Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules

dc.contributorAalto-yliopistofi
dc.contributorAalto Universityen
dc.contributor.authorBesel, Vitusen_US
dc.contributor.authorTodorović, Milicaen_US
dc.contributor.authorKurtén, Theoen_US
dc.contributor.authorRinke, Patricken_US
dc.contributor.authorVehkamäki, Hannaen_US
dc.contributor.departmentDepartment of Applied Physicsen
dc.contributor.groupauthorComputational Electronic Structure Theoryen
dc.contributor.organizationUniversity of Helsinkien_US
dc.contributor.organizationUniversity of Turkuen_US
dc.date.accessioned2023-08-01T06:17:34Z
dc.date.available2023-08-01T06:17:34Z
dc.date.issued2023-07-12en_US
dc.descriptionPublisher Copyright: © 2023. The Author(s).
dc.description.abstractLow-volatile organic compounds (LVOCs) drive key atmospheric processes, such as new particle formation (NPF) and growth. Machine learning tools can accelerate studies of these phenomena, but extensive and versatile LVOC datasets relevant for the atmospheric research community are lacking. We present the GeckoQ dataset with atomic structures of 31,637 atmospherically relevant molecules resulting from the oxidation of α-pinene, toluene and decane. For each molecule, we performed comprehensive conformer sampling with the COSMOconf program and calculated thermodynamic properties with density functional theory (DFT) using the Conductor-like Screening Model (COSMO). Our dataset contains the geometries of the 7 Mio. conformers we found and their corresponding structural and thermodynamic properties, including saturation vapor pressures (pSat), chemical potentials and free energies. The pSat were compared to values calculated with the group contribution method SIMPOL. To validate the dataset, we explored the relationship between structural and thermodynamic properties, and then demonstrated a first machine-learning application with Gaussian process regression.en
dc.description.versionPeer revieweden
dc.format.extent11
dc.format.extent1-11
dc.format.mimetypeapplication/pdfen_US
dc.identifier.citationBesel, V, Todorović, M, Kurtén, T, Rinke, P & Vehkamäki, H 2023, ' Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules ', Scientific Data, vol. 10, no. 1, 450, pp. 1-11 . https://doi.org/10.1038/s41597-023-02366-xen
dc.identifier.doi10.1038/s41597-023-02366-xen_US
dc.identifier.issn2052-4463
dc.identifier.otherPURE UUID: 37909c07-3f3b-4a8f-9e4d-ef7e86b6d5aben_US
dc.identifier.otherPURE ITEMURL: https://research.aalto.fi/en/publications/37909c07-3f3b-4a8f-9e4d-ef7e86b6d5aben_US
dc.identifier.otherPURE LINK: http://www.scopus.com/inward/record.url?scp=85164541565&partnerID=8YFLogxKen_US
dc.identifier.otherPURE FILEURL: https://research.aalto.fi/files/116593043/Atomic_structures_conformers_and_thermodynamic_properties_of_32k_atmospheric_molecules.pdfen_US
dc.identifier.urihttps://aaltodoc.aalto.fi/handle/123456789/122169
dc.identifier.urnURN:NBN:fi:aalto-202308014530
dc.language.isoenen
dc.publisherNature Publishing Group
dc.relation.ispartofseriesScientific Dataen
dc.relation.ispartofseriesVolume 10, issue 1en
dc.rightsopenAccessen
dc.titleAtomic structures, conformers and thermodynamic properties of 32k atmospheric moleculesen
dc.typeData Articlefi
dc.type.versionpublishedVersion
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