Prediction on local structure and properties of LiCl-KCl-AlCl3 ternary molten salt with deep learning potential

Abstract

LiCl-KCl-AlCl3 molten salt is the main electrolyte for Al-Li alloys electrolysis. It’s beneficial for the electrolyte optimization to have a deeply understand to the local structure and properties of LiCl-KCl-AlCl3 molten salt. Deep potential molecular dynamics simulations were applied in this study, which can balance efficiency and accuracy of the calculations. A deep potential model for LiCl-KCl-AlCl3 molten salt was trained to predict the local structure and properties of LiCl-KCl-AlCl3 molten salt. The structure and properties with temperature were analyzed. The structure information includes the partial radial distribution functions and coordination number distribution; the properties consist of density, self-diffusion coefficient, viscosity, ionic conductivity, thermal expansion coefficient and heat capacity. The structure analysis shows that due to the strong and stable covalent bond between Al-Cl ion pairs, the increase of temperature has no obvious effect on the coordination structure of Al3+ and Cl–. For K-Cl and Li-Cl ion pairs, as the temperature increases, the connection between the anion and cation clusters weakens, and the overall structure of the molten salt tends to be loose, so the density and shear viscosity of the LiCl-KCl-AlCl3 molten salt decrease with the increase of temperature, the self-diffusion coefficient and ionic conductivity increase with increasing temperature, and the thermal expansion coefficient increases with increasing temperature. The variation of the self-diffusion coefficient, shear viscosity and ionic conductivity with temperature follows the Arrhenius rule. Moreover, this work provides an effective and accurate way to explore other molten salts.