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.

中文翻译：

---

基于机器学习的深电位模拟熔融氯化镁的分子动力学模拟

在以前的工作中，已经使用基于密度泛函理论（DFT）的第一原理分子动力学（FPMD）模拟研究了熔融氯化镁。但是，这样的模拟是计算密集型的，因此在模拟大小和时间方面受到限制。在这项工作中，对基于机器学习的深电位（DP）进行了培训，以加快熔融氯化镁的分子动力学模拟。受过训练的DP可以准确地描述能量和力，能量和力的预测误差为1.76×10 ^-3  eV /原子和4.76×10 ^-2  eVÅ ^-1， 分别。应用深层潜在分子动力学（DPMD）方法，可以用1000个以上的原子执行模拟，这对于FPMD模拟是不可行的。此外，DPMD模拟预测的部分径向分布函数，角度分布函数，密度和自扩散系数也与FPMD或实验结果合理吻合。这项工作表明，与DFT相比，DP可以实现更高的效率和相似的精度，在熔融盐系统建模中具有广阔的应用前景。