1398 46 17989 6 67109 document 17989 data_archive.zip application/zip 7c1394e17bf9f9ca8f101c31309ae8fd MD5 4919231668 2024-05-08 14:56:01 https://researchdata.bath.ac.uk/1398/1/data_archive.zip 1398 1 1 application/zip other A zipped directory containing distortion/interaction calculations for four datasets: nitro-Michael addition (MA), Diels-Alder, [3+2] cycloaddition, and dimethyl malonate MA. These calculations have been performed at both AM1 and the DFT level of theory of the original dataset. For the dimethyl malonate MA dataset, the reactant and transition structure geometries are also provided. These calculations were performed at AM1 and wB97X-D/def2-TZVP (IEFPCM=Water)//wB97X-D/def2-TZVP. en staffonly all_rights_reserved

data_archive.zip

data archive 12231 disk0/00/00/13/98 2024-10-22 09:49:13 2025-01-31 11:48:29 2024-10-22 09:49:13 data_collection no_search Espley Sam sge28@bath.ac.uk 0000-0002-1135-9890 University of Bath TRUE Allsop Sam ssa48@bath.ac.uk 0009-0002-8284-898X University of Bath FALSE Supervisor Buttar David david.buttar@astrazeneca.com 0000-0001-5466-023X AstraZeneca Supervisor Tomasi Simone simone.tomasi@astrazeneca.com 0000-0002-9373-7639 AstraZeneca Supervisor Grayson Matthew M.N.Grayson@bath.ac.uk 0000-0003-2116-7929 University of Bath CJ0020 dept_chem Machine Learning, Gaussian, Distortion, Interaction, Activation, Strain, Reaction Barrier, Computational Chemistry, Diels-Alder, Michael Addition, Cycloadditions Machine learning (ML) has previously been applied to predict reaction barriers for a variety of different chemical reactions. This is seen as the end point for this type of study however, post-reaction barrier analysis/energy decomposition approaches can provide insight into chemical reactivity. One such approach that has previously been used to provide information on chemical reactivity, for cycloaddition reactions in particular, is distortion/interaction-activation strain analysis (DIAS). We demonstrate that ML can be coupled with cheap and rapid semi-empirical quantum mechanical methods (SQM) to predict distortion and interaction energies at a fraction of the computational cost associated with running density functional theory (DFT) calculations. This dataset includes all the structural data in the form of Gaussian16 (Revision A.03 and C.01) output files for the four datasets used in this work and, the literature dataset reactions. 2024-10-21 University of Bath restricted https://github.com/the-grayson-group/distortion-interaction_ML GitHub Link RightsHolder University of Bath RightsHolder AstraZeneca Engineering and Physical Sciences Research Council (EPSRC) https://doi.org/10.13039/501100000266 EP/V519637/1 Industrial CASE Account - University of Bath 2020 Engineering and Physical Sciences Research Council (EPSRC) https://doi.org/10.13039/501100000266 EP/W003724/1 Machine Learning and Molecular Modelling: A Synergistic Approach to Rapid Reactivity Prediction Ground state reactant and transition state geometries for dimethyl malonate Michael addition reactions were built using Schrödinger’s R-Group Enumeration. R-groups were placed on various different positions of the Michael acceptor; the position depended upon the molecules in question. All structures were built in Gaussian16 (Revisions A.03 and C.01) and were conformationally searched using Schrödinger’s MacroModel (version 12.7). All structures were subsequently optimised using Gaussian16 (Revisions A.03 and C.01) using AM1 and wB97X-D/def2-TZVP (IEFPCM=Water)//wB97X-D/def2-TZVP. For distortion/interaction-activation strain calculations, python code (available on the associated GitHub page: https://github.com/the-grayson-group/distortion-interaction_ML) was used to separate the distorted reactant structures before single point energies were calculated using Gaussian16 (Revision C.01) using AM1 and the DFT level of theory used in the original transition structure calculation. en 1 10.15125/BATH-01398 https://doi.org/10.1039/D4DD00224E pub https://doi.org/10.15125/BATH-01229 data Anatra HTC cluster 70796ba8-e816-43ad-a262-6f8cfa5870e9 open Due to the duplication of a file, a new version was created. 1455