Machine learning activation energies of chemical reactions

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. This article is categorized under: Structure and Mechanism > Reaction Mechanisms and Catalysis Computer and Information Science > Visualization

Automated summary

This article discusses the application of machine learning in predicting reaction activation barriers in chemistry, focusing on ML trained to predict the activation energies and comparisons of different chemical features for ML models. It also explores models that achieve high predictive accuracies with reduced datasets using Gaussian process regression ML model.