In Li‐ion batteries, the mechanical degradation initiated by micro cracks is one of the
bottlenecks for enhancing the performance. Quantifying the crack formation and evolution in
complex composite electrodes can provide important insights into electrochemical behaviors
under prolonged and/or aggressive cycling. However, observation and interpretation of the
complicated crack patterns in battery electrodes through imaging experiments are often time‐
consuming, labor intensive, and subjective. Herein, a deep learning‐based approach is …

In article number 2203070, Jizhou Li, Kai Zhang, Qingxi Yuan, Yijin Liu, and co-workers
demonstrate a deep learning-based approach to effectively extract the crack patterns from
nanoscale hard X-ray holo-tomography data of a piece of cathode from an electrochemically
abused 18650-type commercial battery. It is found that the crack distributions are associated
with the cathode packing densities. A potentially viable architectural design for suppressing
the structural degradation is proposed.