Toward data-driven lithium metal batteries: Machine learning for materials design, mechanistic insight, and autonomous optimization

Lithium metal batteries (LMBs) are widely regarded as promising next-generation energy storage solutions because of their high theoretical energy density. Nonetheless, their practical application is hindered by complex interfacial phenomena, including unstable solid electrolyte interphases (SEI), dendritic lithium growth, and ongoing electrolyte consumption, which cause rapid performance decline and safety concerns. Traditional experimental and computational methods often struggle to capture the multiscale and dynamic nature of these processes. Recent progress in machine learning (ML) offers new opportunities to speed up LMB development through data-driven materials discovery, mechanistic insights, and performance prediction. This review summarizes recent advances in applying ML to LMBs from three key perspectives: materials design, interfacial processes, and lifetime prediction. It highlights how ML models facilitate electrolyte discovery, elucidate the mechanisms of SEI formation and dendrite growth, and predict battery degradation through early-cycle signals and morphological features. Finally, this review explores emerging opportunities for combining ML with high-throughput experimentation and autonomous laboratories to facilitate the closed-loop discovery of advanced battery technologies, as well as discusses the key challenges and future directions of ML-driven LMB research. This review is expected to provide not only a critical assessment of the state of the art but also a practical roadmap for integrating predictive analytics into next-generation battery research.