In addition to electrode design, characterization is a crucial tool for understanding physical mechanisms and evolution to advance the design of batteries. The performance of batteries depends on the transport of lithium ions and the kinetic process of electrodes, which are influenced by microstructure parameters such as porosity and tortuosity. These parameters play a vital role in predicting the fastcharging performance. Visualizing electrode microstructure during the service process is imperative for optimizing electrode structure and diagnosing potential safety issues. Fortunately, significant progress has been made in the field of picture processing and complex structure analysis, providing a robust framework for electrode microstructure visualization and analysis. (94-97) Yang et al. studied the evolution of electrode microstructure in a battery by using a modified U-Net convolutional neural network for high-precision segmentation. DL is used to obtain the porosity and thickness of the negative and positive electrodes at different states of charge, and the relationship between the evolution of porosity and thickness during charging. The method could be extended to measure additional microstructural parameters using broad ion beam-SEM/FIB-SEM for 3D models in the future, providing an approach to exploring microstructure evolution and aiding in electrode structure optimization. Additionally, Gayon-Lombardo presented a method for creating synthetic threedimensional microstructures consisting of several material phases using DC-GANs. This approach enables the model to represent the statistical and morphological characteristics of actual microstructures. The study used two open-source microstructural datasets, and various microstructural properties were calculated for the real data and compared to the synthetic structures created by the trained generator. The results showed excellent agreement, although the synthetic structures had a smaller variance than the training data. (99) Moreover, Petrich et al. developed a classifier that distinguishes between three causes for particle separation using the shape of the gap between particles. (100) Simulated anode material was used to generate correctly labeled sample data for developing the classifier. The classifier was then tested using hand-labeled data from a real electrode, achieving an overall accuracy of 73%.