Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory

Detalhes bibliográficos
Autor(a) principal: Pereira, Florbela
Data de Publicação: 2023
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/153974
Resumo: FP gratefully acknowledges FCT for an Assistant Research Position (CEECIND/01649/2021). We thank Chemaxon Ltd. for access to JChem and Marvin. The author thanks João Aires‐de‐Sousa (NOVA School of Science and Technology, NOVA University of Lisbon) for many interesting discussions and suggestions about different aspects of this work Publisher Copyright: © 2023 Wiley-VCH GmbH.
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spelling Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theorydensity functional theory (DFT)electron affinity energyionization potential energymachine learning (ML)quantitative structure property relationships (QSPR)Chemistry(all)FP gratefully acknowledges FCT for an Assistant Research Position (CEECIND/01649/2021). We thank Chemaxon Ltd. for access to JChem and Marvin. The author thanks João Aires‐de‐Sousa (NOVA School of Science and Technology, NOVA University of Lisbon) for many interesting discussions and suggestions about different aspects of this work Publisher Copyright: © 2023 Wiley-VCH GmbH.Quantum chemical (QC) calculations based on density functional theory (DFT) provide increasingly accurate estimates of various properties, but with a relatively high computational cost. Machine learning (ML) techniques can be envisaged to extract new knowledge from these large volumes of data, creating empirical models to fast predict QC calculations in new situations. Here, ML algorithms were explored for the fast estimation of ionization potential (IP) and electron affinity (EA) energies calculated by DFT using the B3LYP and PBE0 with 6–31G** basic set on molecular descriptors generated from DFT-optimized geometries. A database of 9,410 and 9,627 small organic structures for IP and EA energies modelling were used, respectively. Several ML algorithms such as random forest, support vector machines, deep learning multilayer perceptron networks, and light gradient-boosting machine were screened. The best performance was achieved with a consensus regression model predicted an external test set of 972 and 963 small organic molecules achieving a mean absolute error up to 0.23 eV and 0.32 eV for modelling IP and EA energies, respectively.DQ - Departamento de QuímicaLAQV@REQUIMTERUNPereira, Florbela2023-04-252024-04-25T00:00:00Z2023-04-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/153974engPereira, F. (2023). Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory. ChemistrySelect, 8(16), [e202300036]. https://doi.org/10.1002/slct.2023000362365-6549PURE: 63679072https://doi.org/10.1002/slct.202300036info:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:36:25Zoai:run.unl.pt:10362/153974Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:55:26.590990Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
title Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
spellingShingle Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
Pereira, Florbela
density functional theory (DFT)
electron affinity energy
ionization potential energy
machine learning (ML)
quantitative structure property relationships (QSPR)
Chemistry(all)
title_short Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
title_full Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
title_fullStr Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
title_full_unstemmed Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
title_sort Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory
author Pereira, Florbela
author_facet Pereira, Florbela
author_role author
dc.contributor.none.fl_str_mv DQ - Departamento de Química
LAQV@REQUIMTE
RUN
dc.contributor.author.fl_str_mv Pereira, Florbela
dc.subject.por.fl_str_mv density functional theory (DFT)
electron affinity energy
ionization potential energy
machine learning (ML)
quantitative structure property relationships (QSPR)
Chemistry(all)
topic density functional theory (DFT)
electron affinity energy
ionization potential energy
machine learning (ML)
quantitative structure property relationships (QSPR)
Chemistry(all)
description FP gratefully acknowledges FCT for an Assistant Research Position (CEECIND/01649/2021). We thank Chemaxon Ltd. for access to JChem and Marvin. The author thanks João Aires‐de‐Sousa (NOVA School of Science and Technology, NOVA University of Lisbon) for many interesting discussions and suggestions about different aspects of this work Publisher Copyright: © 2023 Wiley-VCH GmbH.
publishDate 2023
dc.date.none.fl_str_mv 2023-04-25
2023-04-25T00:00:00Z
2024-04-25T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/153974
url http://hdl.handle.net/10362/153974
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Pereira, F. (2023). Machine Learning for the Prediction of Ionization Potential and Electron Affinity Energies Obtained by Density Functional Theory. ChemistrySelect, 8(16), [e202300036]. https://doi.org/10.1002/slct.202300036
2365-6549
PURE: 63679072
https://doi.org/10.1002/slct.202300036
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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