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

2023-10-15Artigo científico Acesso aberto
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dc.contributor.authorRoosta, Ahmadreza
dc.contributor.authorHaghbakhsh, Reza
dc.contributor.authorRita C. Duarte, Ana
dc.contributor.authorRaeissi, Sona
dc.contributor.institutionLAQV@REQUIMTE
dc.contributor.institutionDQ - Departamento de Química
dc.contributor.pblElsevier
dc.date.accessioned2024-02-22T23:51:47Z
dc.date.available2024-02-22T23:51:47Z
dc.date.issued2023-10-15
dc.descriptionFunding 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)
dc.description.abstractIn 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent11
dc.format.extent1835480
dc.identifier.doi10.1016/j.molliq.2023.122747
dc.identifier.issn0167-7322
dc.identifier.otherPURE: 83887859
dc.identifier.otherPURE UUID: 8f21202c-abf1-4626-97bb-f49c5af42068
dc.identifier.otherScopus: 85167835362
dc.identifier.otherWOS: 001057839100001
dc.identifier.otherORCID: /0000-0003-0800-0112/work/153836869
dc.identifier.urihttp://hdl.handle.net/10362/163970
dc.identifier.urlhttps://www.scopus.com/pages/publications/85167835362
dc.language.isoeng
dc.peerreviewedyes
dc.subjectArtificial neural network
dc.subjectDES
dc.subjectMachine learning
dc.subjectPhysical property
dc.subjectSupport vector machine
dc.subjectElectronic, Optical and Magnetic Materials
dc.subjectAtomic and Molecular Physics, and Optics
dc.subjectCondensed Matter Physics
dc.subjectSpectroscopy
dc.subjectPhysical and Theoretical Chemistry
dc.subjectMaterials Chemistry
dc.titleDeep eutectic solvent viscosity prediction by hybrid machine learning and group contributionen
dc.typejournal article
degois.publication.titleJournal of Molecular Liquids
degois.publication.volume388
dspace.entity.typePublication
rcaap.rightsopenAccess

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