Expanding Universal Machine Learning Interatomic Potentials to 97 Elements Towards Nuclear Applications

TL;DR

This paper develops a universal machine learning interatomic potential covering 97 elements, including heavy actinides, enabling faster and accurate simulations for nuclear materials and energy applications.

Contribution

The authors created the broadest elemental coverage MLIP to date by integrating a new heavy element dataset with existing data, expanding applicability to nuclear materials.

Findings

Strong performance on inorganic and organic test sets

Promising accuracy on heavy element dataset

Open-source model facilitates nuclear material research

Abstract

Machine learning interatomic potentials (MLIPs) evaluate potential energy surfaces orders of magnitude faster while maintaining accuracy comparable to first-principles calculations, and universal MLIPs that cover most of the periodic table are becoming increasingly commonplace. However, existing large-scale datasets have limited or no coverage of heavy elements such as minor actinides crucial in the nuclear field, and universal MLIPs are typically limited to 89 elements. Here, we constructed a heavy element dataset HE26 containing minor actinides, based on experimental and computational literature data. By integrating this with existing molecular and crystal datasets, we developed an open-source universal MLIP covering 97 elements, the broadest elemental coverage to date. The resulting model showed strong performance on the inorganic MPtrj and organic OFF23 test sets and promising accuracy on HE26. The dataset and model open a pathway toward the development of energy resources and the design of novel materials, such as actinide-based high-entropy ceramics, in the nuclear field.