Machine Learning Accelerates Molten Salt Simulations: Thermal Conductivity of MgCl2‐NaCl Eutectic

Abstract

AbstractThe marriage of ab initio calculations and machine learning (ML) methods exhibits bright application prospects in interatomic potential development. In this work, a concurrent learning scheme is implemented to automatically generate ML interatomic potential for the MgCl2‐NaCl eutectic. This scheme allows to train ML interatomic potential with training datasets approximately four times smaller than that of previous work, which significantly reduces the computational cost. The learned ML interatomic potential is used to accelerate the ab initio estimation of the properties of MgCl2‐NaCl eutectic, thermal conductivity in particular. With the learned models, simulations are conducted on multiple system sizes (1464–4392 atoms) and a wide temperature range (773–1073 K). The impact of the finite‐size effect on simulated thermal conductivity and derived size‐independent thermal conductivity is carefully investigated. The simulated thermal conductivities decrease with temperature and are in the range 0.469–0.538 W m−1 K−1 at 773–1073 K, which is in reasonable agreement with the literature data. Overall, the training scheme and the learned potential have produced reliable and satisfactory results, and promise to open up new avenues in the computational modeling of molten salts.