Guiding the Design of Heterogeneous Electrode Microstructures for Li‐Ion Batteries: Microscopic Imaging, Predictive Modeling, and Machine Learning

Electrochemical and mechanical properties of lithium-ion battery materials are heavily dependent on their 3D microstructure characteristics. A quantitative understanding the role played by stochastic microstructures is critical for prediction material guiding synthesis processes. Furthermore, tailoring morphology also a viable way achieving optimal electrochemical performances cells. To facilitate establishment microstructure-resolved modeling design methods, review covering spatially temporally resolved imaging phenomena, statistical characterization reconstruction, property prediction, machine learning presented here. The perspectives unresolved challenges opportunities in applying experimental data, modeling, to improve identify paths toward enhanced performance cells presented.