Deep Learning Potential Assisted Prediction of Local Structure and Thermophysical Properties of the SrCl2–KCl–MgCl2 Melt

The local structure and thermophysical properties of SrCl2–KCl–MgCl2 melt were revealed by deep potential molecular dynamicsdriven by machine learning to facilitate the development of molten salt electrolytic Mg–Sr alloys. The short- and intermediate-range order of the SrCl2–KCl–MgCl2 melts was explored through radial distribution functions and structure factors, respectively, and their component and temperature dependence were discussed comprehensively. In the MgCl2-rich system, the intermediate-range order is more pronounced, and its evolution with temperature exhibits a non-Debye–Waller behavior. Mg–Cl is dominated by 4,5 coordination and Sr–Cl by 6,7 coordination, and their coordination geometries exhibit distorted octahedra and distorted pentagonal bipyramids, respectively. A database of thermophysical properties of SrCl2–KCl–MgCl2 melts, including density, self-diffusion coefficient, viscosity, and ionic conductivity, was thus developed, covering the temperature range from 873 to 1173 K.