[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119657-en":3,"doc-seo-119657-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119657,7971461741311,"Ophelia","https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826",8,"Research & Report","Similarity-based analysis of atmospheric organic compounds for machine learning applications","Atmospheric aerosol particle formation significantly influences air quality and climate, yet many involved organic molecules remain uncharacterized. Machine learning offers faster identification by leveraging molecular properties and detection characteristics, but progress is limited by the absence of curated datasets for atmospheric molecules and their associated features. The study introduces a similarity analysis linking atmospheric compounds to large molecular datasets used in machine learning development. Results show only a small overlap under standard representations, with the out-of-domain nature linked to distinct functional groups and elemental composition, motivating shared molecular-level atmospheric chemistry data to support future dataset curation.","This is an electronic reprint of the original article.  \nThis reprint may differ from the original in pagination and typographic detail.  \nSandström, Hilda; Rinke, Patrick  \nSimilarity-based analysis of atmospheric organic compounds for machine learning applications  \nPublished in:  \nGeoscientific Model Development  \nDOI:  \n10.5194/gmd-18-2701-2025  \nPublished: 01/01/2025  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublished under the following license:  \nCC BY  \nPlease cite the original version:  \nSandström, H. , & Rinke, P. (2025) . Similarity-based analysis of atmospheric organic compounds for machine learning applications. Geoscientific Model Development, 18(9), 2701-2724 . [https://doi.org/10.5194/gmd-18-](https://doi.org/10.5194/gmd-18-)[ ](https://doi.org/10.5194/gmd-18-)[2701-2025](2701-2025)  \nThis material is protected by copyright and other intellectual property rights, and duplication or sale of all or part of any of the repository collections is not permitted, except that material may be duplicated by you foryour research use or educational purposes in electronic or print form. You must obtain permission for anyother use. Electronic or print copies may not be offered, whether for sale or otherwise to anyone who is not an authorised user.  \nGeosci. Model Dev., 18, 2701–2724, 2025 [https://doi.org/10.5194/gmd-18-2701-2025](https://doi.org/10.5194/gmd-18-2701-2025)[ ](https://doi.org/10.5194/gmd-18-2701-2025)© Author(s) 2025 . This work is distributed under the Creative Commons Attribution 4 .0 License.  \nSimilarity-based analysis of atmospheric organic compounds for machine learning applications  \nHilda Sandström 1 and Patrick Rinke 1,2,3,4  \n1Department of Applied Physics, Aalto University, P.O. Box 11000, 00076 Aalto, Espoo, Finland  \n2Physics Department, TUM School of Natural Sciences, Technical University of Munich, 85748 Garching, Germany  \n3Atomistic Modelling Center, Munich Data Science Institute, Technical University of Munich, 85748 Garching, Germany  \n4Munich Center for Machine Learning, 80538 Munich, Germany Correspondence: Patrick Rinke (patrick.rinke@aalto.fi)  \nReceived: 1 August 2024 – Discussion started: 9 September 2024  \nRevised: 22 January 2025 – Accepted: 18 February 2025 – Published: 15 May 2025  \nAbstract. The formation of aerosol particles in the atmosphere impacts air quality and climate change, but many of the organic molecules involved remain unknown. Machine learning could aid in identifying these compounds through accelerated analysis of molecular properties and detection characteristics. However, such progress is hindered by the current lack of curated datasets for atmospheric molecules and their associated properties. To tackle this challenge, we propose a similarity analysis that connects atmospheric compounds to existing large molecular datasets used for machine learning development. We find a small overlap between atmospheric and non-atmospheric molecules using standard molecular representations in machine learning applications. The identified out-of-domain character of atmospheric compounds is related to their distinct functional groups and atomic composition. Our investigation underscores the need for collaborative efforts to gather and share more molecularlevel atmospheric chemistry data. The presented similaritybased analysis can be used for future dataset curation for machine learning development in the atmospheric sciences.  \n1 Introduction  \nAerosol particles influence our climate by sunlight reflection and absorption, as well as by serving as nuclei for cloud condensation (Pörtner et al., 2023) . Beyond climate impact, aerosol particles affect air quality, causing adverse effects on human health (Pozzer et al., 2023 ; Khomenko et al., 2021 ; Lelieveld et al., 2020) . However, the underlying molecular-  \nlevel processes involving organic molecules remain poorly understood, due to the vast number of organic compounds participating in atmospheric chemistry","cbCaifXoRDSWASVQ","https://ap.wps.com/l/cbCaifXoRDSWASVQ","pdf",11563586,1,25,"English","en",105,"# Introduction\n## Aerosol impacts on climate and air quality\n## Knowledge gap at molecular level\n## Machine learning motivation\n## Similarity-based analysis approach\n# Methods for assessment of models","[{\"question\":\"Why are aerosol-related atmospheric organic molecules difficult to identify?\",\"answer\":\"Many organic molecules participate in atmospheric chemistry, and a large fraction remains unknown. The difficulty is compounded by the limited availability of curated datasets for atmospheric molecules and their properties.\"},{\"question\":\"What problem does the proposed similarity analysis address?\",\"answer\":\"It connects atmospheric compounds to large existing molecular datasets commonly used for machine learning development. The goal is to enable tailored model development for aerosol formation studies.\"},{\"question\":\"What do the similarity results suggest about atmospheric compounds?\",\"answer\":\"The analysis finds a small overlap between atmospheric and non-atmospheric molecules when using standard machine-learning molecular representations. The out-of-domain character is associated with distinct functional groups and atomic composition.\"}]","Similarity-based analysis of atmospheric organic compounds for machine learning applications | PDF",1785725523,63,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"similarity-based-analysis-of-atmospheric-organic-compounds-for-machine-learning-applications","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/similarity-based-analysis-of-atmospheric-organic-compounds-for-machine-learning-applications/119657/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are aerosol-related atmospheric organic molecules difficult to identify?","Question",{"text":75,"@type":76},"Many organic molecules participate in atmospheric chemistry, and a large fraction remains unknown. The difficulty is compounded by the limited availability of curated datasets for atmospheric molecules and their properties.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the proposed similarity analysis address?",{"text":80,"@type":76},"It connects atmospheric compounds to large existing molecular datasets commonly used for machine learning development. The goal is to enable tailored model development for aerosol formation studies.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the similarity results suggest about atmospheric compounds?",{"text":84,"@type":76},"The analysis finds a small overlap between atmospheric and non-atmospheric molecules when using standard machine-learning molecular representations. The out-of-domain character is associated with distinct functional groups and atomic composition.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]