[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120129-en":3,"doc-seo-120129-105":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":4,"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},120129,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine learning-based estimator for electron impact ionization fragmentation patterns - Research paper summary","Electron impact ionization fragmentation patterns are vital for plasma physics, astrochemistry, and environmental science, yet mass spectrometry data required for branching ratios is often unavailable. The work introduces a machine learning approach that predicts mass spectra, then estimates partial electron impact ionization cross sections using predicted spectra and ionic appearance thresholds. Case studies include ammonia and the C2F5 radical, where predicted branching ratios and Binary-Encounter Bethe total ionization cross sections yield fragmentation patterns. Limitations include missing light fragments such as H+ and validity below 100 eV to reduce double ionization effects, while the method remains adaptable beyond BEB inputs.","ORCA – Online Research @  \nCardiff  \nThis is an Open Access document downloaded from ORCA, Cardiff University's institutional repository:[https://orca.cardiff.ac.uk/id/eprint/175000/](https://orca.cardiff.ac.uk/id/eprint/175000/)  \nThis is the author’s version of a work that was submitted to / accepted for publication.  \nCitation for final published version:  \nLemishko, Kateryna, Armstrong, Gregory S. J., Mohr, Sebastian, Nelson, Anna, Tennyson, Jonathan and Knowles, Peter J. 2025. Machine learning-based estimator for electron impact ionization fragmentation patterns. Journal of Physics D: Applied Physics 58 (10) , 105208. 10.1088/1361-6463/ada37e  \nPublishers page: [http://dx.doi.org/10.1088/1361-6463/ada37e](http://dx.doi.org/10.1088/1361-6463/ada37e)  \nPlease note:  \nChanges made as a result of publishing processes such as copy-editing, formatting and page numbers may not be reflected in this version. For the definitive version of this publication, please refer to the published source. You are advised to consult the publisher’s version if you wish to cite this paper.  \nThis version is being made available in accordance with publisher policies. See [http://orca.cf.ac.uk/policies.html](http://orca.cf.ac.uk/policies.html) for usage policies. Copyright and moral rights for publications made  \navailable in ORCA are retained by the copyright holders.  \nJournal of Physics D: Applied Physics  \nPAPER • OPEN ACCESS  \nMachine learning-based estimator for electron impact ionization fragmentation patterns  \nTo cite this article: Kateryna M Lemishko et al 2025 J. Phys. D: Appl. Phys. 58 105208  \nView the article online for updates and enhancements.  \nYou may also like  \n-Coexistence of Kondo effect and non~~trivial Berry phase ~~in~~ ~~G~~d doped ~~Bi~~2~~Se~~3~~:~~ ~~an ARPES and magneto-transport study  \nSwayangsiddha Ghosh, Rahul Singh, Srishti Dixit et al.  \n-CHARACTERIZING THE COOL KOIs.  \nVIII. PARAMETERS OF THE PLANETS ORBITING KEPLER’S COOLEST DWARFS  \nJonathan J. Swift, Benjamin T. 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Phys. 58 (2025) 105208 (15pp) [https://doi.org/10.1088/1361-6463/ada37e](https://doi.org/10.1088/1361-6463/ada37e)  \nMachine learning-based estimator for electron impact ionization fragmentation patterns  \nKateryna M Lemishko 1 􀁂, Gregory S J Armstrong1 􀁂, Sebastian Mohr1, Anna Nelson 1 􀁂 , Jonathan Tennyson 1,2, ∗ 􀁂 and Peter J Knowles3 􀁂  \n1 Quantemol Ltd, 320 City Rd, London EC1V 2NZ, United Kingdom  \n2 Department of Physics and Astronomy, University College London, London WC1E 6BT, United Kingdom  \n3 School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom  \nE-mail: [j.tennyson@ucl.ac.uk](j.tennyson@ucl.ac.uk)  \nReceived 1 July 2024, revised 4 December 2024 Accepted for publication 22 December 2024 Published 9 January 2025  \nAbstract  \nNumerous measurements and calculations exist for total electron impact ionization cross sections. However, knowing electron impact ionization fragmentation patterns is important in various scientific fields such as plasma physics, astrochemistry, and environmental sciences. Partial ionization cross sections can be calculated by multiplying total ionization cross sections with branching ratios for different fragments, which can be deduced from ionization mass spectra. However, the required mass spectrometry data is frequently unavailable. A machine learning-based method to predict mass spectra is presented. This method is used to estimate partial electron impact ionization cross sections using the predicted mass spectra and the appearance thresholds for the ionic fragments. As examples, ammonia and the C2F5 radical are considered: branching ratios derived from the predicted mass spectra and Binary-Encounter Beth","cbCaivmm0jVtqrZZ","https://ap.wps.com/l/cbCaivmm0jVtqrZZ","pdf",1923494,1,17,"English","en",105,"# Abstract\n# Introduction\n## Electron impact ionization cross sections\n## Challenges in obtaining fragmentation data\n# Machine learning-based prediction approach\n## Predicting mass spectra and partial cross sections\n## Case studies: ammonia and C2F5\n# Limitations and applicability\n## Missing light fragments and energy range\n# Conclusion","[{\"question\":\"What problem does the machine learning method address?\",\"answer\":\"It estimates electron impact ionization fragmentation patterns when the mass spectrometry data needed to derive branching ratios is unavailable.\"},{\"question\":\"How are partial ionization cross sections computed in this approach?\",\"answer\":\"Predicted mass spectra are combined with ionic appearance thresholds to estimate partial electron impact ionization cross sections.\"},{\"question\":\"What limitations affect the method’s reliability?\",\"answer\":\"Light fragments such as H+ are not accounted for due to absent training peaks, and the validity is restricted to electron energies below 100 eV to limit double-ionization contributions not covered by the BEB model.\"}]","Machine learning-based estimator for electron impact ionization fragmentation patterns - 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