Impacts of the Particle Geometric Heterogeneity on Battery Degradation

Decoding the hidden scientific principles in massive and complex data causes bottlenecks in experimental science. A typical objective is to analyze the causes of battery aging through the local geometric environment. Here, we deployed visually aware virtual probes to see the hidden chemical fingerprints in active particles, decoding invisible stochastic microscopic events into visualized ensemble quantization behavior. By developing a deep learning architecture with hierarchical interaction perception, we break the detection bottleneck triggered by crack scale variability and decipher the multiscale aging code of massive particle microregions. The significant geometric mismatch effects in chemical microregions drive the Li⁺ cross-phase transport traps. This behavior leads to elevated contact stress, which ignites a degradation cascade reaction in batteries. Further, the electrode was reprogrammed according to the electrode microarchitecture engineering, extending the pouch cell life by 25%. Our approach, utilizing computer vision to analyze the hidden scientific laws behind the phenomena, guides the design of failure immunity in other energy systems.