code. We demonstrate the power of this approach by using our models to conduct a fully ML-based materials discovery exercise to search for new stable, highly active perovskite oxide catalyst materials.

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Foundry-ML - Software and Services to Simplify Access to Machine Learning Datasets in Materials Science

Cited by 2 publications

References 14 publications

Machine learning in materials research: Developments over the last decade and challenges for the future

Machine learning in materials research: Developments over the last decade and challenges for the future

Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

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