MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Generating transition states of isomerization reactions with deep learning

Author(s)
Pattanaik, Lagnajit; Ingraham, John; Grambow, Colin A.; Green Jr, William H
Thumbnail
DownloadPublished version (3.277Mb)
Publisher with Creative Commons License

Publisher with Creative Commons License

Creative Commons Attribution

Terms of use
Creative Commons Attribution 3.0 unported license https://creativecommons.org/licenses/by/3.0/
Metadata
Show full item record
Abstract
Lack of quality data and difficulty generating these data hinder quantitative understanding of reaction kinetics. Specifically, conventional methods to generate transition state structures are deficient in speed, accuracy, or scope. We describe a novel method to generate three-dimensional transition state structures for isomerization reactions using reactant and product geometries. Our approach relies on a graph neural network to predict the transition state distance matrix and a least squares optimization to reconstruct the coordinates based on which entries of the distance matrix the model perceives to be important. We feed the structures generated by our algorithm through a rigorous quantum mechanics workflow to ensure the predicted transition state corresponds to the ground truth reactant and product. In both generating viable geometries and predicting accurate transition states, our method achieves excellent results. We envision workflows like this, which combine neural networks and quantum chemistry calculations, will become the preferred methods for computing chemical reactions.
Date issued
2020-10
URI
https://hdl.handle.net/1721.1/128542
Department
Massachusetts Institute of Technology. Department of Chemical Engineering; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Physical Chemistry Chemical Physics
Publisher
Royal Society of Chemistry (RSC)
Citation
Pattanaik, Lagnajit et al. "Generating transition states of isomerization reactions with deep learning." Physical Chemistry Chemical Physics 22, 41 (October 2020): 23618-23626 © 2020 Owner Societies
Version: Final published version
ISSN
1463-9076
1463-9084

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Instagram YouTube

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.