  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
${\Sigma}$4 optical mode exists. Phonon spectral function analysis showed that
the phonon anharmonicity of alpha-U is very weak. We predict that the
single-crystal elastic constants C44, C55, C66, polycrystalline modulus (E,G),
and polycrystalline sound velocity ($C_L$,$C_S$) 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 alpha-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 alpha-U demonstrate the reliability of the method. This
versatile method facilitates the study of other complex metallic materials.
