Neutral aqueous organic redox flow batteries (AORFBs) are a promising energy storage technology supporting new energy systems. Their overall energy efficiency is determined by the physicochemical properties of the organic electrolyte materials.

However, traditional empirical "trial-and-error" design methods often lack universal theoretical guidance, resulting in inconsistent material performance. In particular, a trade-off between solubility and stability is commonly encountered in high-concentration battery performance.

Viologen derivatives, known for their unique photoelectric response and structural tunability, have become ideal templates for neutral AORFB anolyte design. Current mainstream molecular design strategies focus on the functional modification of the bipyridine core, followed by terminal N-alkylation to construct a hydrophilic functional layer, thereby achieving a gradient increase in solubility.

However, the inherent alkaline degradation mechanism – where the pyridine C-N bond dissociates under nucleophilic attack in alkaline conditions (S_N2 reaction) – severely limits battery cycle life. The underlying issue is that the regulatory laws governing how dynamic solvation effects between viologen electrolytes and solvent water molecules impact structural stability have yet to be elucidated.

To address these issues, a research group led by Professor He Gang from the Frontier Institute of Science and Technology at Xi'an Jiaotong University (XJTU) has built upon their previous work to pioneer a machine learning strategy utilizing large language models (LLMs). Trained on over 1,300 AORFB studies, the model was used to predict chiral viologens featuring ortho-dihydroxy groups.

This bonding network forms a dynamic, pH-adaptive "solvation armor" that stabilizes the parent viologen structure. The solubility of R-/S-enantiomers was found to be 1.66 times higher than that of the RS-racemate. Molecular simulations and in situ spectroscopy confirmed that the dihydroxy groups protect the reactive C-N bonds through the solvation structure, thereby enhancing viologen stability within a pH range of 11.

Consequently, a symmetric battery (1 M) achieved a capacity retention rate of 99.42 percent over 3,652 cycles. A 1 M R-based AORFB maintained 100 percent capacity over 533 cycles, outperforming [(NPr)₂V]Cl₄ (94.92 percent) and sulfonic acid-modified viologen [(SPr)₂V] (65.49 percent). Furthermore, the large-scale synthesis of chiral viologens at the kg-Ah level and stack testing (2M) were validated, proving the immense industrial potential of chiral viologens.

This work establishes a new paradigm for machine learning-driven chiral electrolyte design, clarifies the "solvation armor" stabilization mechanism, and marks a critical leap toward industrial application through kilogram-scale synthesis and stack-level validation. It lays the theoretical and technical foundation for the transition of AORFBs from laboratory prototypes to large-scale applications.

The research results were published in Angewandte Chemie International Edition, a leading journal in the field of international chemistry, and the paper was selected as a very important paper.