Material and Thermal Properties of MgCl2 Molten Salt by Ab Initio and Machine-Learning Molecular-Dynamics Simulations

This research targets the materials and thermal properties of MgCl2 molten salt across a wide temperature range. It combines Ab Initio Molecular Dynamics with molecular dynamics simulations based on machine-learning interaction potentials to investigate structural, thermophysical, and dynamical behavior. The authors report that MLIPs can approach AIMD accuracy at substantially lower computational cost, supporting simulations of larger systems and longer timescales.