A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Atomic structures, conformers and thermodynamic properties of 32k atmospheric molecules
Tekijät: Besel Vitus, Todorović Milica, Kurten Theo, Rinke Patrick, Vehkamäki Hanna
Kustantaja: NATURE PORTFOLIO
Julkaisuvuosi: 2023
Journal: Scientific Data
Tietokannassa oleva lehden nimi: SCIENTIFIC DATA
Lehden akronyymi: SCI DATA
Artikkelin numero: 450
Vuosikerta: 10
Sivujen määrä: 11
eISSN: 2052-4463
DOI: https://doi.org/10.1038/s41597-023-02366-x
Verkko-osoite: https://www.nature.com/articles/s41597-023-02366-x
Rinnakkaistallenteen osoite: 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
