Atomistic modeling of uranium monocarbide with a machine learning interatomic potential

TL;DR

This paper develops a machine learning interatomic potential for uranium monocarbide, enabling accurate large-scale simulations of its properties and behaviors critical for nuclear fuel applications.

Contribution

The study introduces a novel MLIP for UC, trained via active learning, to accurately predict its properties and facilitate advanced molecular dynamics simulations.

Findings

MLIP accurately predicts structural and thermophysical properties

Aligns well with experimental and theoretical data

Enables efficient large-scale simulations of UC

Abstract

Uranium monocarbide (UC) is an advanced ceramic fuel candidate due to its superior uranium density and thermal conductivity compared to traditional fuels. To accurately model UC at reactor operating conditions, we developed a machine learning interatomic potential (MLIP) using an active learning procedure to generate a comprehensive training dataset capturing diverse atomic configurations. The resulting MLIP predicts structural, elastic, thermophysical properties, defect formation energies, and diffusion behaviors, aligning well with experimental and theoretical benchmarks. This work significantly advances computational methods to explore UC, enabling efficient large-scale and long-time molecular dynamics simulations essential for reactor fuel qualification.