Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries

Abstract Conventional N ‐alkylated viologen electrolytes in neutral aqueous organic redox flow batteries (AORFBs) undergo irreversible nucleophilic S N 2 dealkylation degradation. Moreover, trial‐and‐error molecular design often fails to resolve the solubility–stability trade‐off in high‐concentration systems. Here we report a machine learning (ML) strategy using large language models (LLMs) trained on over 1300 AORFB studies to predict chiral viologens with ortho ‐dihydroxy motifs. This bonding network forms a dynamic, pH‐adaptive “solvation armor” that stabilizes the viologen structure. The R ‐/ S ‐ enantiomers (2.75/2.76 M) exhibit 1.66 times higher solubility versus RS ‐ racemate. Molecular simulations and in situ spectroscopy confirm that the dihydroxy groups protect reactive C─N bonds via a solvation structure (unrelated to chiral effect), enhancing stability to pH 11. The 1 M R 2+ / R +• redox couple sets a new record by achieving 99.42% capacity retention over 3652 cycles. The 1 M R ‐based AORFB shows 100% retention over 533 cycles, outperforming quaternary ammonium‐ ( [(NPr) 2 V]Cl 4 , 94.92%) and sulfonate‐modified viologen ( (SPr) 2 V) , 65.49%). Stable cycling across 0.1 ∼ 2.5 M demonstrates decoupling of degradation from concentration. This strategy is validated by 2.5 kg‐scale synthesis and Ah‐class stack testing (98.65% retention over 77 cycles), demonstrating industrial scalability. This work establishes a generalizable, ML‐enabled platform for electrolyte development, bridging molecular design and practical AORFB deployment.