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Machine learning-accelerated screening of hydroquinone analogs for proton-coupled electron transfer
Rajdeep Sarma1, Yiwen Wang1, David D Hebert1
1Department of Chemistry, Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA olexandr@cmu.edu igarciab@andrew.cmu.edu.
Chemical Science
|July 13, 2026
Summary
Researchers developed a new computational method to predict the bond dissociation free energy (BDFE) for thousands of molecules. This tool aids in designing new materials for energy conversion and storage.
Area of Science:
- Chemistry
- Computational Chemistry
- Materials Science
Background:
- Proton-coupled electron transfer (PCET) is vital for energy conversion processes.
- Hydroquinone derivatives are crucial for PCET, but their thermochemistry is complex to study.
- Existing methods for studying BDFE are experimentally challenging and computationally expensive.
Purpose of the Study:
- To develop a scalable computational approach for determining BDFEavg in hydroquinone-like molecules.
- To create a large-scale thermochemical database for PCET reagent design.
- To provide a predictive tool for optimizing molecular structures for energy applications.
Main Methods:
- Utilized the AIMNet2 neural network potential for high-throughput BDFEavg calculations.
- Calculated BDFEavg for approximately 200,000 hydroquinone-like compounds.
- Benchmarked AIMNet2 against Density Functional Theory (DFT) for accuracy.
Main Results:
- AIMNet2 showed good agreement with DFT calculations (R² ≈ 0.84).
- BDFEavg for diamines spans 50-80 kcal mol⁻¹, tunable via structural modifications.
- Electron-withdrawing groups increase BDFEavg, while reduced aromaticity decreases it.
Conclusions:
- AIMNet2 provides an efficient method for BDFEavg prediction.
- Systematic tuning of BDFEavg is achievable through molecular design.
- The developed database and tool will accelerate the design of PCET reagents for various applications.
