Lithium iron phosphate (LiFePO4, LFP) has regained prominence as a cathode for lithium ion batteries thanks to its intrinsic safety, thermal stability, long cycle life, and cost advantages. We present an agentic knowledge-graph pipeline that converts titles/abstracts into directed, signed agent → property relations. Using a Scopus corpus of the 9500 most-cited LFP journal articles (2000-present), we benchmark three matched modes: A, rules with a closed vocabulary; B, LLM-only with an open vocabulary; and mixed LLM with a hybrid vocabulary. A yields a compact, high-precision core; B expands recall but increases label dispersion; C preserves much of B's breadth while maintaining schema alignment via canonicalization and role gating. Robustness tests with eight bootstrap passes show rapid convergence: requiring recurrence across ∼6 passes plus a modest publication-support threshold yields a compact, high-confidence backbone. The resulting network is predominantly positive and centers on transport and interfacial outcomes, with a small number of mixed and negative ties indicating condition dependence. Beyond LFP, the workflow can be adapted to other battery chemistries with modest retuning of vocabularies and projection rules alongside routine validation on held-out annotations, enabling a stability-aware, literature-scale synthesis of direction-of-effect relations.
Choi et al. (2026) studied this question.