The solid-electrolyte interphase (SEI) is essential for the performance and longevity of lithium-ion batteries, yet its formation remains one of the least understood processes in battery science. Formed spontaneously during early charging, the SEI enables lithium-ion transport while preventing further electrolyte degradation. A predictive, molecular-level understanding of SEI formation is urgently needed to design more durable and efficient batteries. In this work, we leverage quantum chemistry calculations, stochastic simulations, and chemical reaction networks (CRNs) to systematically explore the formation of SEI without prior knowledge of the reaction mechanisms or end products. Using a recently developed methodology, we constructed the largest CRN to date, comprising over 10,000 species and 209 million reactions. To ensure chemical plausibility, we applied rigorous filtering criteria based on electrochemical feasibility and stability. Large-scale, in-parallel kinetic Monte Carlo simulations were then performed with fixed reaction rates using the discrete-time Gillespie algorithm. This approach enabled the recovery of experimentally identified SEI product (e.g., diethyl carbonate, lithium butylene dicarbonate) as well as the prediction of previously unreported species (e.g., PO4C4H10Li, C6H10O3, C5H8O4), despite reaction kinetics being entirely omitted in network exploration. First-principles mechanistic analyses further identified key elementary pathways contributing to the formation of these species. Importantly, these new molecules have been validated by laser desorption ionization (LDI) coupled to ultrahigh-resolution Fourier Transform Ion Cyclotron Resonance Mass Spectrometry. Our findings highlight the power of data-driven CRNs in uncovering hidden reaction mechanisms within electrochemical systems. This framework not only deepens our fundamental understanding of battery interface chemistry but also provides a scalable platform for accelerating the design of next-generation energy storage devices with improved efficiency, lifetime, and cost-effectiveness.
