Liquid-Vapor Phase Equilibrium in Molten Aluminum Chloride (AlCl3) Enabled by Machine Learning Interatomic Potentials

Abstract

Molten salts are promising candidates in numerous clean energy applications, where challenges in experimental methods limit knowledge of their safety-critical temperature-properties correlations. Herein, we developed and employed machine learning interatomic potentials (MLIP) to study AlCl3 molten salt across varied thermodynamic conditions. The MLIP accurately predicted the existence of Al2Cl6 dimers in this molten salt as informed by Raman spectra and neutron structure factor. The MLIP is validated using available experimental data for temperature correlations with viscosities, surface tension, as well as liquid and vapor densities evaluated from two-phase coexistence simulations. In doing so, we closely predicted the critical temperature and critical density compared to reported experimental values for AlCl3. The demonstrated approach for MLIP training in closely predicting phase equilibrium in this study can be useful towards screening nuclear reactors-relevant salt compositions, helping to mitigate safety concerns.