Development of a Neural Network Potential for Modeling Molten LiCl/KCl Salts: Bridging Efficiency and Accuracy

Optimizing molten salts for molten salt reactors and
concentrated
solar power can be challenging due to limited experimental data. To
tackle this, we utilize neural network potentials (NNPs) for the atomistic
modeling of molten salts and use the widely popular LiCl/KCl salts
as prototype systems. Based on the results reported herein, the NNP
exhibits remarkable accuracy and is similar to density functional
theory calculations. The reliability of the NNP was due to a rigorous
approach to acquiring training data, which covered atomic configurations
at different temperatures and pressures for pure LiCl, pure KCl, and
LiCl–KCl (58.8% mol LiCl) systems. It was observed that the
NNP reasonably reproduced experimental physical properties of molten
LiCl/KCl salts across various compositions and temperatures and microstructures
that are similar to highly accurate first-principles molecular dynamics.
Furthermore, the NNP was employed to calculate diffusion coefficients of molten LiCl–KCl
salts, for which no current experimental data are available. From
this, we verify the NNP by reporting the well-known Chemla effect
in molten LiCl–KCl systems. We further employ the use of the
NNP to predict the phase diagram of the LiCl–KCl system by
using solid–liquid coexistence simulations. The robustness
and versatility of the NNP reported in this study demonstrate the
promising potential of the developed NNP in overcoming the long-standing
trade-offs between computational efficiency and accuracy in the MD
simulations of molten salts.