Toward machine learning interatomic potentials for modeling uranium mononitride

2024/11/21 by Lorena Alzate-Vargas, Alzate-Vargas, Lorena, Kashi N. Subedi +11 · 3 citations
Materials Science · Physics and Astronomy · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Nuclear Materials and Properties #Nuclear Physics and Applications

paper · pdf · doi:10.48550/arxiv.2411.14608

openalex publication_date 2024/11/21 · openalex created_date 2024/12/04 · openalex updated_date 2026/07/28

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.

Cited by

• Machine learning interatomic potential for predicting the thermal properties of uranium nitride
• Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
• Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials

Related

• Machine learning interatomic potential for predicting the thermal properties of uranium nitride
• Atomistic modeling of uranium monocarbide with a machine learning interatomic potential
• Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
• Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
• Beyond potentials: integrated machine-learning models for materials