Molecular dynamics simulation of molten strontium chloride based on deep potential

Abstract

In this work, a scheme based on ab initio calculations and machine learning is applied to train deep learning interatomic potentials (DP) for molten SrCl2. With the trained DP, we investigate the local structures and properties of molten SrCl2 in the temperature range of 1200–1500 K. The results show that deep potential molecular dynamics (DPMD) simulations can accurately reproduce the radial distribution functions and angle distribution functions of ab initio molecular dynamics simulations. Meanwhile, the simulated partial structure factors of DPMD simulations are in good agreement with those obtained by the thermal neutron scattering technique. Additionally, the predicted density, self-diffusion coefficient, viscosity, and ionic conductivity are satisfactory compared with the experimental data. This study indicates that DP models can serve as an effective tool for the theoretical study of molten salts.