Accelerating the Battery Revolution: AI‐Driven Multiscale Innovation From Material Discovery to Smart Manufacturing

Abstract Just as artificial intelligence (AI) demonstrates remarkable potential in accelerating material discovery, its transformative impact is now extending to address critical challenges in lithium‐ion batteries (LIBs) development, particularly in overcoming persistent hurdles like protracted innovation cycles and prohibitive costs. This review systematically examines how AI and machine learning (ML) provide innovative solutions across the LIBs value chain‐from accelerating material innovation and optimizing synthesis processes to enhancing manufacturing precision. Beginning with fundamental concepts of AI/ML in energy storage, the analysis progresses to comprehensive applications in LIBs technology. Meanwhile, AI‐driven approaches enhance discovery efficiency for electrode materials, while improving property prediction accuracy and cost‐effectiveness. For materials synthesis, AI enables parameter optimization across scales and facilitates transition from lab‐scale breakthroughs to industrial production. Within electrode manufacturing, AI applications evolve from localized process optimization toward integrated full‐chain modeling and closed‐loop control systems. In cell manufacturing, AI demonstrates particular promise in three key areas, while showing limitations in whole‐process reliability forecasting. The review ultimately identifies critical barriers to AI adoption in battery manufacturing, including data fragmentation across production stages, insufficient high‐quality datasets, lack of standardized data protocols, and fundamental constraints in model interpretability and cross‐scenario adaptability.