QuantumPioneer: Scalable generation of quantum chemical data for solution-phase hydrogen transfer reactions
Abstract
High-fidelity quantum chemical (QM) datasets that jointly resolve reaction thermochemistry, kinetics, and solvation at scale remain scarce, especially for radical chemistry. We introduce QuantumPioneer, an open-access reaction-centered QM database and workflow for small organic molecules, focused on peroxyl-mediated hydrogen atom transfer (HAT) and the corresponding homolytic bond dissociation reactions. QuantumPioneer contains 348,258 species (2–21 heavy atoms), 167,237 validated HAT transition states (TS) with corresponding reaction energies and homolytic bond dissociation energies (BDEs), and over 100 million COSMO-RS solvation free energies (∆Go_solv) and enthalpies (∆Ho_solv) across 295 solvents. The workflow uses ωB97X-D/def2-SVP geometries, DLPNO-CCSD(T)-F12d/def2-TZVP single-point energies, empirical thermochemical corrections, transition-state theory, and COSMORS BP-TZVPD-FINE solvation in a single high-throughput pipeline. Our benchmarks show reliable accuracy, with mean absolute errors compared to experimental data of 0.82 kcal/mol for gas-phase enthalpies of formation, 1.60 kcal/mol for C–H BDEs, 1.45 kcal/mol for HAT barriers, and 0.57 kcal/mol for ∆Go_solv values. We demonstrate two predictive applications. First, we show that a combined BDE and HAT-barrier model identifies experimentally observed oxidative degradation sites in drug-like molecules with a 91% top-5 hit rate and 80% site-level recall. Second, a QM-parameterized Abraham model enables rapid solvation energy estimates at near-COSMO-RS accuracy within its training domain, reproducing ∆Go_solv and ∆Ho_solv values with MAEs of 0.16 kcal/mol and 0.18 kcal/mol, respectively, though performance on experimental ΔGo_solv values for unseen solutes was worse, with MAEs of 1.32 kcal/mol, providing a scalable template for other reaction families, unifying equilibrium species, validated TS, thermochemistry, kinetics, and solvation into one workflow.
Fields of Study & Subjects
Metal-Catalyzed Oxygenation Mechanisms
Machine Learning in Materials Science
Free Radicals and Antioxidants
Publication details
- Journal / source
- ChemRxiv
- Publisher
- American Chemical Society (ACS)
- Publication date
- 12 July 2026
- Document type
- Preprint