近日，上海大学施思齐团队研究了基于维数分解的非平衡电双层建模深度学习方法。该项研究成果发表在2025年11月3日出版的《中国物理快报》杂志上。

电解质-电极界面电荷吸附形成的双电层（EDL）构成了控制电化学反应的微环境。然而，由于EDL厚度和电极形貌之间的尺度不匹配，求解二维（2D）非均匀泊松-能-普朗克（N-PNP）方程仍然难以计算。这种限制阻碍了对二维EDL中曲率驱动不稳定性等基本现象的理解。

研究组提出了一种嵌入全连接神经网络（FCNN）的维数分解策略来求解二维N-PNP方程，其中FCNN通过将静电边界简化为多个等效的一维表示来训练关键电化学参数。通过LiPF₆在锂金属半电池上还原的典型案例，意外地发现核核尺寸对枝晶形态和尖端动力学有重要影响。这项工作为将纳米尺度和宏观尺度模拟与其他1D EDL模型的2D情况的可扩展性联系起来铺平了道路。

附：英文原文

Title: Dimensionality-Decomposition Based Deep Learning Approach for Non-Equilibrium Electric Double Layer Modeling

Author: Weijie Li, Yajie Li, Maxim Avdeev, Siqi Shi

Issue&Volume: 2025-11-03

Abstract: 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.

DOI: 10.1088/0256-307X/42/12/120803

Source: https://cpl.iphy.ac.cn/article/doi/10.1088/0256-307X/42/12/120803