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

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.