Lattice dynamics and elastic properties of alpha-U at high-temperature and high-pressure by machine learning potential simulations
Hao Wang, Xiao-Long Pan, Yu-Feng Wang, Xiang-Rong Chen, Yi-Xian Wang,, and Hua Y. Geng

TL;DR
This study uses machine learning force fields to accurately simulate the high-pressure and high-temperature properties of alpha-uranium, revealing elastic and lattice dynamics behaviors with implications for nuclear materials.
Contribution
The paper develops a machine learning force field for alpha-U that outperforms classical potentials and accurately predicts its properties under extreme conditions.
Findings
Weak phonon anharmonicity in alpha-U
Strong heating-induced softening of elastic constants
Increased anisotropy at high pressures and temperatures
Abstract
Studying the physical properties of materials under high pressure and temperature through experiments is difficult. Theoretical simulations can compensate for this deficiency. Currently, large-scale simulations using machine learning force fields are gaining popularity. As an important nuclear energy material, the evolution of the physical properties of uranium under extreme conditions is still unclear. Herein, we trained an accurate machine learning force field on alpha-U and predicted the lattice dynamics and elastic properties at high pressures and temperatures. The force field agrees well with the ab initio molecular dynamics (AIMD) and experimental results, and it exhibits higher accuracy than classical potentials. Based on the high-temperature lattice dynamics study, we first present the temperature-pressure range in which the Kohn anomalous behavior of the 4 optical…
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