Scientific Data (Mar 2023)

Comprehensive exploration of graphically defined reaction spaces

  • Qiyuan Zhao,
  • Sai Mahit Vaddadi,
  • Michael Woulfe,
  • Lawal A. Ogunfowora,
  • Sanjay S. Garimella,
  • Olexandr Isayev,
  • Brett M. Savoie

DOI
https://doi.org/10.1038/s41597-023-02043-z
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 10

Abstract

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Abstract Existing reaction transition state (TS) databases are comparatively small and lack chemical diversity. Here, this data gap has been addressed using the concept of a graphically-defined model reaction to comprehensively characterize a reaction space associated with C, H, O, and N containing molecules with up to 10 heavy (non-hydrogen) atoms. The resulting dataset is composed of 176,992 organic reactions possessing at least one validated TS, activation energy, heat of reaction, reactant and product geometries, frequencies, and atom-mapping. For 33,032 reactions, more than one TS was discovered by conformational sampling, allowing conformational errors in TS prediction to be assessed. Data is supplied at the GFN2-xTB and B3LYP-D3/TZVP levels of theory. A subset of reactions were recalculated at the CCSD(T)-F12/cc-pVDZ-F12 and ωB97X-D2/def2-TZVP levels to establish relative errors. The resulting collection of reactions and properties are called the Reaction Graph Depth 1 (RGD1) dataset. RGD1 represents the largest and most chemically diverse TS dataset published to date and should find immediate use in developing novel machine learning models for predicting reaction properties.