A1 Refereed original research article in a scientific journal
Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules
Authors: Besel Vitus, Todorović Milica, Kurten Theo, Rinke Patrick, Vehkamäki Hanna
Publisher: NATURE PORTFOLIO
Publication year: 2023
Journal: Scientific Data
Journal name in source: SCIENTIFIC DATA
Journal acronym: SCI DATA
Article number: 450
Volume: 10
Number of pages: 11
eISSN: 2052-4463
DOI: https://doi.org/10.1038/s41597-023-02366-x
Web address : https://www.nature.com/articles/s41597-023-02366-x
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/180438809
Low-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 & alpha;-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 (p(Sat)), chemical potentials and free energies. The p(Sat) 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.
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