Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models
Autor(a) principal: | |
---|---|
Data de Publicação: | 2024 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFG |
dARK ID: | ark:/38995/0013000003v9x |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/13368 |
Resumo: | In the present study, the stereoselectivity of Claisen Rearrangements was addressed, focusing on the influence of two distinct electron-withdrawing groups and eight different substituents in three variants of the rearrangement: Hurd, Eschenmoser, and Johnson. Using the Curtin-Hammett principle, the energies of reactions, products, and transition states were calculated using the M062X/def2TZVPP theory level. The results indicate that kinetic effects predominantly govern the reaction equilibrium. A key aspect of our investigation involved applying Shubin’s energy decomposition analysis to the optimized transition states. This approach highlighted the significant influence of the electrostatic component on stereoselectivity, revealing its predominance over the quantum and steric components. Moreover, each transition state was divided into four fragments: the electron-withdrawing groups (Ester and Nitrile), the specific Hurd/Esch/John group (H, NMe2, and OEt), various substituents (alkyl and aryl), and the central fragment. This fragmentation allowed for a comprehensive analysis of the dipole moments of the groups and non-covalent interactions, providing insights into the electrostatic forces driving the rearrangement process. In addition, Supervised Machine Learning algorithms were employed, focusing on the analysis of electronic and geometric datasets related to the transition states. The results obtained not only elucidate the mechanisms underlying the stereoselectivity of Claisen Rearrangements but also provide a subtle understanding of the interaction between different molecular components, establishing new perspectives in advanced applications in organic synthesis. |
id |
UFG-2_ef8d40bc2a22b31838bff4615ce967b2 |
---|---|
oai_identifier_str |
oai:repositorio.bc.ufg.br:tede/13368 |
network_acronym_str |
UFG-2 |
network_name_str |
Repositório Institucional da UFG |
repository_id_str |
|
spelling |
Oliveira, Heibbe Cristhian Benedito dehttp://lattes.cnpq.br/ 5995553993631378Oliveira, Heibbe Cristhian Benedito deAlonso, Christian GonçalvesMuniz, Aline SilvaSilveira Neto, Brenno Amaro daOliveira, Guilherme Colherinhas dehttp://lattes.cnpq.br/1762107968815537Oliveira, Ana Gabriela Coelho2024-09-18T12:05:45Z2024-09-18T12:05:45Z2024-01-29OLIVEIRA, A. G. C. Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models. 2024. 209 f. Tese (Doutorado em Química) - Faculdade de Química, Universidade Federal de Goiás, Goiânia, 2024.http://repositorio.bc.ufg.br/tede/handle/tede/13368ark:/38995/0013000003v9xIn the present study, the stereoselectivity of Claisen Rearrangements was addressed, focusing on the influence of two distinct electron-withdrawing groups and eight different substituents in three variants of the rearrangement: Hurd, Eschenmoser, and Johnson. Using the Curtin-Hammett principle, the energies of reactions, products, and transition states were calculated using the M062X/def2TZVPP theory level. The results indicate that kinetic effects predominantly govern the reaction equilibrium. A key aspect of our investigation involved applying Shubin’s energy decomposition analysis to the optimized transition states. This approach highlighted the significant influence of the electrostatic component on stereoselectivity, revealing its predominance over the quantum and steric components. Moreover, each transition state was divided into four fragments: the electron-withdrawing groups (Ester and Nitrile), the specific Hurd/Esch/John group (H, NMe2, and OEt), various substituents (alkyl and aryl), and the central fragment. This fragmentation allowed for a comprehensive analysis of the dipole moments of the groups and non-covalent interactions, providing insights into the electrostatic forces driving the rearrangement process. In addition, Supervised Machine Learning algorithms were employed, focusing on the analysis of electronic and geometric datasets related to the transition states. The results obtained not only elucidate the mechanisms underlying the stereoselectivity of Claisen Rearrangements but also provide a subtle understanding of the interaction between different molecular components, establishing new perspectives in advanced applications in organic synthesis.No presente estudo foi abordado a estereosseletividade dos Rearranjos de Claisen, focando na influência de dois grupos distintos de retirada de elétrons e oito substituintes diferentes em três variantes do rearranjo: Hurd, Eschenmoser e Johnson. Utilizando o princípio de Curtin-Hammett, foi calculado as energias das reações, produtos e estados de transição usando o nível de teoria M062X/def2TZVPP. Os resultados indicam que efeitos cinéticos predominantemente governam o equilíbrio da reação. Um aspectochave de nossa investigação envolveu a aplicação da análise de decomposição de energia de Shubin aos estados de transição otimizados. Essa abordagem destacou a