Machine learning-accelerated screening of hydroquinone analogs for proton-coupled electron transfer

Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion. The reactivity of these molecules depends on their bond dissociation free energy (BDFE), but studying the relationship between structure and thermochemistry across chemical space has been limited by computational expense. Here, we present the first use of the AIMNet2 neural network potential to calculate average BDFE (BDFEavg) values for the 2H+/2e− dehydrogenation of about 200,000 hydroquinone-like compounds, including vicinal diamines, diols, and dithiols. Benchmarking against DFT calculations for 168 substituted ortho-phenylenediamines (opda) shows good agreement (R² > 0.9). Our analysis finds that BDFEavg ranges from 50 to 80 kcal/mol and can be systematically tuned by modifying the backbone and N-substitution: electron-withdrawing groups raise BDFEavg by up to 15 kcal/mol, while lower aromaticity in furan and thiophene backbones decreases BDFEavg by approximately 10 kcal/mol compared to phenyl systems. We developed an additive "offset model" that allows separate investigation of backbone and sidechain effects. Validation through cyclic voltammetry and reactivity studies with quinone oxidants for selected compounds supports the computational results. This extensive thermochemical database and web-based prediction tool offer valuable resources for designing PCET reagents for catalysis, energy storage, and biomedical uses.