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Generating Transition States of Isomerization Reactions with Deep Learning

preprint
revised on 15.05.2020 and posted on 18.05.2020 by Lagnajit Pattanaik, John Ingraham, Colin Grambow, William H. Green
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.

History

Email Address of Submitting Author

lagnajit@mit.edu

Institution

Massachusetts Institute of Technology

Country

USA

ORCID For Submitting Author

0000-0001-7001-6698

Declaration of Conflict of Interest

No conflict of interest

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