High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions
Name
s41597-022-01529-6.pdf
Description
Published version
Size
2.12 MB
Format
Adobe PDF
Checksum (MD5)
ae45378d0b195987e387333cb7a3ba79
Author(s)
Spiekermann, Kevin
Pattanaik, Lagnajit
Green, William H
Date Issued
December 2022
Journal
Scientific Data
Publisher
Springer Science and Business Media LLC
Citation
Spiekermann, Kevin, Pattanaik, Lagnajit and Green, William H. 2022. "High accuracy barrier heights, enthalpies, and rate coefficients for chemical reactions." Scientific Data, 9 (1).
Version
Final published version
Abstract
AbstractQuantitative chemical reaction data, including activation energies and reaction rates, are crucial for developing detailed kinetic mechanisms and accurately predicting reaction outcomes. However, such data are often difficult to find, and high-quality datasets are especially rare. Here, we use CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP to obtain high-quality single point calculations for nearly 22,000 unique stable species and transition states. We report the results from these quantum chemistry calculations and extract the barrier heights and reaction enthalpies to create a kinetics dataset of nearly 12,000 gas-phase reactions. These reactions involve H, C, N, and O, contain up to seven heavy atoms, and have cleaned atom-mapped SMILES. Our higher-accuracy coupled-cluster barrier heights differ significantly (RMSE of ∼5 kcal mol−1) relative to those calculated at ωB97X-D3/def2-TZVP. We also report accurate transition state theory rate coefficients $${k}_{\infty }(T)$$
k ∞ ( T ) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.
k ∞ ( T ) between 300 K and 2000 K and the corresponding Arrhenius parameters for a subset of rigid reactions. We believe this data will accelerate development of automated and reliable methods for quantitative reaction prediction.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41597-022-01529-6
