Machine Learning Helps Data Mining to Build Descriptor Databases for Lithium‐Ion Batteries

ABSTRACT The rapid development of computer science has made machine learning (ML)‐driven design a research hotspot in high‐performance lithium‐ion batteries. Descriptors play a critical role in ML processes, as accurate descriptors significantly improve prediction accuracy (achieving over 92% validation accuracy in density functional theory [DFT]‐calibrated models). Although open‐source databases offer rich material data, their operational complexity hinders effective utilization. This review highlights ML algorithms that streamline data extraction from these repositories, slashing experimental iterations by 75%–80%. Further analyze future challenges in descriptor acquisition for lithium‐ion batteries. This review is to provide insights into dataset construction and ML‐compatible descriptor generation, accelerating electrode material discovery from conventional 5–7 years to < 18 months in recent cases.