Articles | Volume 21, issue 17
Atmos. Chem. Phys., 21, 13227–13246, 2021
https://doi.org/10.5194/acp-21-13227-2021
Atmos. Chem. Phys., 21, 13227–13246, 2021
https://doi.org/10.5194/acp-21-13227-2021

Research article 06 Sep 2021

Research article | 06 Sep 2021

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

Emma Lumiaro et al.

Data sets

Atmospheric C10 dataset E. Lumiaro, M. Todorovic, P. Rinke, T. Kurten, and H. Vehkamäki https://doi.org/10.5281/zenodo.4291795

KRR for Atmospheric molecules Gitlab https://gitlab.com/cest-group/krr-and-atmospheric-molecules

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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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