Effect of composition and temperature on microstructure and thermophysical properties of LiCl-CaCl2 molten salt based on machine learning potentials

LiCl-CaCl2 molten salt is a promising candidate for storing and converting energy and capturing CO2. The machine learning potential molecular dynamics simulations have high efficiency and accuracy and can overcome the disadvantages of classical molecular dynamics simulations and first-principles molecular dynamics simulations. Therefore, this study applied machine learning potential molecular dynamics simulations to investigate the microstructure change and thermophysical properties of LiCl-CaCl2 molten salt in response to temperature and composition. Partial radial distribution function, coordination number distribution, and angular distribution function were used for microstructural analysis. It is found that the local structure of LiCl-CaCl2 molten salt hardly changed with temperature rise. However, it suffered from serious octahedral distortion with the increased CaCl2 content from 20% to 80%. Thermophysical properties of LiCl-CaCl2 molten salt were systematically analyzed, including density, self-diffusion coefficient, shear viscosity, electrical conductivity, thermal expansion coefficient, and specific heat capacity. The relationships between properties and temperature or CaCl2 mole fraction were fitted. In conclusion, this study can provide a novel and efficient approach to investigating molten salt in-depth and enrich the data of fundamental properties of LiCl-CaCl2 molten salt.