Generating transition states of isomerization reactions with deep learning
Author(s)
Pattanaik, Lagnajit; Ingraham, John; Grambow, Colin A.; Green Jr, William H
DownloadPublished version (3.277Mb)
Publisher with Creative Commons License
Publisher with Creative Commons License
Creative Commons Attribution
Terms of use
Metadata
Show full item recordAbstract
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
2020-10Department
Massachusetts Institute of Technology. Department of Chemical Engineering; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence LaboratoryJournal
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