Bridging the Gap from Material to Cell

Abstract Battery research has historically prioritized material discovery and electrochemical characterization, often overlooking the critical manufacturing parameters that determine commercial viability and cell-level performance. Variables such as electrode mass loading, porosity, slurry rheology, negative-to-positive (N/P) ratio, and formation protocols are rarely reported in academic literature, yet they govern the scalability of battery chemistries in gigafactory environments. This knowledge gap creates a persistent disconnect between laboratory-scale materials research and industrial cell production, leading to costly failures during scale-up and prolonged development cycles. Here we present CellIntel, a hybrid artificial intelligence-driven industrial knowledge platform that transforms unstructured scientific literature into structured, manufacturing-aware "cell recipes." By combining deterministic chemical entity extraction with generative reasoning and first-principles physical modeling, CellIntel infers the missing industrial parameters necessary to translate material research into manufacturable cell designs. The platform integrates techno-economic analysis and a live digital twin interface, enabling engineers to benchmark synthesized designs against commercial cells such as Tesla 4680 and Panasonic 2170. Across 1,500 documents spanning lithium iron phosphate (LFP) and nickel-manganese-cobalt (NMC) systems, CellIntel achieves energy density predictions with 6.3% mean absolute percentage error and successfully reconstructs complete manufacturing recipes from papers reporting only material properties. This work demonstrates how AI grounded in physical constraints can bridge the persistent gap between academic battery discovery and industrial cell production, potentially reducing development cycles from years to months.