Articles | Volume 21, issue 17
Atmos. Chem. Phys., 21, 13227–13246, 2021
Atmos. Chem. Phys., 21, 13227–13246, 2021
Research article
 | Highlight paper
06 Sep 2021
Research article  | Highlight paper | 06 Sep 2021

Predicting gas–particle partitioning coefficients of atmospheric molecules with machine learning

Emma Lumiaro et al.

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Cited articles

Arp, H. P. H. and Goss, K.-U.: Ambient Gas/Particle Partitioning. 3. Estimating Partition Coefficients of Apolar, Polar, and Ionizable Organic Compounds by Their Molecular Structure, Environ. Sci. Technol., 43, 1923–1929, 2009. a
Barnes, E. A., Hurrell, J. W., Ebert-Uphoff, I., Anderson, C., and Anderson, D.: Viewing Forced Climate Patterns Through an AI Lens, Geophys. Res. Lett., 46, 13389–13398, 2019. a, b
Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., and Ceriotti, M.: Machine learning unifies the modeling of materials and molecules, Sci. Adv., 3, e1701816,, 2017. a
Bianchi, F., Kurtén, T., Riva, M., Mohr, C., Rissanen, M. P., Roldin, P., Berndt, T., Crounse, J. D., Wennberg, P. O., Mentel, T. F., Wildt, J., Junninen, H., Jokinen, T., Kulmala, M., Worsnop, D. R., Thornton, J. A., Donahue, N., Kjaergaard, H. G., and Ehn, M.: Highly Oxygenated Organic Molecules (HOM) from Gas-Phase Autoxidation Involving Peroxy Radicals: A Key Contributor to Atmospheric Aerosol, Chem. Rev., 119, 3472–3509, 2019. a, b
Short summary
The study of climate change relies on climate models, which require an understanding of aerosol formation. We train a machine-learning model to predict the partitioning coefficients of atmospheric molecules, which govern condensation into aerosols. The model can make instant predictions based on molecular structures with accuracy surpassing that of standard computational methods. This will allow the screening of low-volatility molecules that contribute most to aerosol formation.
Final-revised paper