Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 9
Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2701-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-2701-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 9
Methods for assessment of models
 | 
15 May 2025
Methods for assessment of models |  | 15 May 2025

Similarity-based analysis of atmospheric organic compounds for machine learning applications

Hilda Sandström and Patrick Rinke

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Latest update: 25 Oct 2025
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Short summary
Machine learning has the potential to aid the identification of organic molecules involved in aerosol formation. Yet, progress is stalled by a lack of curated atmospheric molecular datasets. Here, we compared atmospheric compounds with large molecular datasets used in machine learning and found minimal overlap with similarity algorithms. Our result underlines the need for collaborative efforts to curate atmospheric molecular data to facilitate machine learning models in atmospheric sciences.
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