Material and thermal properties of MgCl2 molten salt by ab initio and machine-learning molecular-dynamics simulations

Abstract

We study the structural, thermophysical, and dynamical properties of molten MgCl2 over a wide temperature range using Ab Initio Molecular Dynamics (AIMD) and molecular dynamics simulations based on machine-learning interaction potentials (MLIPs). MLIPs can achieve near-AIMD accuracy at a fraction of the computational cost, enabling simulations of larger systems and longer timescales. This opens up the possibility of studying the system under out-of-equilibrium conditions, which in turn allows for the calculation of physical properties, such as the thermal conductivity or viscosity, that cannot be obtained reliably for the typical time and length scales accessible to AIMD. We follow two complementary approaches to develop and evaluate the MLIPs. First, we develop a Deep Potential (DP) from scratch using AIMD trajectories at different temperatures. Second, we also evaluate two out-of-the-box foundation models and a fine-tuned version of one of them. The fine-tuning is performed using configurations from the AIMD trajectories originally used to train the DP. We compare the predictions of the trained DP, the foundation models, and AIMD simulations with the available experimental data and provide a comprehensive calculation of the most important physical quantities of molten MgCl2. We compute densities, radial and angular distribution functions, thermal conductivity, heat capacity, viscosity, and diffusion coefficients over the 1000–1600 K temperature range. This range is relevant to important technological applications such as thermal energy storage, generation IV nuclear reactors, and concentrated solar power systems. Finally, we briefly review the accuracy and performance of the different MLIP versions used throughout the work.