A Composition-Transferable Machine Learning Potential for LiCl-KCl Molten Salts Validated by HEXRD

Abstract

Unraveling the liquid structure of multi-component molten salts is challenging due to the difficulty in conducting and interpreting high temperature diffraction experiments. Motivated by this challenge, we developed composition-transferable Gaussian Approximation Potentials (GAP) for molten LiCl-KCl. A DFT-SCAN accurate GAP is active learned from only ~1100 training configurations drawn from 10 unique mixture compositions enriched with metadynamics. The GAP-computed structures show strong agreement across HEXRD experiments, including for a eutectic not explicitly included in model training, thereby opening the possibility for composition discovery.