Improving Aqueous Metal Salt Interactions Using Machine-Learned Interatomic Potentials

Accurate modeling of aqueous metal salt solutions is essential for understanding processes relevant to environmental safety, energy storage, and separation technologies. Trace metals such as As³⁺ at low concentrations pose significant health and environmental risks. They are challenging to simulate due to limitations in both classical force fields (CFFs), which lack accuracy, and <i>ab initio</i> methods, which are restricted to short trajectories. In this study, we develop machine-learned interatomic potentials (MLIPs) to model aqueous AsCl₃ and MgCl₂ using the NequIP/Allegro equivariant graph neural network architecture trained on <i>ab initio</i> molecular dynamics (AIMD) and density functional theory data. Our MLIP models accurately reproduce <i>ab initio</i> energies and forces while capturing solvation structure, ion diffusion, and hydration dynamics more effectively than CFFs (AMBER and UFF models). Our MLIPs achieve energy MAEs < 1 meV/atom and force RMSEs < 40 meV/Å, while providing an O(10⁴) speedup over AIMD. These MLIPs offer a reliable and efficient alternative for modeling trace metal speciation and transport, with implications for improved separation and environmental processes.