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.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2020-1258', Frank Wania, 13 Feb 2021
  • RC2: 'Comment on acp-2020-1258', Anonymous Referee #2, 16 Feb 2021
  • RC3: 'Comment on acp-2020-1258', Anonymous Referee #3, 23 Mar 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Emma Lumiaro on behalf of the Authors (26 May 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (12 Jul 2021) by Gordon McFiggans
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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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