Deep Neural Network Enhanced Mesoscopic Thermodynamic Model for Unlocking the Electrode/Electrolyte Interface
Haolan Tao, Sijie Wang, Honglai Liu, Cheng Lian
Abstract
Structure and properties of the electrode/electrolyte interface significantly influence the electrochemical processes of energy storage and conversion, yet the challenge lies in accurate description of both molecular characteristics and external field effects. Here, we develop a mesoscopic thermodynamic model that calculates the thermodynamic properties of electrolytes based on chemical potential, and its efficiency is enhanced by a deep neural network. The deep neural network enhanced mesoscopic thermodynamic (DeepMT) model effectively bridges the gap between micro-level characteristics of ions and macro-level effects of external field, enabling precise presentation of ion density distributions over complex conditions. Our result indicates that the DeepMT model not only demonstrates a computational efficiency improvement of approximately four orders of magnitude over direct theoretical calculations, but also accurately predicts interface properties including ion adsorption, surface charge, and differential capacitance through the statistical analysis of density distributions.