AI-Guided Design and Discovery of Silicon-Based Anode Materials for Lithium-Ion Batteries

The development of high-capacity anodes remains a central challenge for advancinglithium-ion battery (LIB) technology. Silicon (Si) offers an exceptional theoreticalcapacity but suffers from severe volume expansion, structural degradation, andlimited Li-ion mobility after cycling. Here, we present an AI-driven framework forthe analysis, generation, and optimization of silicon-based anode materials withenhanced Li-ion transport and controlled volume variation during lithiation anddelithiation. Our approach integrates large-scale materials databases with state-of-the-art machine learning to: (i) analyze Li-ion migration pathways in knowncompounds, (ii) generate lithiation states from charged configurations, and (iii)design novel Si-based materials with enlarged Li-ion migration channels whiletracking their volume changes across lithiation levels. By combining graph-basedgenerative models with efficient property predictors, we accelerate the discoveryof candidate anodes through database-guided screening. The results highlightthe potential of AI to identify next-generation silicon-based anode materials withimproved stability and performance, providing valuable guidance for experimentaldevelopment.