Dimensionality-Decomposition Based Deep Learning Approach for Non-equilibrium Electric Double Layer Modeling

The electric double layer (EDL), formed by charge adsorption at the electrolyte-electrode interface, constitutes the microenvironment governing electrochemical reactions. However, due to scale mismatch between the EDL thickness and electrode topography, solving the two-dimensional (2D) nonhomogeneous Poisson-Nernst-Planck (N-PNP) equations remains computationally intractable. This limitation hinders understanding of fundamental phenomena such as curvature-driven instabilities in 2D EDL. Here, we propose a dimensionality-decomposition strategy embedding a fully connected neural network (FCNN) to solve 2D N-PNP equations, in which the FCNN is trained on key electrochemical parameters by reducing the electrostatic boundary into multiple equivalent 1D representations. Through a representative case of LiPF6 reduction on lithium metal half-cell, nucleus size is unexpectedly found to have an important influence on dendrite morphology and tip kinetics. This work paves the way for bridging nanoscale and macroscale simulations with expandability to 2D situations of other 1D EDL models.