influência significativa do componente eletrostático na estereosseletividade, revelando sua predominância sobre os componentes quânticos e estéricos. Além disso, cada estado de transição foi dividido em quatro fragmentos: os grupos de retirada de elétrons (Éster e Nitrila), o grupo específico de Hurd/Esch/John (H, NMe2, e OEt), vários substituintes (alquil e aril) e o fragmento central. Essa fragmentação possibilitou uma análise abrangente dos momentos dipolares dos grupos e das interações não covalentes, fornecendo insights sobre as forças eletrostáticas que impulsionam o processo de rearranjo. Além disso, foram empregados algorítmos de Aprendizado de Máquina Supervisionado com foco na análise de conjuntos de dados eletrônicos e geométricos relacionados aos estados de transição. Os resultados obtidos além de elucidarem os mecanismos que fundamentam a estereosseletividade das Rearranjos de Claisen, fornecem uma compreensão sutil da interação entre diferentes componentes moleculares, estabelecendo novas perspectivas no que se refere as aplicações avançadas na síntese orgânica.Submitted by Onia Arantes Albuquerque (onia.ufg@gmail.com) on 2024-09-17T13:32:16Z workflow start=Step: editstep - action:claimaction No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Tese - Ana Gabriela Coelho Oliveira - 2024.pdf: 54876881 bytes, checksum: 25256f74cd35768f279213a790a8b638 (MD5)Step: editstep - action:editaction Approved for entry into archive by Onia Arantes Albuquerque(onia.ufg@gmail.com) on 2024-09-18T12:05:44Z (GMT)Made available in DSpace on 2024-09-18T12:05:45Z (GMT). No. of bitstreams: 2 license_rdf: 805 bytes, checksum: 4460e5956bc1d1639be9ae6146a50347 (MD5) Tese - Ana Gabriela Coelho Oliveira - 2024.pdf: 54876881 bytes, checksum: 25256f74cd35768f279213a790a8b638 (MD5) Previous issue date: 2024-01-29Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESengUniversidade Federal de GoiásPrograma de Pós-graduação em Química (IQ)UFGBrasilInstituto de Química - IQ (RMG)Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessRearranjos de ClaisenEstereosseletividadeEstado de transiçãoTeoria do funcional de densidadeDecomposição de energiaAprendizado de máquina supervisionadoClaisen rearrangementsStereoselectivityTransition stateDensity functional theoryEnergy decompositionSupervised machine learningCIENCIAS EXATAS E DA TERRA::QUIMICADeciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning modelsDecifrando a estereosseletividade dos rearranjos de Claisen: modelos conjuntos de teoria funcional da densidade e aprendizado de máquinainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisreponame:Repositório Institucional da UFGinstname:Universidade Federal de Goiás (UFG)instacron:UFGLICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://repositorio.bc.ufg.br/tede/bitstreams/9db9e813-ffd2-4d07-84a2-c42b0a971c9f/download8a4605be74aa9ea9d79846c1fba20a33MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805http://repositorio.bc.ufg.br/tede/bitstreams/720b88d0-4d9b-42f1-a52d-3391b9cad413/download4460e5956bc1d1639be9ae6146a50347MD53ORIGINALTese - Ana Gabriela Coelho Oliveira - 2024.pdfTese - Ana Gabriela Coelho Oliveira - 2024.pdfapplication/pdf54876881http://repositorio.bc.ufg.br/tede/bitstreams/c92fb3ef-81a7-4156-adc2-d2e0afa98a84/download25256f74cd35768f279213a790a8b638MD54tede/133682024-09-18 09:05:45.446http://creativecommons.org/licenses/by-nc-nd/4.0/Attribution-NonCommercial-NoDerivatives 4.0 Internationalopen.accessoai:repositorio.bc.ufg.br:tede/13368http://repositorio.bc.ufg.br/tedeRepositório InstitucionalPUBhttp://repositorio.bc.ufg.br/oai/requesttasesdissertacoes.bc@ufg.bropendoar:2024-09-18T12:05:45Repositório Institucional da UFG - Universidade Federal de Goiás (UFG)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 |
dc.title.none.fl_str_mv |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
dc.title.alternative.por.fl_str_mv |
Decifrando a estereosseletividade dos rearranjos de Claisen: modelos conjuntos de teoria funcional da densidade e aprendizado de máquina |
title |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
spellingShingle |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models Oliveira, Ana Gabriela Coelho Rearranjos de Claisen Estereosseletividade Estado de transição Teoria do funcional de densidade Decomposição de energia Aprendizado de máquina supervisionado Claisen rearrangements Stereoselectivity Transition state Density functional theory Energy decomposition Supervised machine learning CIENCIAS EXATAS E DA TERRA::QUIMICA |
title_short |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
title_full |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
title_fullStr |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
title_full_unstemmed |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
title_sort |
Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models |
author |
Oliveira, Ana Gabriela Coelho |
author_facet |
Oliveira, Ana Gabriela Coelho |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Oliveira, Heibbe Cristhian Benedito de |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/ 5995553993631378 |
dc.contributor.referee1.fl_str_mv |
