Hydration MgCl₂-NaCl-KCl molten salt using a novel approach for training machine learning potential

The structural information and thermophysical properties of high-temperature magnesium electrolytes play critical roles in controlling the energy consumption of industrial production and the quality of magnesium alloys. Large-scale molecular dynamics simulations can achieve accuracy similar to that of DFT while overcoming the limitations of high-temperature experiments by integrating machine learning (ML) strategies. However, many AIMD simulation tasks, computationally expensive due to configuration and volume optimization, are required to construct an initial dataset for the current ML potential training scheme. To bypass this issue, we developed a novel scheme using Deep Potential to generate ML potential that can be applied to multiple temperatures, improving efficiency without compromising accuracy. A short-time AIMD simulation at a certain target temperature was involved, followed by an iterative task using the DP-GEN package to generate an initial dataset based on a varying upper bound for model force deviation. This dataset was then used in a new DP-GEN task to train ML potential. Our method has been applied for the first time to the molten MgCl2-NaCl-KCl system with trace amounts of water (hydration MNK). The trained potential effectively predicted interatomic energies and forces, with errors of 0.5517 meV/atom and 3.063 meV/& Aring;, respectively, denoting the trained potential is similar accuracy relative to DFT. DPMD simulation, the large-scale methodology, was performed to investigate the structural information and thermophysical properties of the hydration MNK system. Through analyzing the obtained structural information, Coulombic interactions are the dominant forces in the melt and a stronger affinity between Na+ and water molecules relative to other cations. H2O is unable to cross the first coordination layer of Cl-Mg. There are also differences in how the hydrogen and oxygen atoms of the water molecules interact with the cations in the melt. This work discusses the temperature-dependent evolution of several thermophysical properties, including density, self-diffusion coefficient, viscosity, and ionic conductivity. Our results show that density, viscosity, and ionic conductivity all exhibit negative relationships with temperature. In addition, the order of diffusion is as follows: D(Mg)2+More