An artificial neural network potential for uranium metal at low pressures

Based on machine learning,the high-dimensional fitting of potential energy surfaces under the framework of first principles provides density-functional accuracy of atomic interaction potential for high-precision and large-scale simulation of alloy materials.In this paper,we obtained the high-dimensional neural network(NN)potential function of uranium metal by training a large amount of first-principles calculated data.The lattice constants of uranium metal with different crystal structures,the elastic constants,and the anisotropy of lattice expansion of alpha-uranium obtained based on this potential function are highly consistent with first-principles calculation or experimental data.In addition,the calculated formation energy of vacancies in alpha-and beta-uranium also matches the first-principles calculation.The calculated site of the most stable self-interstitial and its formation energy is in good agreement with the findings from density functional theory(DFT)calculations.These results show that our potential function can be used for further large-scale molecular dynamics simulation studies of uranium metal at low pressures,and provides the basis for further construction of potential model suitable for a wide range of pressures.