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.

本数据集收录与Zhang等人2024年发表的《氧化物-电解质界面双电层的分子尺度解析》一文相关的数据。此项研究采用包含长程静电相互作用的从头算机器学习势能，对二氧化钛（TiO₂）-电解质界面处的双电层开展大规模原子级模拟。本数据集包含该论文中报道的完整密度泛函理论（DFT）训练数据集与深度势能模型。