Progress towards machine learning reaction rate constants

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.

Automated summary

Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features, overcoming the infeasibility caused by the curse of dimensionality. The research discusses various kinetic datasets, input feature representations, and the use and design of machine learning algorithms for predicting reaction rate constants. Areas for further exploration to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants are also identified.