CHAIN: unlocking informatics-aided design of Li metal anode from materials to applications

With the rapid development of consumer electronics, electric vehicles and grid-scale stationary energy storage, high-energy batteries are urgently demanded at present. Lithium metal batteries (LMBs) are considered to be one of the most promising high-energy density energy storage devices at present and have received much attention due to their ultra-high theoretical capacity, extremely low electrochemical potential and light mass. However, critical issues, such as uncontrollable lithium dendrite growth, dynamic changes in volume, interfacial impedance, severe chemical and electrochemical corrosion, remain huge challenges for Li metal anodes, which not only lead to low Columbic efficiency of LMBs, but also pose the risk of internal short circuit, causing serious side reactions and safety concerns that hinder LMBs from practical applications. Nevertheless, lithium metal is gradually poised for a revival after decades of oblivion, due to the development of research tools and nanotechnology-based solutions. In this review, various recent material designs for lithium metal anodes are reviewed based on previous theoretical understanding and analysis. Suppressing Li dendrites and ensuring the long life span of practical batteries through limited Li metal anodes design are still challenges. Multi-scale modeling methods are concerned, requiring the application of electrode material development. Hybrid multi-scale modeling application methods with machine learning technology are proposed based on the cloud computing platform. Computational material designs for Li metal anodes on model information are integrated with artificial intelligence. Finally, this review provides a novel framework for next-generation lithium metal anode design methods with a digital solution based on multi-scale data-driven models and machine learning techniques. Graphical Abstract: [Figure not available: see fulltext.]