Deep Learning of Activation Energies
Name
Grambow2020_deep_learning_of_activation_energies.pdf
Size
527.11 KB
Format
Adobe PDF
Checksum (MD5)
8fcddfb00f53049a2d03d5420552a660
Author(s)
Grambow, Colin A.
Pattanaik, Lagnajit
Green, William H.
Date Issued
March 2020
Journal
Journal of Physical Chemistry Letters
Publisher
American Chemical Society (ACS)
Citation
Grambow, Colin A. et al. "Deep Learning of Activation Energies." Journal of Physical Chemistry Letters 11, 8 (March 2020): 2992-2997 © 2020 American Chemical Society
Version
Author's final manuscript
Abstract
Quantitative predictions of reaction properties, such as activation energy, have been limited due to a lack of available training data. Such predictions would be useful for computer-assisted reaction mechanism generation and organic synthesis planning. We develop a template-free deep learning model to predict the activation energy given reactant and product graphs and train the model on a new, diverse data set of gas-phase quantum chemistry reactions. We demonstrate that our model achieves accurate predictions and agrees with an intuitive understanding of chemical reactivity. With the continued generation of quantitative chemical reaction data and the development of methods that leverage such data, we expect many more methods for reactivity prediction to become available in the near future.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.jpclett.0c00500
