Enhanced exploration of LiF–NaF thermal conductivity through transferable equivariant graph neural networks

Although molten salt reactors and thermal storage systems are attracting increasing interest, our understanding of the physicochemical properties of molten salts is still incomplete. This is largely due to the difficulty of conducting experiments under extreme temperatures with strict control of impurities and corrosion. Ab initio calculations, machine-learned force fields, and classical molecular dynamics have helped to alleviate some of these issues. However, discrepancies between experimental and theoretical computations of the thermal conductivity of fluoride molten salts have become of increasing concern. In this paper, we present a modernized method for training a transferable equivariant graph neural network force fields to model a simple fluoride molten salt system, LiF–NaF, using minimal ab initio calculations. Using this transferable machine-learned force field, the thermal conductivity as well as various other functions of LiF–NaF were computed at various chemical temperatures and ratios in order to gain new insights into the limitations and behaviors of molten salts in relation to their thermal conductivity. Results show discrepancies between experimental and theoretical computations of the thermal conductivity as a function of temperature but good agreement between experimental and theoretical computations of the thermal conductivity as a function of ratio. Secondary results show compelling agreement of a machine-learned force field with first-principles computations and the ability to interpolate and extrapolate various chemical ratios.