Attention-Enhanced Damage Prediction in Electrode Materials From Simulation Video Sequences

Accurate prediction of damage in electrode materials is crucial to automating reliability management for lithium-ion batteries. Phase field-based simulations offer valuable insight into the chemomechanical processes and are nowadays often used as substitutes in lieu of physical microscopy data for developing fracture detection and tracking algorithms. Even for simulated phase-field videos, the data amount is still limited, making a machine learning task challenging and the traditional approaches less effective. This study proposes an attention-enhanced approach that applies physics-informed attention operations under deep learning-based image prediction models. Using synchronized stress and damage field data, the proposed method is able to focus effectively on critical fracture regions to improve spatio-temporal prediction accuracy despite the limited amount of training data. Experimental results demonstrate the robustness and effectiveness of the proposed attention-enhanced approach, which could offer a scalable solution for real-time fracture analysis and for vision systems for in-line metrology in the long run.