Issue 41, 2020

Generating transition states of isomerization reactions with deep learning

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.

Graphical abstract: Generating transition states of isomerization reactions with deep learning

Supplementary files

Article information

Article type
Paper
Submitted
04 sep 2020
Accepted
05 okt 2020
First published
05 okt 2020
This article is Open Access
Creative Commons BY license

Phys. Chem. Chem. Phys., 2020,22, 23618-23626

Generating transition states of isomerization reactions with deep learning

L. Pattanaik, J. B. Ingraham, C. A. Grambow and W. H. Green, Phys. Chem. Chem. Phys., 2020, 22, 23618 DOI: 10.1039/D0CP04670A

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