Oliveira, Heibbe Cristhian Benedito de |
dc.contributor.referee2.fl_str_mv |
Alonso, Christian Gonçalves |
dc.contributor.referee3.fl_str_mv |
Muniz, Aline Silva |
dc.contributor.referee4.fl_str_mv |
Silveira Neto, Brenno Amaro da |
dc.contributor.referee5.fl_str_mv |
Oliveira, Guilherme Colherinhas de |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/1762107968815537 |
dc.contributor.author.fl_str_mv |
Oliveira, Ana Gabriela Coelho |
contributor_str_mv |
Oliveira, Heibbe Cristhian Benedito de Oliveira, Heibbe Cristhian Benedito de Alonso, Christian Gonçalves Muniz, Aline Silva Silveira Neto, Brenno Amaro da Oliveira, Guilherme Colherinhas de |
dc.subject.por.fl_str_mv |
Rearranjos de Claisen Estereosseletividade Estado de transição Teoria do funcional de densidade Decomposição de energia Aprendizado de máquina supervisionado |
topic |
Rearranjos de Claisen Estereosseletividade Estado de transição Teoria do funcional de densidade Decomposição de energia Aprendizado de máquina supervisionado Claisen rearrangements Stereoselectivity Transition state Density functional theory Energy decomposition Supervised machine learning CIENCIAS EXATAS E DA TERRA::QUIMICA |
dc.subject.eng.fl_str_mv |
Claisen rearrangements Stereoselectivity Transition state Density functional theory Energy decomposition Supervised machine learning |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::QUIMICA |
description |
In the present study, the stereoselectivity of Claisen Rearrangements was addressed, focusing on the influence of two distinct electron-withdrawing groups and eight different substituents in three variants of the rearrangement: Hurd, Eschenmoser, and Johnson. Using the Curtin-Hammett principle, the energies of reactions, products, and transition states were calculated using the M062X/def2TZVPP theory level. The results indicate that kinetic effects predominantly govern the reaction equilibrium. A key aspect of our investigation involved applying Shubin’s energy decomposition analysis to the optimized transition states. This approach highlighted the significant influence of the electrostatic component on stereoselectivity, revealing its predominance over the quantum and steric components. Moreover, each transition state was divided into four fragments: the electron-withdrawing groups (Ester and Nitrile), the specific Hurd/Esch/John group (H, NMe2, and OEt), various substituents (alkyl and aryl), and the central fragment. This fragmentation allowed for a comprehensive analysis of the dipole moments of the groups and non-covalent interactions, providing insights into the electrostatic forces driving the rearrangement process. In addition, Supervised Machine Learning algorithms were employed, focusing on the analysis of electronic and geometric datasets related to the transition states. The results obtained not only elucidate the mechanisms underlying the stereoselectivity of Claisen Rearrangements but also provide a subtle understanding of the interaction between different molecular components, establishing new perspectives in advanced applications in organic synthesis. |
publishDate |
2024 |
dc.date.accessioned.fl_str_mv |
2024-09-18T12:05:45Z |
dc.date.available.fl_str_mv |
2024-09-18T12:05:45Z |
dc.date.issued.fl_str_mv |
2024-01-29 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
OLIVEIRA, A. G. C. Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models. 2024. 209 f. Tese (Doutorado em Química) - Faculdade de Química, Universidade Federal de Goiás, Goiânia, 2024. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/13368 |
dc.identifier.dark.fl_str_mv |
ark:/38995/0013000003v9x |
identifier_str_mv |
OLIVEIRA, A. G. C. Deciphering the stereoselectivity of Claisen rearrangements: joint density functional theory and machine learning models. 2024. 209 f. Tese (Doutorado em Química) - Faculdade de Química, Universidade Federal de Goiás, Goiânia, 2024. ark:/38995/0013000003v9x |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/13368 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.publisher.program.fl_str_mv |
Programa de Pós-graduação em Química (IQ) |
dc.publisher.initials.fl_str_mv |
UFG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Química - IQ (RMG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFG instname:Universidade Federal de Goiás (UFG) instacron:UFG |
instname_str |
Universidade Federal de Goiás (UFG) |
instacron_str |
UFG |
institution |
UFG |
reponame_str |
Repositório Institucional da UFG |
collection |
Repositório Institucional da UFG |
bitstream.url.fl_str_mv |
http://repositorio.bc.ufg.br/tede/bitstreams/9db9e813-ffd2-4d07-84a2-c42b0a971c9f/download http://repositorio.bc.ufg.br/tede/bitstreams/720b88d0-4d9b-42f1-a52d-3391b9cad413/download http://repositorio.bc.ufg.br/tede/bitstreams/c92fb3ef-81a7-4156-adc2-d2e0afa98a84/download |
bitstream.checksum.fl_str_mv |
8a4605be74aa9ea9d79846c1fba20a33 4460e5956bc1d1639be9ae6146a50347 25256f74cd35768f279213a790a8b638 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFG - Universidade Federal de Goiás (UFG) |
repository.mail.fl_str_mv |
tasesdissertacoes.bc@ufg.br |
_version_ |
1815172550167101440 |