Deep Learning Potential Assisted Prediction of Local Structure and Thermophysical Properties of the SrCl-KCl-MgCl Melt.

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Abstract

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.