Towards Accurate Thermal Property Predictions in Uranium Nitride using Machine Learning Interatomic Potential

Journal Article · Mon Nov 24 00:00:00 EST 2025 · Journal of Applied Physics

DOI:https://doi.org/10.1063/5.0294389· OSTI ID:3013640

Chen, Beihan; Hua, Zilong ^[1]; Watkins, Jennifer; Malakkal, Linu ^[1]

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1. Idaho National Laboratory (INL), Idaho Falls, ID (United States)

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 out 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 show 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.

• Research Organization: Idaho National Laboratory (INL)
• Sponsoring Organization: USDOE Office of Nuclear Energy (NE)
• Grant/Contract Number: AC07-05ID14517
• OSTI ID: 3013640
• Report Number(s): INL/JOU-25-85882
• Journal Information: Journal of Applied Physics, Journal Name: Journal of Applied Physics Journal Issue: 0 Vol. 138
• Country of Publication: United States
• Language: English

Related Subjects

36 MATERIALS SCIENCE
Machine Learning interatomic potential
Thermal property
Uranium Nitride
phonon