Estimating Potential-Dependent Physicochemical Properties at Metal–Electrolyte Interfaces Using Machine Learning Interatomic Potentials

Metal–electrolyte interfaces play a central role in electrocatalysis, energy storage, and environmental remediation. Understanding the structure and properties of these interfaces is therefore essential to designing efficient electrochemical systems. Density functional theory (DFT)-based molecular dynamics (MD) can accurately capture interfacial structure but is restricted to short time scales and small system sizes. To overcome these limitations, we develop machine learning interatomic potentials (MLIPs) using the MACE architecture within an active learning workflow to model aqueous NaCl electrolytes in contact with Au, Cu, and Rh(111) electrodes. The resulting committee of MLIPs achieves DFT-level accuracy across 21 metal–electrolyte systems spanning a wide range of surface charge densities. MACE–MD simulations reproduce key interfacial properties obtained from ab initio MD, including water density profiles, water orientation, and chemisorbed water coverage. Our simulations reveal a universal trend across all metals: the total coverage of water and ions decreases with increasing surface charge density or potential, reaches a minimum at or slightly below the pzc, and increases thereafter. Two distinct capacitance regimes emerge for all electrodes, corresponding to potentials below and above this point. Ion-specific effects strongly influence interfacial structure. Cl– exhibits significantly stronger interactions with all metal surfaces than Na+, undergoing partial desolvation of up to 3.5 water molecules upon approaching the interface, compared to only 0.5 for Na+. These behaviors manifest in the vibrational density of states, where Cu and Rh show broad O–H stretching features at negative charge densities associated with Na+ accumulation and strengthened hydrogen bonding. Overall, this work demonstrates that MLIPs based on the MACE architecture enable long-time scale, first-principles-accurate simulations of metal–electrolyte interfaces and provide detailed mechanistic insight into their potential-dependent physicochemical properties.