Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution

Detalhes bibliográficos
Autor(a) principal: Roosta, Ahmadreza
Data de Publicação: 2023
Outros Autores: Haghbakhsh, Reza, Rita C. Duarte, Ana, Raeissi, Sona
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/163970
Resumo: Funding Information: The authors wish to thank Shiraz University, University of Isfahan and Universidade Nova de Lisboa for the facilities provided. Publisher Copyright: © 2023 The Author(s)
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spelling Deep eutectic solvent viscosity prediction by hybrid machine learning and group contributionArtificial neural networkDESMachine learningPhysical propertySupport vector machineElectronic, Optical and Magnetic MaterialsAtomic and Molecular Physics, and OpticsCondensed Matter PhysicsSpectroscopyPhysical and Theoretical ChemistryMaterials ChemistryFunding Information: The authors wish to thank Shiraz University, University of Isfahan and Universidade Nova de Lisboa for the facilities provided. Publisher Copyright: © 2023 The Author(s)In this study, hybrid machine learning nonlinear models were developed to predict the viscosity of DESs by combining the group contribution (GC) concept with the multilayer perceptron, a well-known feedforward artificial neural network, and the Least Squares Support Vector Machine (LSSVM) algorithm. Deep Eutectic Solvents (DESs) have come to the forefront in recent years as appealing substitutes for conventional solvents. It is imperative to have a thorough grasp of the essential properties of DESs to expand the employment of these compounds in new procedures. Most frequently, one of the crucial physical properties of a DES that must be precisely determined is its viscosity. To develop the models, a dataset of 2533 viscosity data points for 305 DESs at various temperatures (from 277.15 to 373.15 K) was gathered to build the models. By using temperature, molar ratios, and functional groups as inputs, the results indicate that the suggested models can determine the viscosity of DESs with high accuracy. The models yield average absolute relative deviations below 10% and squared correlation coefficients higher than 0.98.LAQV@REQUIMTEDQ - Departamento de QuímicaRUNRoosta, AhmadrezaHaghbakhsh, RezaRita C. Duarte, AnaRaeissi, Sona2024-02-22T23:51:47Z2023-10-152023-10-15T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article11application/pdfhttp://hdl.handle.net/10362/163970eng0167-7322PURE: 83887859https://doi.org/10.1016/j.molliq.2023.122747info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:49:56Zoai:run.unl.pt:10362/163970Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:59:57.825090Repositório 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 Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
title Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
spellingShingle Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
Roosta, Ahmadreza
Artificial neural network
DES
Machine learning
Physical property
Support vector machine
Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Condensed Matter Physics
Spectroscopy
Physical and Theoretical Chemistry
Materials Chemistry
title_short Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
title_full Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
title_fullStr Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
title_full_unstemmed Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
title_sort Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
author Roosta, Ahmadreza
author_facet Roosta, Ahmadreza
Haghbakhsh, Reza
Rita C. Duarte, Ana
Raeissi, Sona
author_role author
author2 Haghbakhsh, Reza
Rita C. Duarte, Ana
Raeissi, Sona
author2_role author
author
author
dc.contributor.none.fl_str_mv LAQV@REQUIMTE
DQ - Departamento de Química
RUN
dc.contributor.author.fl_str_mv Roosta, Ahmadreza
Haghbakhsh, Reza
Rita C. Duarte, Ana
Raeissi, Sona
dc.subject.por.fl_str_mv Artificial neural network
DES
Machine learning
Physical property
Support vector machine
Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Condensed Matter Physics
Spectroscopy
Physical and Theoretical Chemistry
Materials Chemistry
topic Artificial neural network
DES
Machine learning
Physical property
Support vector machine
Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Condensed Matter Physics
Spectroscopy
Physical and Theoretical Chemistry
Materials Chemistry
description Funding Information: The authors wish to thank Shiraz University, University of Isfahan and Universidade Nova de Lisboa for the facilities provided. Publisher Copyright: © 2023 The Author(s)
publishDate 2023
dc.date.none.fl_str_mv 2023-10-15
2023-10-15T00:00:00Z
2024-02-22T23:51:47Z
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/10362/163970
url http://hdl.handle.net/10362/163970
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0167-7322
PURE: 83887859
https://doi.org/10.1016/j.molliq.2023.122747
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 11
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
institution RCAAP
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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