An artificial neural network potential for uranium metal at low pressures  

作　　者：郝茂生 管鹏飞 Maosheng Hao;Pengfei Guan(Beijing Computational Science Research Center,Beijing 100193,China)

机构地区：[1]Beijing Computational Science Research Center,Beijing 100193,China

出　　处：《Chinese Physics B》2023年第9期514-521,共8页中国物理B（英文版）

基　　金：Beijing Computational Science Research Center(CSRC);the National Natural Science Foundation of China(Grant Nos.52161160330 and U2230402)。

摘　　要：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.

关 键 词：machine learning potential uranium metal first-principles calculation 

分 类 号：O48[理学—固体物理] TP183[理学—物理]