Show simple item record

dc.contributor.authorErmanis, Kristaps
dc.contributor.authorGoodman, Jonathan M.
dc.contributor.otherLee, Sanha
dc.date.accessioned2021-11-09T15:17:58Z
dc.date.available2021-11-09T15:17:58Z
dc.date.issued2021-11-09
dc.identifier.urihttps://rdmc.nottingham.ac.uk/handle/internal/9356
dc.description.abstract=============================================================== Data for paper "MolE8: Finding DFT Potential Energy Surface Minima Values from Force-Field Optimised Organic Molecules with New Machine Learning Representations" Sanha Lee, Kristaps Ermanis* and Jonathan M. Goodman* Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW and School of Chemistry, University of Nottingham, University Park Nottingham, Nottingham, NG7 2RD =============================================================== This dataset contains Gaussian DFT optimization and frequency calculation output files for all of the molecules used in the training of the MolE8 representations and machine learning methods. The dataset is divided in 7 parts to keep the archive file sizes manageable. Each folder contains data for around 8000 molecules. The data includes the geometry optimization *a.out files, frequency calculation *f.out files and *sdf files of the optimized structures for wider compatibility with visualization software. Part 1 contains structure files up to 009999A1* Part 2 contains structure files up to 019999A1* Part 3 contains structure files up to 021988A1* Part 4 contains structure files up to 39997A1* Part 5 contains structure files up to 49999A1* Part 6 contains structure files up to 59999A1* Part 6 contains structure files up to 69125A1* All structures in these folders have been optimized and frequencies calculated at B3LYP/6-31g(2df,p) level in gas phase. All of the files can be opened in any text editor. Gaussian output structures can be viewed and the frequency modes visualised in GausView, Avogadro, jmol and in most other molecular viewers/editors. *.sdf files can be viewed in essentially all 3D molecular editors and viewers.en_UK
dc.language.isoenen_UK
dc.publisherThe University of Nottinghamen_UK
dc.rightsCC-BY*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subject.lcshMachine learningen_UK
dc.subject.lcshChemistry, Organicen_UK
dc.subject.lcshDensity functionalsen_UK
dc.subject.lcshComputational chemistryen_UK
dc.titleDFT data used in training MolE8 chemical ML modelsen_UK
dc.identifier.doihttp://doi.org/10.17639/nott.7159
dc.subject.freeDFT, Gaussian, organic chemistry, machine learning, MolE8, neural networks, kernel ridge regressionen_UK
dc.subject.jacsPhysical sciences::Chemistry::Organic chemistryen_UK
dc.subject.lcQ Science::QD Chemistry::QD241 Organic chemistryen_UK
dc.subject.lcQ Science::QD Chemistry::QD450 Physical and theoretical chemistryen_UK
uon.divisionUniversity of Nottingham, UK Campus::Faculty of Science::School of Chemistryen_UK
uon.funder.controlledOtheren_UK
uon.datatypeGaussian 16 DFT software output filesen_UK
uon.funder.freeLeverhulme Trusten_UK
uon.funder.freeIsaac Newton Trusten_UK
uon.funder.freeTrinity College, University of Cambridgeen_UK
uon.grantECF-2017-255en_UK
uon.grant17.08(d)en_UK
uon.collectionmethodGaussian 16 DFT softwareen_UK
uon.preservation.rarelyaccessedtrue


Files in this item

Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

CC-BY
Except where otherwise noted, this item's license is described as Creative Commons by Attribution