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

Processing math: 100%

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11 MLFFs在碱金属K高压固态结构相变机制研究中的应用^[117,118]

Fig.11 Application of MLFFs in phase transition of the high-pressure solid alkali metal potassium
(a) forcefield simulated phase diagram of potassium^[118]
(b) top view and side view of the incommensurate host-guest (HG) structure K-III (a, b, c—lattice constants)
(c) correlation along the chains, showing long-range oscillatory order at 250 K (blue) and exponentially decaying short-ranged order at 750 K (red)^[118](

$〈σ_{z}〉$

—the intrachain correlation function, Δz(c_g)—the difference in the z-directional displacement between atoms, expressed as multiples of the guest lattice constant)
(d, e) typical microstructure evolution of bicrystalline KIII-fcc upon isothermal annealing at 16 and 21 GPa^[117] (Blue boxes outline the presence of disordered regions)