Research and development of artificial intelligence in layered cathode materials for lithium-ion batteries

Lithium-ion batteries (LIBs), as core energy storage devices, have attracted significant attention due to their advantages such as high energy density. As the most promising cathode materials, layered cathode materials face challenges such as poor structural stability and thermal instability. Traditional trial-and-error approaches in layered cathode material research struggle to address these issues efficiently due to their reliance on complex variables and labor-intensive processes. In contrast, artificial intelligence (AI), with its capacity to process vast datasets and intricate variables, offers a transformative pathway to overcome these bottlenecks. This paper reviews recent advancements in AI applications for layered cathode materials in LIBs, covering material design and manufacturing, characterization and performance prediction, and battery management. Current limitations of AI, including data quality inconsistencies and model interpretability issues, are critically analyzed. Furthermore, future directions for AI-driven material discovery and multi-physics modeling are proposed, providing novel insights for the development of high-performance layered cathode materials.