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arXiv2026

Machine-Learned Interatomic Potentials for Predicting Physicochemical Properties of Molten Metal-Salt Systems for Calcium Electrolysis

The design of efficient electrolysis devices for pure metal production requires accurate data on the properties of the melts used in the process. This work focuses on two key systems for calcium production: the molten Ca-Cu alloy and the CaCl$_2$-KCl electrolyte. High-temperature experiments are often expensive and time-consuming; however, we demonstrate that molecular dynamics (MD) simulations driven by machine-learned Moment Tensor Potentials (MTPs), trained on highly accurate density functional theory data, offer an effective and accurate alternative. Our MTP-driven MD simulations accurately reproduce the structural, thermodynamic, and transport properties across a range of temperatures and compositions relevant to electrolysis systems. We report calculated densities, radial distribution functions, heat capacities, thermal conductivities, ionic conductivities (for the electrolyte), viscosities, and diffusion coefficients, with deviations from experimental data within 20%. The strong agreement between calculations and experiments validates the proposed approach, establishing a robust framework for the computational exploration and optimization of liquid systems in metallurgical applications.

cond-mat.mtrl-sci↗

arXiv2021

Bayesian learning of thermodynamic integration and numerical convergence for accurate phase diagrams

Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free energy functions using data of various origin. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with existing works studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.

physics.comp-ph↗