1148 46 16045 3 53283 document 16045 data.zip application/zip 5abf03f97ce6340cb9496babfb5f903e MD5 1944707089 2022-05-27 14:08:40 https://researchdata.bath.ac.uk/1148/2/data.zip 1148 2 2 application/zip other Zip file containing Gaussian09 and Gaussian16 output files for five distinct projects included in the associated thesis, as well as a document with all machine learning model metrics, features, and hyperparameters. A README.txt file is also included that details the structure of the folders and files provided. en public cc_by

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documentation 16047 1 53293 document 16047 indexcodes.txt text/plain 1e0a546dd56dddae7862fde2994fecf0 MD5 955 2022-05-27 15:00:42 https://researchdata.bath.ac.uk/1148/4/indexcodes.txt 1148 4 4 text/plain other Generate index codes conversion from other to indexcodes en public

indexcodes.txt

http://eprints.org/relation/isVersionOf https://researchdata.bath.ac.uk/id/document/16046 http://eprints.org/relation/isVolatileVersionOf https://researchdata.bath.ac.uk/id/document/16046 http://eprints.org/relation/isIndexCodesVersionOf https://researchdata.bath.ac.uk/id/document/16046 archive 10223 disk0/00/00/11/48 2022-08-12 08:32:24 2024-07-18 12:32:04 2022-08-12 08:32:24 data_collection show Farrar Elliot ehef20@bath.ac.uk 0000-0003-3350-2907 University of Bath TRUE Supervisor Grayson Matthew M.N.Grayson@bath.ac.uk 0000-0003-2116-7929 University of Bath CJ0020 CM0010 dept_chem Computational chemistry, Asymmetric organocatalysis, Computational modelling, Density functional theory, Machine learning For several decades, chemical modelling methods such as density functional theory (DFT) have provided invaluable contributions to the understanding of asymmetric and catalytic reactions. However, machine learning (ML) models, once trained, could allow for much more rapid screening of chemical reactions. In the thesis associated with this dataset, research into two distinct approaches to understanding organic reactions - modelling and ML - are presented, including several examples of conventional modelling with DFT, as well as details of a new ML methodology that bridges the gap between semi-empirical quantum mechanical (SQM) methods and DFT. This repository contains Gaussian09 and Gaussian16 output files for all computed structures used in this work, as well as a document containing a complete list of all metrics, features, and hyperparameters for all computed ML models. 2022-08-12 University of Bath public All computed chemical structures used to model several chemical reactions and 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/R513155/1 DTP 2018-19 University of Bath Structures were generated by conformational searching with Schrödinger's MacroModel (Version 11.3, 11.6, 12.7) and enumeration with Schrödinger’s R-Group Enumeration. These structures were optimised with Gaussian09 (Revision D.01) (performed at the University of Cambridge) and Gaussian16 (Revision A.03) (performed at the University of Bath) using several different molecular modelling methods. Additional analyses were performed with NCIPLOT. Machine learning models were built using the Scikit-learn and mlxtend python packages. 2018-09-01 2022-05-26 en 1 10.15125/BATH-01148 https://doi.org/10.1002/anie.202005563 pub https://doi.org/10.1002/anie.202011256 pub https://doi.org/10.1021/acs.joc.0c02226 pub https://researchportal.bath.ac.uk/en/studentTheses/93e667c0-15f7-40b8-aca8-69d55534a1a9 thesis Balena High Performance Computing (HPC) System (Bath) 3e22ef13-31b9-4700-b57e-57bcd3b3b985 Anatra High Performance Computing (HPC) System (Bath) Darwin High Performance Computing (HPC) System (Cambridge) Ziggy Compute Server (Cambridge) open Dataset