Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies.

ABSTRACT: Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers w

SUBMITTER: Jorner K 

PROVIDER: S-EPMC9528810 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature