📝 SummarXiv

Abstract

The electric double layer (EDL) governs electrocatalysis, energy conversion, and storage, yet its atomic structure, capacitance, and reactivity remain elusive. Here we introduce a machine learning interatomic potential framework that incorporates long-range electrostatics, enabling nanosecond simulations of metal-electrolyte interfaces under applied electric bias with near-quantum-mechanical accuracy. At the benchmark Pt(111)/water and Pt(111)/aqueous KF electrolyte interfaces, we resolve the molecular structure of the EDL, reveal proton-transfer mechanisms underlying anodic water dissociation and the diffusion of ionic water species, and compute differential capacitance. We find that the nominally inert K+ and F- ions, while leaving interfacial water structure largely unchanged, screen bulk fields, slow proton transfer, and generate a prominent capacitance peak near the potential of zero charge. These results show that ion-specific interactions, which are ignored in mean-field models, are central to capacitance and reactivity, providing a molecular basis for interpreting experiments and designing electrolytes.