A Data‐to‐Interface Framework for AI‐Guided Sodium‐Ion Battery Materials Discovery: From Descriptors to Full‐Cell Decisions
ABSTRACT For sodium‐ion batteries (SIBs), the central obstacle to artificial intelligence (AI)‐guided discovery is not simply data volume or algorithm choice, but the conditional nature of electrochemical labels. Capacity, voltage, initial Coulombic efficiency (ICE), rate capability, and retention depend on synthesis, disorder, electrolyte, interphase, and cell format rather than composition alone. Existing SIB and AI‐battery reviews are commonly organized by material class, value chain, or chemistry, but they rarely explain how a prediction becomes a defensible cell‐level decision. This review develops a data‐to‐interface framework linking literature, computation, processing, electrolyte, and full‐cell data to descriptors, models, mechanistic hypotheses, and validation under realistic constraints. Ordered cathodes are matched to voltage, phase stability, and Na‐ion migration; hard carbon is treated as a disordered‐anode case governed by precursor history, pore structure, storage mechanism, and electrolyte‐sensitive interphases. Electrolytes and electrode/electrolyte interfaces define whether electrode‐level predictions remain valid in practical cells. A structured assessment of representative studies shows that no study in the selected evidence set covers all seven metadata layers, with full‐cell matching and interphase reporting as the most common gaps. We propose a sodium‐specific minimum reporting profile for uncertainty‐aware, descriptor‐driven, and interface‐validated decision‐making.