Accurate prediction of chemo-mechanical coupled full-field responses of battery electrodes using a deep learning scheme

Elucidating chemo-mechanical degradation mechanisms and optimizing the design of lithium-ion battery electrodes require a deep understanding of the coupled chemo-mechanical behavior within real heterogeneous microstructures. However, traditional microstructure-based finite element (FE) simulations are computationally prohibitive for resolving such complex three-dimensional (3D) multiphysics fields. In this study, an end-to-end deep learning scheme is developed to directly predict the 3D coupled chemo-mechanical full-field responses of electrode microstructures from geometric information, without requiring any additional prior physical information. The microstructural geometries are generated using a convolutional neural network (CNN)-based stochastic reconstruction algorithm trained on scanning electron microscope (SEM) images of real electrodes. The prediction model is built on a conditional generative adversarial network (cGAN) comprising a hybrid CNN–Transformer generator and a multi-scale discriminator. This architecture enables the simultaneous prediction of 3D stress and lithium concentration fields. The proposed framework demonstrates high predictive accuracy, achieving average relative errors of approximately 4.4% for stress fields and 1.5% for concentration fields. Experimental validation further supports the predicted heterogeneous lithium concentration distributions. By reducing inference time to only tens of seconds while maintaining accuracy comparable to high-fidelity simulations, this work provides an efficient alternative for rapid multiphysics analysis and microstructure design of battery materials.