Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer
Rajdeep Sarma, Yiwen Wang, David Hebert, Ethan Tran, Chenrui Shao, Sijie Fu, Ilkwon Cho, Olexandr Isayev, Isaac Garcia-Bosch
Highlight
Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion.
Abstract
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.
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Cite This Paper
@article{Sarma2025,
author = {Sarma, Rajdeep and Wang, Yiwen and Hebert, David and Tran, Ethan and Shao, Chenrui and Fu, Sijie and Cho, Ilkwon and Isayev, Olexandr and Garcia-Bosch, Isaac},
title = {Machine Learning-Accelerated Screening of Hydroquinone Analogs for Proton-Coupled Electron Transfer},
year = {2025},
doi = {10.26434/chemrxiv-2025-9k7p7},
url = {http://dx.doi.org/10.26434/chemrxiv-2025-9k7p7},
publisher = {American Chemical Society (ACS)},
keywords = {neural network, machine learning},
researchAreas = {ml-potentials, quantum-chemistry, reactions-reactivity},
highlight = {Proton-coupled electron transfer (PCET) mediated by hydroquinone and related molecules is key to natural and artificial energy conversion.}
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