机器学习型分子力场在金属材料相变与变形领域的研究进展

李志尚, 赵龙, 宗洪祥, 丁向东

Machine-Learning Force Fields for Metallic Materials: Phase Transformations and Deformations

LI Zhishang, ZHAO Long, ZONG Hongxiang, DING Xiangdong

图9 MLFFs在过渡金属元素Zr相变机制研究中的应用[104,106]

Fig.9 Application of MLFFs in phase transition of zirconium (a) predicted phase diagram of pure Zr as a function of pressure and temperature[106] (b) snapshots of the phase transformation processes in [0001] α shocked Zr single crystals with different piston velocities[104] (Insets show the neiborhood information of the atoms before and after the transformation. They correspond to the hcp and bcc structures, respectively) (c) typical microstructure evolution of β-Zr during cooling at pressure P = 0 GPa and P = 8.0 GPa using the present machine-learning (ML) potential[106]