Abstract

The electrical double layer (EDL) at metal oxide-electrolyte interfaces critically affects fundamental processes in water splitting, batteries, and corrosion. However, limitations in the microscopic-level understanding of the EDL have been a major bottleneck in controlling these interfacial processes. We used ab initio-based machine learning potential simulations incorporating long-range electrostatics to investigate the structure and chemistry of the EDL at the prototypical anatase TiO2-electrolyte interface under various pH conditions. Our simulations show that the larger capacitance of the EDL under basic relative to acidic conditions originates primarily from the higher affinity of the cations for the oxide surface and gives rise to distinct charging mechanisms on negative and positive surfaces. These results are validated by the agreement between the computed EDL capacitance and experimental data.