A molecular-scale picture of the electrical double layer at TiO₂-electrolyte interfaces

The electrical double layer (EDL) is a structure that appears at solid-liquid interfaces, which governs the chemical reactivity and physical properties of the interface and plays a critical role in numerous electrochemical, electrocatalytic, geological, and biological processes. A molecular-scale understanding of the EDL is a significant step towards better controlling and optimizing these impactful processes. However, due to the inherent complexity of the interface, the molecular-scale simulation of the interface has been a long-standing challenge. In this work, we use the advanced deep potential long-range neural network method to simulate the interface between TiO₂ and electrolyte of varying pH values with ab initio accuracy. This gives us a comprehensive molecular-scale picture of the EDL, including the surface charging mechanism, ion distribution, and water orientation in the EDL, which confirms the limitations of the widely adopted classical mean-field description of the EDL provided by the Gouy-Chapman-Stern model. Moreover, our DeepWannier neural network describes the separation of electronic and ionic centers of charge, which enables us to calculate the electrostatic potential drop at the interface. We further calculate the capacitance, a property of experimental relevance. The computed capacitance agrees semi-quantitatively with experimental results, suggesting the reliability of our molecular-scale picture of the EDL.