Issue 5, 2022

Progress towards machine learning reaction rate constants

Abstract

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the “curse of dimensionality”, their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants.

Graphical abstract: Progress towards machine learning reaction rate constants

Supplementary files

Article information

Article type
Perspective
Submitted
26 ستمبر 2021
Accepted
03 دسمبر 2021
First published
06 دسمبر 2021

Phys. Chem. Chem. Phys., 2022,24, 2692-2705

Progress towards machine learning reaction rate constants

E. Komp, N. Janulaitis and S. Valleau, Phys. Chem. Chem. Phys., 2022, 24, 2692 DOI: 10.1039/D1CP04422B

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements