Machine learning interatomic potential for predicting the thermal properties of uranium nitride

TL;DR

This paper develops a machine learning interatomic potential for uranium nitride, enabling accurate predictions of its thermal properties through combined computational and experimental validation.

Contribution

The study introduces a novel MLIP trained on DFT data for UN, validated against experiments, and used to predict thermal properties with high accuracy.

Findings

MLIP accurately predicts thermal conductivity, melting point, and thermal expansion.

Strong agreement between MLIP predictions and experimental measurements.

The approach enables reliable simulations of UN thermal behavior.

Abstract

We present a combined computational and experimental investigation of the thermal properties of uranium nitride (UN), focusing on the development of a machine learning interatomic potential (MLIP) using the moment tensor potential (MTP) framework. The MLIP was trained on density functional theory (DFT) data and validated against various quantities including energies, forces, elastic constants, phonon dispersion, and defect formation energies, achieving excellent agreement with DFT calculations, prior experimental results and our thermal conductivity measurement. The potential was then employed in molecular dynamics (MD) simulations to predict key thermal properties such as melting point, thermal expansion, specific heat, and thermal conductivity. To further assess model accuracy, we fabricated a UN sample and performed new thermal conductivity measurements representative of single-crystal properties, which showed strong agreement with the MLIP predictions. This work confirms the reliability and predictive capability of the developed potential for determining the thermal properties of UN.