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Dissociative Electron Attachment Prediction of Halogenated Organic Molecules Using Machine Learning

2026-04-02Artigo científico Acesso aberto
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dc.contributor.authorSilva, Tomas
dc.contributor.authorLobo, Victor Sousa
dc.contributor.authorPereira-da-Silva, Joao
dc.contributor.authorDa Silva, Filipe Ferreira
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionDF – Departamento de Física
dc.contributor.institutionCeFITec – Centro de Física e Investigação Tecnológica
dc.contributor.pblACS - American Chemical Society
dc.date.accessioned2026-07-30T08:54:01Z
dc.date.available2026-07-30T08:54:01Z
dc.date.issued2026-04-02
dc.description
dc.description.abstractDissociative Electron Attachment (DEA) is a fundamental process in which low-energy electrons interact with molecules, causing bond dissociation and the formation of negative ions. It plays a key role in environmental science, nanotechnology, biology, and astrochemistry. However, experimental DEA studies are typically conducted in the gas phase under high-vacuum conditions, limiting the investigation of larger or less volatile molecules. To address these limitations, we developed machine learning (ML) models to predict negative ion formation in halogenated organic molecules. Classification models were designed to predict the energy range of the most intense DEA resonance, while regression models estimate its peak energy. A relational database consolidating experimental DEA data and molecular descriptors for 143 molecules served as the basis for model training and evaluation. Various ML approaches spanning different algorithm families were compared, using 120 molecules for training and 23 for testing. The ensemble Voting Classifier, combining three models from different families, achieved 94.9% accuracy in cross-validation with only one test set misclassification. The Random Forest model achieved the best regression results with a mean absolute error of 0.301 eV in cross-validation and 0.234 eV on the test set, comparable to typical experimental resolutions. These results demonstrate the feasibility of ML-based DEA prediction, establishing a foundation for computational approaches capable of expanding research to molecules that are challenging to study experimentally.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent2961176
dc.identifier.doi10.1021/acsomega.5c13274
dc.identifier.issn2470-1343
dc.identifier.otherPURE: 160531598
dc.identifier.otherPURE UUID: 76b9fac6-3067-4dce-ba36-66e8d3278656
dc.identifier.otherWOS: 001732700300001
dc.identifier.otherORCID: /0000-0002-0149-3367/work/222337280
dc.identifier.otherORCID: /0000-0002-2182-2965/work/222337988
dc.identifier.urihttp://hdl.handle.net/10362/204961
dc.identifier.urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=nova_api&SrcAuth=WosAPI&KeyUT=WOS:001732700300001&DestLinkType=FullRecord&DestApp=WOS_CPL
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Programático/UIDP%2F00068%2F2020/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Avaliação UID 2023%2F2024/UID%2F04152%2F2025/PT
dc.relationinfo:eu-repo/grantAgreement/FCT/Avaliação UID 2023%2F2024 PRR/UID%2FPRR%2F04152%2F2025/PT
dc.titleDissociative Electron Attachment Prediction of Halogenated Organic Molecules Using Machine Learningen
dc.typejournal article
degois.publication.firstPage22082
degois.publication.issue14
degois.publication.lastPage22092
degois.publication.titleACS Omega
degois.publication.volume11
dspace.entity.typePublication
rcaap.rightsopenAccess

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