An electronic and molecular-level understanding of the metal–electrolyte interface is critical for advancing technologies in energy, catalysis, and nanofluidics, yet accurately modeling the electrical double layer (EDL) remains a significant challenge. To overcome the trade-off between the inaccuracies of classical potentials and the prohibitive cost of ab initio molecular dynamics, this work develops a deep learning potential from SCAN density functional theory simulations to enable large-scale, nanosecond time scale molecular dynamics of the Au(111)-aqueous KCl interface with quantum accuracy. We demonstrate that chloride ions specifically chemisorb to the surface, a process driven not by simple electrostatics but by orbital hybridization and partial charge transfer from the ion to the metal surface. Despite the strong chemisorption, we found that the adsorbed chloride ions are mobile, exhibiting a low lateral diffusion barrier. This work provides a detailed atomistic picture of the electrical double layer with first-principles accuracy, paving the way for more realistic and predictive simulations of complex electrochemical systems.
Saxena et al. (Sat,) studied this question.