Toward machine learning interatomic potentials for modeling uranium mononitride

Alzate-Vargas, Lorena [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000242234046); Subedi, Kashi Nath [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000262373801); Lubbers, Nicholas Edward [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000290019973); Cooper, Michael William Donald [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000182707947); Tutchton, Roxanne M'liss [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000185213504); Gibson, Tammie Renee [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)]; Messerly, Richard Alma [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000313214246)

doi:10.1088/2632-2153/ae0242

DOE OSTI · 2588095

Toward machine learning interatomic potentials for modeling uranium mononitride

Alzate-Vargas, Lorena [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000242234046)·Subedi, Kashi Nath [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000262373801)·Lubbers, Nicholas Edward [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000290019973)·Cooper, Michael William Donald [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000182707947)·Tutchton, Roxanne M'liss [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000185213504)·Gibson, Tammie Renee [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)]·Messerly, Richard Alma [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000313214246)

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Abstract

Uranium mononitride (UN) is a promising accident-tolerant fuel because of its high fissile density and high thermal conductivity. In this study, we developed the first machine learning interatomic potentials for reliable atomic-scale modeling of UN at finite temperatures. We constructed a training set using density functional theory (DFT) calculations that was enriched through an active learning procedure, and two neural network potentials were generated. Both potentials successfully reproduce key thermophysical properties of interest, such as temperature-dependent lattice parameter, specific heat capacity, and bulk modulus. We also evaluated the energy of stoichiometric defect reactions and defect migration barriers and found close agreement with DFT predictions, demonstrating that our potentials can be used for modeling defects in UN. Additional tests provide evidence that our potentials are reliable for simulating diffusion, noble gas impurities, and radiation damage.

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11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS 36 MATERIALS SCIENCE Machine learning interatomic potentials molecular dynamics nuclear fuels

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Alzate-Vargas, Lorena [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000242234046), Subedi, Kashi Nath [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000262373801), Lubbers, Nicholas Edward [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000290019973), Cooper, Michael William Donald [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000182707947), Tutchton, Roxanne M'liss [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000185213504), Gibson, Tammie Renee [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)], Messerly, Richard Alma [Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)] (ORCID:0000000313214246). 2025-09-24. Toward machine learning interatomic potentials for modeling uranium mononitride. https://doi.org/10.1088/2632-2153%2Fae0242

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