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



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.
Date made available14 Jun 2022
PublisherUniversity of Bath

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