Machine learning potential assisted exploration of complex defect potential energy surfaces

Jiang, Chao; Marianetti, Chris A.; Khafizov, Marat; Hurley, David H.

doi:10.1038/s41524-024-01207-8

Title: Machine learning potential assisted exploration of complex defect potential energy surfaces

Journal Article · 23 January 2024 · npj Computational Materials

DOI: https://doi.org/10.1038/s41524-024-01207-8 · OSTI ID:2282658

; Marianetti, Chris A.;

; Hurley, David H.

Abstract Atomic-scale defects generated in materials under both equilibrium and irradiation conditions can significantly impact their physical and mechanical properties. Unraveling the energetically most favorable ground-state configurations of these defects is an important step towards the fundamental understanding of their influence on the performance of materials ranging from photovoltaics to advanced nuclear fuels. Here, using fluorite-structured thorium dioxide (ThO ₂ ) as an exemplar, we demonstrate how density functional theory and machine learning interatomic potential can be synergistically combined into a powerful tool that enables exhaustive exploration of the large configuration spaces of small point defect clusters. Our study leads to several unexpected discoveries, including defect polymorphism and ground-state structures that defy our physical intuitions. Possible physical origins of these unexpected findings are elucidated using a local cluster expansion model developed in this work.

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Nuclear Energy (NE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 2282658

Report Number(s):: INL/JOU--23-74456-Revision-0; 21; PII: 1207

Journal Information:: npj Computational Materials, Journal Name: npj Computational Materials Journal Issue: 1 Vol. 10; ISSN 2057-3960

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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