Department of Applied Physics, Aalto University, P.O. Box 11000, 00076 Aalto, Espoo, Finland
Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany
Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, 85748 Garching, Germany
Munich Center for Machine Learning, 80538 Munich, Germany
Viewed
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 2,049 (including HTML, PDF, and XML)
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Total
BibTeX
EndNote
1,975
59
15
2,049
36
63
HTML: 1,975
PDF: 59
XML: 15
Total: 2,049
BibTeX: 36
EndNote: 63
Views and downloads (calculated since 09 Sep 2024)
Cumulative views and downloads
(calculated since 09 Sep 2024)
Total article views: 1,816 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
1,742
59
15
1,816
36
63
HTML: 1,742
PDF: 59
XML: 15
Total: 1,816
BibTeX: 36
EndNote: 63
Views and downloads (calculated since 15 May 2025)
Cumulative views and downloads
(calculated since 15 May 2025)
Total article views: 233 (including HTML, PDF, and XML)
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233
0
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233
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0
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Total: 233
BibTeX: 0
EndNote: 0
Views and downloads (calculated since 09 Sep 2024)
Cumulative views and downloads
(calculated since 09 Sep 2024)
Viewed (geographical distribution)
Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.
Total article views: 2,049 (including HTML, PDF, and XML)
Thereof 2,000 with geography defined
and 49 with unknown origin.
Total article views: 1,816 (including HTML, PDF, and XML)
Thereof 1,771 with geography defined
and 45 with unknown origin.
Total article views: 233 (including HTML, PDF, and XML)
Thereof 229 with geography defined
and 4 with unknown origin.
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.
Machine learning has the potential to aid the identification of organic molecules involved in...