Dataset for "Machine learning and semi-empirical calculations: A synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction"

All computed chemical structures used to build machine learning models to predict high-level chemical reaction barriers using low-level inputs.

Modern quantum mechanical modelling methods, such as Density Functional Theory (DFT), have provided detailed mechanistic insights into countless reactions and have been used in the design of a handful of chemical transformations. 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 achieves the fast and accurate prediction of DFT-quality free energy activation barriers using purely SQM-derived data. This dataset includes all the structural data, in the form of Gaussian16 (Revision A.03) output files, for the Nitro-Michael reaction used for this machine learning analysis.