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<p>We present an extended quantum chemical reaction database, called Reaction-QM, containing millions of reactions with fully optimized reactant, product, and transition state (TS) structures. The database comprises three subsets: The first set contains 2,304,303 organic reactions with up to ten heavy atoms and various elements (H, C, O, N, F, Si, P, S, and Cl) characterized at the GFN2-xTB level of theory, expanding the element scope and the size of the dataset. The second set consists of 199,890 reactions characterized at the B3LYP-D3/TZVP level of theory. All TS structures were validated through vibrational analysis and intrinsic reaction coordinate (IRC) calculations, ensuring the correct assignment of reactants and products. The final set contains full IRC trajectories at the DFT level for all 199,890 reactions, consisting of total 22,984,707 molecular configurations, offering a rich dataset for training machine learning interatomic potentials (MLIPs). Together, the databases can serve as a&#xa0;new data source for developing and benchmarking ML-based models for reaction modeling tasks, including TS structure prediction, activation barrier estimation, and the construction of reactive MLIPs.</p>

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A Comprehensive Dataset of Chemical Reactions Covering Second and Third Row Elements with Million-Scale Quantum Chemical Calculations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Kyunghoon Lee,
  • Sungwoo Kang,
  • Beomgyu Kang,
  • Won-Joon Son,
  • Seungmin Lee,
  • Dae Sin Kim,
  • Woo Youn Kim

摘要

We present an extended quantum chemical reaction database, called Reaction-QM, containing millions of reactions with fully optimized reactant, product, and transition state (TS) structures. The database comprises three subsets: The first set contains 2,304,303 organic reactions with up to ten heavy atoms and various elements (H, C, O, N, F, Si, P, S, and Cl) characterized at the GFN2-xTB level of theory, expanding the element scope and the size of the dataset. The second set consists of 199,890 reactions characterized at the B3LYP-D3/TZVP level of theory. All TS structures were validated through vibrational analysis and intrinsic reaction coordinate (IRC) calculations, ensuring the correct assignment of reactants and products. The final set contains full IRC trajectories at the DFT level for all 199,890 reactions, consisting of total 22,984,707 molecular configurations, offering a rich dataset for training machine learning interatomic potentials (MLIPs). Together, the databases can serve as a new data source for developing and benchmarking ML-based models for reaction modeling tasks, including TS structure prediction, activation barrier estimation, and the construction of reactive MLIPs.