Deep eutectic solvent viscosity prediction by hybrid machine learning and group contribution
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
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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7160 |
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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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1799138175904382976 |