Lattice dynamics and elastic properties of α-U at high-temperature and high-pressure by machine learning potential simulations

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 α-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 Σ4 optical mode exists. Phonon spectral function analysis showed that the phonon anharmonicity of α-U is very weak. We predict that the single-crystal elastic constants C44, C55, C66, polycrystalline modulus (E, G), and polycrystalline sound velocity (CL, CS) have strong heating-induced softening. All the elastic moduli exhibited compression-induced hardening behavior. The Poisson's ratio shows that it is difficult to compress α-U at high pressures and temperatures. Moreover, we observed that the material becomes substantially more anisotropic at high pressures and temperatures. The accurate predictions of α-U demonstrate the reliability of the method. This versatile method facilitates the study of other complex metallic materials.