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

Sandström, Hilda; Rinke, Patrick

doi:10.5194/gmd-18-2701-2025

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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Total article views: 5,007 (including HTML, PDF, and XML)

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Month	HTML	PDF	XML	Total
Sep 2024	170	0	170
Oct 2024	60	0	60
Nov 2024	50	0	50
Dec 2024	20	0	20
Jan 2025	92	0	92
Feb 2025	48	0	48
Mar 2025	12	0	12
Apr 2025	12	0	12
May 2025	164	10	4	178
Jun 2025	124	17	16	157
Jul 2025	84	11	3	98
Aug 2025	425	11	0	436
Sep 2025	1,476	8	3	1,487
Oct 2025	145	16	5	166
Nov 2025	201	36	4	241
Dec 2025	365	59	12	436
Jan 2026	283	47	15	345
Feb 2026	403	19	9	431
Mar 2026	105	29	9	143
Apr 2026	142	35	2	179
May 2026	75	28	7	110
Jun 2026	22	7	5	34
Jul 2026	64	19	4	87
Aug 2026	13	2	0	15

Cumulative views and downloads (calculated since 09 Sep 2024)

Month	HTML	PDF	XML	Total
Sep 2024	170	0	170
Oct 2024	60	0	60
Nov 2024	50	0	50
Dec 2024	20	0	20
Jan 2025	92	0	92
Feb 2025	48	0	48
Mar 2025	12	0	12
Apr 2025	12	0	12
May 2025	164	10	4	178
Jun 2025	124	17	16	157
Jul 2025	84	11	3	98
Aug 2025	425	11	0	436
Sep 2025	1,476	8	3	1,487
Oct 2025	145	16	5	166
Nov 2025	201	36	4	241
Dec 2025	365	59	12	436
Jan 2026	283	47	15	345
Feb 2026	403	19	9	431
Mar 2026	105	29	9	143
Apr 2026	142	35	2	179
May 2026	75	28	7	110
Jun 2026	22	7	5	34
Jul 2026	64	19	4	87
Aug 2026	13	2	0	15

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Total article views: 5,007 (including HTML, PDF, and XML) Thereof 4,947 with geography defined and 60 with unknown origin.

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Latest update: 06 Aug 2026

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.