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

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 using machine learning force fields are gaining popularity. As an important nuclear energy material, evolution uranium extreme conditions still unclear. Herein, we trained accurate field on α-U predicted lattice dynamics elastic at pressures temperatures. The agrees well with ab initio molecular (AIMD) experimental results it exhibits higher accuracy than classical potentials. Based high-temperature study, first present temperature-pressure range in which Kohn anomalous behavior Σ4 optical mode exists. Phonon spectral function analysis showed that phonon anharmonicity very weak. We predict single-crystal constants C44, C55, C66, polycrystalline modulus (E, G), sound velocity (CL, CS) have strong heating-induced softening. All moduli exhibited compression-induced hardening behavior. Poisson's ratio shows difficult to compress Moreover, observed material becomes substantially more anisotropic predictions demonstrate reliability method. This versatile method facilitates study other complex metallic materials.