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Sci Data

Title:		"Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules"
Author(s):		Besel V; Todorovic M; Kurten T; Rinke P; Vehkamaki H;
Address:		"University of Helsinki, Institute for Atmospheric and Earth System Research, Helsinki, 00014, Finland. vitus.besel@helsinki.fi. University of Turku, Dept. Mechanical and Materials Engineering, Turku, FI-20014, Finland. University of Helsinki, Institute for Atmospheric and Earth System Research, Helsinki, 00014, Finland. Aalto University, Dept. of Applied Physics, P.O. Box 11100, FI-00076 Aalto, Espoo, Finland"
Journal Title:		Sci Data
Year:		2023
Volume:		20230712
Issue:		1
Page Number:		450 -
DOI:		10.1038/s41597-023-02366-x
ISSN/ISBN:		2052-4463 (Electronic) 2052-4463 (Linking)
Abstract:		"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"
Keywords:
Notes:		"PubMed-not-MEDLINEBesel, Vitus Todorovic, Milica Kurten, Theo Rinke, Patrick Vehkamaki, Hanna eng 426 346368/Academy of Finland (Suomen Akatemia)/ 345552/Academy of Finland (Suomen Akatemia)/ 337549/Academy of Finland (Suomen Akatemia)/ Dataset England 2023/07/13 Sci Data. 2023 Jul 12; 10(1):450. doi: 10.1038/s41597-023-02366-x"