Generating transition states of isomerization reactions with deep learning
Name
d0cp04670a.pdf
Description
Published version
Size
3.28 MB
Format
Adobe PDF
Checksum (MD5)
4f45135d944bd7ed1e343319fa7035b0
Author(s)
Pattanaik, Lagnajit
Ingraham, John
Grambow, Colin A.
Green Jr, William H
Date Issued
October 2020
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/d0cp04670a
