Artificial intelligence–driven decoupling structure–activity relationship for lithium‐ion batteries

Due to the rapid development of electric vehicles and energy storage systems, a more severe demand is proposed for the energy density and lifespan of lithium‐ion batteries. The performances depend on the structure of the materials, such as the cathode, anode, and electrolyte. However, the traditional material research pipeline requires massive preliminary surveys, high experimental investment, and lengthy time span, which makes the battery iteration in years. The application of artificial intelligence (AI) and machine learning (ML) subverts the traditional research pattern. AI‐driven battery research has broken through the bottleneck of traditional battery investigation. ML can decouple high‐dimensional data to excavate the clues for the structure–activity relationship of batteries. Herein, the essential concepts, models, and techniques for engaging ML, as well as the mechanisms and active components of lithium‐ion batteries, are summarized. The basic theoretical research for deciphering the structure–activity relationship of lithium‐ion batteries is further collected to mentor electrode and electrolyte design. The ML‐driven mining pipeline for handling complex data and exploring relationships for different materials is revealed. The challenges confronted in AI‐driven battery designation are discussed, and the ML‐driven research paradigm progress in battery research is prospected.