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

1092 76 16038 2 53199 document 16038 structures.zip application/zip 1c53d038f24c0665101ccc6bf66d7dad MD5 1136300268 2022-05-20 09:16:37 https://researchdata.bath.ac.uk/1092/1/structures.zip 1092 1 1 application/zip other Zip file containing optimised structures for each level of theory (AM1, AM1-IEFPCM, PM6, PM6-IEFPCM, UFF, wB97XD, wB97XD-IEFPCM). With the exception of IEFPCM folders, each level of theory includes the nucleophile (nuc), 1037 Michael acceptors (gs), and 1037 Michael addition transition states for the reaction of the nucleophile with each Michael acceptor (ts), complete with IEFPCM(toluene) single point energy calculations. IEFPCM folders include only the 37 literature reactions. en public cc_by

structures.zip

data archive 10223 disk0/00/00/10/92 2022-06-15 11:20:39 2025-06-16 10:54:17 2022-06-15 11:20:39 data_collection show Farrar Elliot ehef20@bath.ac.uk 0000-0003-3350-2907 University of Bath TRUE Grayson Matt M.N.Grayson@bath.ac.uk 0000-0003-2116-7929 University of Bath FALSE Dataset for "Machine learning and semi-empirical calculations: A synergistic approach to rapid, accurate, and mechanism-based reaction barrier prediction" CJ0020 CM0010 dept_chem Gaussian, Computational Chemistry, Machine Learning, Michael Additions 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. 2022-06-14 University of Bath public All computed chemical structures used to build machine learning models to predict high-level chemical reaction barriers using low-level inputs. RightsHolder University of Bath Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/N509589/1 DTP 2016-2017 University of Bath Uncatalysed reactant and transition state geometries for 1000 Nitro-Michael addition reactions were built using Schrödinger’s R-Group Enumeration by varying at four positions of a generic Michael acceptor core with common organic fragments. In addition to the nucleophile, uncatalysed reactant and transition state geometries for a further 37 biologically important Nitro-Michael addition reactions from literature were built in Gaussian16 (Revision A.03). All reactant and transition state structures were conformationally searched using Schrödinger’s MacroModel (version 12.7). All structures were subsequently optimised using Gaussian16 (Revision A.03) using several different molecular modelling methods. en 1 10.15125/BATH-01092 https://doi.org/10.1039/D2SC02925A pub Balena High Performance Computing (HPC) System 3e22ef13-31b9-4700-b57e-57bcd3b3b985 open Dataset