QuantumPioneer: Scalable Generation of Quantum Chemical Data for Solution-Phase Hydrogen Transfer Reactions

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Authors: Haoyang Wu, Jonathan W. Zheng, Hao‐Wei Pang, Yu-Chi Kao, Akshat Shirish Zalte, Charlles R. A. Abreu, Emad Al Al Ibrahim, Sayandeep Biswas, Chansup Byun, Jackson W. Burns, Chuangchuang Cao, Yunsie Chung
+15 more Xiaorui Dong, Anna C. Doner, Jeremy Kepner, Bonhyeok Koo, Shih‐Cheng Li, Angiras Menon, Lauren Milechin, Nathan Morgan, Kariana Moreno Sader, Kevin Spiekermann, F Vermeire, Bryan Wang, Markus Kraft, Connor W. Coley, William H. Green
Cited by: 1
References: 105

Abstract

High-fidelity quantum chemical (QM) data sets 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(ΔGsolv*) and enthalpies (ΔHsolv*) across 295 solvents. The workflow uses ωB97X-D/def2-SVP geometries, DLPNO-CCSD(T)-F12d/cc-pVTZ-F12 single-point energies, empirical thermochemical corrections, transition-state theory, and COSMO-RS BP-TZVPD-FINE solvation in a single high-throughput pipeline. Our benchmarks show reliable accuracy, with mean absolute errors (MAEs) compared to experimental and high-level QM reference 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 ΔGsolv* values. We demonstrate two predictive applications. First, we show that combining BDE and HAT-barrier models identifies experimentally observed oxidative degradation sites in drug-like molecules with a 91% top-5 hit rate and 82% site-level recall. Second, we show that a QM-parametrized Abraham model enables rapid solvation energy estimates at near-COSMO-RS accuracy within its training domain, reproducing computed ΔGsolv* and ΔHsolv* values with MAEs of 0.16 and 0.18 kcal/mol, respectively, though performance on experimental ΔGsolv*values for unseen solutes was worse, with an MAE of 1.32 kcal/mol. This work provides a scalable template for other reaction families, unifying equilibrium species, validated TS, thermochemistry, kinetics, and solvation into one workflow.

Fields of Study & Subjects

Free Radicals and Antioxidants Metal-Catalyzed Oxygenation Mechanisms Machine Learning in Materials Science

Publication details

Journal / source
Journal of the American Chemical Society
Publisher
American Chemical Society (ACS)
Volume
148
Issue
34
Pages
37018–37033
Publication date
19 August 2026
Language
EN