Ion-modulated structure, proton transfer, and capacitance in the Pt-water electric double layer

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 simulate 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. Our simulations quantify how ion-specific interactions, which are ignored in mean-field models, modulate capacitance and reactivity, providing a molecular basis for interpreting experiments and designing electrolytes.