IMS Rapid Response 2024 Summary Report: A Machine Learning Potential for the Periodic Table

Taylor, Michael Gilbert; Lubbers, Nicholas Edward

doi:10.2172/2460463

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DOE OSTI · 2460463

IMS Rapid Response 2024 Summary Report: A Machine Learning Potential for the Periodic Table

Taylor, Michael Gilbert·Lubbers, Nicholas Edward

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Abstract

Stockpile stewardship and nuclear waste remediation are inherently chemically complex, involving practically the full diversity of the periodic table, but existing methods are too expensive or not functional for a large diversity of atom types. Overall, the field of machine learning interatomic potentials (MLIPs) has advanced dramatically in 2024 with large high-accuracy datasets existing for bulk, surface, and organic chemical systems and new online leaderboards for diverse chemistry. To participate in, and bring LANL interests into this ecosystem, here, we have built upon existing technologies created by LANL to create a framework capable of creating machine learning interatomic potentials (MLIPs) for over 90 atom types. Our results have created a massively diverse coordination complex training dataset more than 3 times the size of existing datasets, parallelized MLIP training over multiple GPUs, enabling the training of an MLIP spanning the periodic table at 20 times the speed of prior training on 32 GPUs. These advances are substantial towards creation on foundational MLIPs for LANL-specific application areas.

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY Inorganic Chemistry Physical Chemistry

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Taylor, Michael Gilbert, Lubbers, Nicholas Edward. 2024-10-08. IMS Rapid Response 2024 Summary Report: A Machine Learning Potential for the Periodic Table. https://doi.org/10.2172/2460463

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