Dataset for "Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach"

1229 79 16984 3 59688 document 16984 data_archive_3_2.zip application/zip b2ec72fd43b20b86cde3473311ad096d MD5 175353643 2023-04-03 14:05:55 https://researchdata.bath.ac.uk/1229/4/data_archive_3_2.zip 1229 4 4 application/zip other Zip folder containing optimised ground state reactant (1,3-dipoles and dipolarophiles) and transition state structures for [3+2] cycloaddition reactions. These calculations were performed at two levels of theory (AM1 and wB97X-D/def2-TZVP). en public cc_by

data_archive_3_2.zip

data 16985 2 59694 document 16985 example_code.zip application/zip 4669cb0253c5f84029273e5bbe3e1f4d MD5 571922 2023-04-03 14:06:48 https://researchdata.bath.ac.uk/1229/5/example_code.zip 1229 5 5 application/zip other Zip folder containing exemplar code to perform the machine learning in the paper. The folder contains the example.ipynb, endo_dataset.pkl (the extracted data in .pkl file format), environment.yaml (the .yaml file to generate the environment) and, a README.md file. en public cc_by

example_code.zip

code 17105 2 60350 document 17105 data_archive.zip application/zip 22bd574f37d6e69451a9f716042c774b MD5 2105491517 2023-04-24 12:47:20 https://researchdata.bath.ac.uk/1229/6/data_archive.zip 1229 6 6 application/zip other Zip folder containing optimised ground state reactant (dienes and dienophiles) and transition state structures for Diels-Alder reactions. These calculations were performed at multiple levels of theory (AM1, PM3, wB97X-D/def2-TZVP, and DSD-PBEP86-D3(BJ)/def2-TZVP) and are grouped into different enumerations (enum1, enum2, enum3, enum4, and enum5). Included is a dictionaries folder that contains csv files to pair each transition state with its respective diene and dienophile. en public cc_by

data_archive.zip

data archive 12231 disk0/00/00/12/29 2023-05-31 11:46:04 2026-02-07 05:54:59 2023-05-31 11:46:04 data_collection show Espley Sam sge28@bath.ac.uk 0000-0002-1135-9890 University of Bath TRUE Farrar Elliot ehef20@bath.ac.uk 0000-0003-3350-2907 University of Bath FALSE Supervisor Grayson Matthew M.N.Grayson@bath.ac.uk 0000-0003-2116-7929 University of Bath Supervisor Tomasi Simone simone.tomasi@astrazeneca.com 0000-0002-9373-7639 AstraZeneca Supervisor Buttar David david.buttar@astrazeneca.com 0000-0001-5466-023X AstraZeneca Dataset for "Machine learning reaction barriers in low data regimes: a horizontal and diagonal transfer learning approach" CJ0020 dept_chem Machine Learning, Transfer Learning, Computational Chemistry, Diels-Alder, Gaussian Machine learning (ML) has previously been used to predict density functional theory (DFT) free energy reaction barriers on a variety of different reactions from semi-empirical quantum mechanical (SQM) inputs. These models can require expensive dataset curation and can struggle with generalisability outside of the datasets immediate chemical space. One such approach that can drastically lower the number of required training points is transfer learning (TL). We demonstrate that various TL techniques can be used to provide highly accurate results with a fraction of the training points required for standard ML, thus lowering the overall computational cost of barrier predictions. This dataset includes all the structural data in the form of Gaussian16 (Revision A.03 and C.01) output files for the Diels-Alder and [3+2] cycloaddition reactions used for this ML/TL analysis. This data archive also includes exemplar code for performing some standard ML from the manuscript. 2023-05-31 University of Bath public RightsHolder University of Bath RightsHolder AstraZeneca Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/V519637/1 Industrial CASE Account – University of Bath 2020 Engineering and Physical Sciences Research Council https://doi.org/10.13039/501100000266 EP/R513155/1 DTP 2018-19 University of Bath Engineering and Physical Sciences Research Council 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 Diels-Alder reactions were built using Schrödinger’s R-Group Enumeration. R-groups were placed on various different positions of both dienes and dienophiles; 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 three different molecular modelling methods (AM1, PM3, and wB97X-D/def2-TZVP). A subset of the reactions were also optimised with DSD-PBEP86-D3(BJ)/def2-TZVP. The same process was used for the [3+2] reactions however, these reactions were only optimised at the AM1 and wB97X-D/def2-TZVP levels of theory. en 1 10.15125/BATH-01229 https://doi.org/10.1039/D3DD00085K pub Balena High Performance Computing (HPC) System 3e22ef13-31b9-4700-b57e-57bcd3b3b985 Anatra High Performance Computing (HPC) System open Dataset 1455