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
 | 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.

Viewed

Total article views: 3,502 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,667 787 48 3,502 26 26
  • HTML: 2,667
  • PDF: 787
  • XML: 48
  • Total: 3,502
  • BibTeX: 26
  • EndNote: 26
Views and downloads (calculated since 26 Jan 2021)
Cumulative views and downloads (calculated since 26 Jan 2021)

Viewed (geographical distribution)

Total article views: 3,502 (including HTML, PDF, and XML) Thereof 3,529 with geography defined and -27 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 03 Feb 2023
Download
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.
Altmetrics
Final-revised paper
Preprint