A data-driven elucidation of the challenges in designing stable quinone derivatives for aqueous organic redox flow batteries: correlations between thermodynamics of chemical degradation and energy capacity metrics

Aqueous organic redox flow batteries (AORFBs) offer significant potential for grid-scale storage of renewable energy. However, their commercial viability is often limited by the chemical instability of the organic electrolytes. Quantifying and assessing the effects of the various degradation mechanisms from a molecular design perspective is very challenging both experimentally and computationally. Here, we propose the use of simple thermodynamic descriptors for seven degradation mechanisms for a diverse virtual library of ca. 2000 monofunctionalized quinones built from seven core structures. In the process, we developed a cheminformatics-based workflow that can reliably and automatically generate reactants and mechanism-specific degradation products. Using DFT-calculated reaction Gibbs free energies as thermodynamic descriptors and a calibrated model for redox-potential prediction, we systematically analyse seven degradation mechanisms across this library. We find clear relationships between redox potential and degradation thermodynamics for six of the seven mechanisms, with opposite trend directions for oxidized and reduced forms, revealing stability – potential trade-offs that constrain molecular design. The analysis further shows how functional groups modulate degradation thermodynamics, helps rationalize the relative stability of anthraquinone-based outliers. Finally, redox active molecules from recent experimental studies are evaluated within the proposed thermodynamics-based framework, and we comment on the implications of electrochemical reversibility of some critical degradation mechanisms. Overall, this work provides a physically motivated framework for the multi-objective screening of quinone-based AORFB electrolytes and helps clarify the design criteria needed to identify favourable molecular outliers.