Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 21, issue 17
Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13227-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-13227-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 21, issue 17
Research article
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06 Sep 2021
Research article | Highlight paper |  | 06 Sep 2021

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

Emma Lumiaro, Milica Todorović, Theo Kurten, Hanna Vehkamäki, and Patrick Rinke

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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.
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