Large Language Model‐Guided Design of Anti‐Swelling Hybrid Dual Network Membranes for Long‐Duration Alkaline Zinc Iron Flow Batteries

ABSTRACT Membranes applied in alkaline flow batteries suffer swelling from spontaneous polymer chain motion, which degrades ion selectivity and mechanical strength. Here, we employ a large language model (LLM)‐driven screening workflow to efficiently shortlist crosslinkers from a curated knowledge base, guiding the rational design of a hybrid dual network (H‐DN) membrane. The workflow identifies N,N,N′,N′‐tetramethylethylenediamine as the optimal candidate to build a robust, chemically crosslinked polysulfone network interpenetrated within a widely used sulfonated poly(ether ether ketone) (SPEEK) matrix. Mechanistically, the in situ formed quaternary ammonium motifs strongly ion‐pair with sulfonic acid groups, effectively suppressing chain motion without blocking ion transport channels. Consequently, the H‐DN membrane exhibits a 68% reduction in swelling and an ultra‐high wet‐state hardness of 216 MPa (a sixfold increase over SPEEK), while retaining high ionic conductivity (10.7 mS cm ⁻¹ ). Enabled by this mechanically robust and highly selective architecture, the alkaline zinc‐iron flow battery achieves an energy efficiency of 88.87% at a challenging areal capacity of 240 mAh cm ⁻² and operates stably for over 820 h without dendrite‐induced failure, which surpassed the commercial Nafion212 and SPEEK benchmarks by threefold and sevenfold, respectively. This work establishes a paradigm for LLM‐accelerated material discovery in addressing the stability‐selectivity dilemma of ion‐exchange membranes.