分子动力学
工作(物理)
镁
熔盐
密度泛函理论
扩散
材料科学
氯化物
势能
Atom(片上系统)
化学物理
计算化学
统计物理学
化学
计算机科学
热力学
物理
原子物理学
冶金
并行计算
作者
Wenshuo Liang,Guanzhong Lu,Jianguo Yu
标识
DOI:10.1002/adts.202000180
摘要
Abstract In previous work, molten magnesium chloride has been investigated using first‐principles molecular dynamics (FPMD) simulations based on density functional theory (DFT). However, such simulations are computationally intensive and therefore are restricted in terms of simulated size and time. In this work, a machine learning‐based deep potential (DP) is trained to accelerate the molecular dynamics simulation of molten magnesium chloride. The trained DP can accurately describe the energies and forces with the prediction errors in energy and force being 1.76 × 10 −3 eV/atom and 4.76 × 10 −2 eV Å −1 , respectively. Applying the deep potential molecular dynamics (DPMD) approach, simulations can be performed with more than 1000 atoms, which is infeasible for FPMD simulations. Additionally, the partial radial distribution functions, angle distribution functions, densities, and self‐diffusion coefficients predicted by DPMD simulations are also in reasonable agreement with FPMD or experimental results. This work shows that the DP enables higher efficiency and similar accuracy relative to DFT, exhibiting a bright application prospect in modeling molten salt systems.
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