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4 不同MLFFs精度与效率的互制关系[83]

Fig.4 Trade-offs between accuracy and cost among different types of MLFFs. All potentials except EAM4 were refitted to the same tungsten data set. Computational costs were benchmarked with a 128-atom bcc-tungsten supercell[83] (EAM—embedded atom method; GAP—Gaussian approximation potential; SNAP, qSNAP—spectral neighbor analysis potential (SNAP) and its quadratic variant, respectively; MTP—moment tensor potential; DFT—density functional theory; LJ—Lennard-Jones function; RMSE—root-mean-squared error; RMSF—root-mean-square-fluctuation; σE—the range of the ground truth value of RMSE; σF—the range of the ground truth value of RMSF)