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Machine learning activation energies of chemical reactions

Lewis-Atwell, Toby; Townsend, Piers A.; Grayson, Matthew N.

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Authors

Toby Lewis-Atwell

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Dr Piers Townsend Piers.Townsend@uwe.ac.uk
Lecturer in Environmental and Forensic Toxicology

Matthew N. Grayson



Abstract

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar.

Citation

Lewis-Atwell, T., Townsend, P. A., & Grayson, M. N. (2022). Machine learning activation energies of chemical reactions. Wiley Interdisciplinary Reviews: Computational Molecular Science, 12(4), e1593. https://doi.org/10.1002/wcms.1593

Journal Article Type Article
Acceptance Date Nov 22, 2021
Online Publication Date Dec 30, 2021
Publication Date Jul 1, 2022
Deposit Date Sep 5, 2022
Publicly Available Date Mar 28, 2024
Journal Wiley Interdisciplinary Reviews: Computational Molecular Science
Electronic ISSN 1759-0884
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 12
Issue 4
Pages e1593
DOI https://doi.org/10.1002/wcms.1593
Keywords Materials Chemistry; Computational Mathematics; Physical and Theoretical Chemistry; Computer Science Applications; Biochemistry
Public URL https://uwe-repository.worktribe.com/output/9949207
Publisher URL https://wires.onlinelibrary.wiley.com/doi/10.1002/wcms.1593

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