Data from "Molecular-scale insights into the electrical double layer at oxide-electrolyte interfaces”

This dataset contains data relates to the publication "Molecular-scale insights into the electrical double layer at oxide-electrolyte interfaces", by Zhang et al., 2024. In this work, we employ ab initio-based machine learning potentials including long-range electrostatic interactions to perform large-scale atomistic simulations of the electrical double layer at the TiO2-electrolyte interface. This dataset contains the complete DFT training datasets and the deep potential models reported in the publication.