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

TL;DR

This study uses machine learning interatomic potentials to accurately model phase equilibrium and thermodynamic properties of molten AlCl3, aiding safety assessments for energy applications.

Contribution

The paper introduces a machine learning approach to predict phase behavior and thermodynamic properties of molten AlCl3 with high accuracy, validated against experimental data.

Findings

MLIP accurately predicts Al2Cl6 dimers in molten salt.

Close prediction of critical temperature and density.

Validated against experimental viscosity, surface tension, and densities.

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.