Machine learning and semi-empirical calculations: a synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction.

ABSTRACT: Modern QM modelling methods, such as DFT, have provided detailed mechanistic insights into countless reactions. However, their computational cost inhibits their ability to rapidly screen large numbers of substrates and catalysts in reaction discovery. For a C-C bond forming nitro-Michael addition, we introduce a synergistic semi-empirical quantum mechanical (SQM) and machine learning (ML) approach that allows the prediction of DFT-quality reaction barriers in minutes, even on a standard laptop using widely available modelling software. Mean absolute errors (MAEs) are obtained that are below the accepted chemical accuracy threshold of 1 kcal mol^-1 and substantially better than SQM methods without ML correction (5.71 kcal mol^-1). Predictive power is shown to hold when t

SUBMITTER: Farrar EHE 

PROVIDER: S-EPMC9242013 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature