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This study introduces ProtoMI, a literature-driven framework that leverages sparse knowledge from reported electrolyte additives to prioritize candidates in vast, unlabeled chemical spaces for lithium-ion batteries. By employing graph contrastive learning, ProtoMI identifies seven interpretable prototypes from 126 known additives and adapts them through semi-supervised learning to screen nearly 180,000 candidates, achieving remarkable enrichment factors of 9.2-45.6 with minimal screening. Notably, the framework successfully identifies a promising candidate, TNDB, which enhances the performance of lithium-ion batteries at elevated temperatures by nearly 35% compared to baseline electrolytes.
Sparse literature knowledge can effectively guide the discovery of high-performance electrolyte additives, achieving up to 45.6 times enrichment in candidate screening.
Electrolyte additive discovery remains challenging because experimentally validated molecules are sparse, whereas accessible chemical spaces are vast and largely unlabeled. This challenge is amplified in lithium-ion batteries, where additive performance arises from coupled interfacial reactions rather than a single molecular property. Here, we develop a prototype-guided molecular intelligence, ProtoMI, a literature-driven framework that learns transferable structural priors from reported electrolyte additives and uses them to prioritize candidates in unlabeled chemical space. For boron-containing additives, ProtoMI combines 126 literature-reported molecules with 179,977 unlabeled candidates. Graph contrastive learning identifies seven chemically interpretable prototypes from the reported additives, and prototype guided semi-supervised contrastive learning adapts these prototypes to the candidate space under source-target distribution mismatch. In retrospective temporal validation, ProtoMI achieves enrichment factors of 9.2-45.6 while screening less than 2% of the candidate space. A subsequent translation step identifies four commercially accessible candidates. One representative candidate, 4,4,5,5-Tetramethyl-2-[10-(1naphthyl)anthracen-9-yl]-1,3,2-dioxaborolane (TNDB), improves high-temperature LiFePO4||graphite cycling at 55 {\deg}C by 34.93% relative to the baseline electrolyte. An arsenal of characterizations and operando optical fiber Fourier transform infrared spectroscopy suggest that TNDB forms B-containing, F/P/O-modified inorganic interphases, suppresses solvent decomposition and reduces Fe deposition on graphite. This case study shows how sparse literature knowledge can guide experimentally efficient molecular discovery in data-scarce battery-additive spaces.