Local structure and thermophysical property prediction for CaCl2-KCl molten salt with machine learning potentials

Y Xie and M Bu and GM Lu, MATERIALS TODAY COMMUNICATIONS, 41, 110243 (2024).

DOI: 10.1016/j.mtcomm.2024.110243

CaCl2-KCl molten salt, due to its excellent physical and chemical properties, is considered to have great application prospects in the fields of concentrated solar power (CSP) systems and preparation of metallic calcium. Structure information and thermophysical property have been simulated at various temperatures to explore the effect of the temperature on CaCl2-KCl molten salt. The reliability of machine learning potential molecular dynamics (MLPMD) simulation results is studied by comparison with the first-principles molecular dynamics (FPMD) simulations and experiments. Moreover, the MLPMD simulations lower the barrier to understanding and designing molten salts due to their high efficiency and accuracy.

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