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Prediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approach

2022-08-05Artigo científico Acesso aberto
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dc.contributor.authorCarrera, Gonçalo Valente da Silva Marino
dc.contributor.authorKlimenko, Kyrylo
dc.contributor.authorCruz, Mariana Lopes
dc.contributor.authorEsperanca, J.
dc.contributor.authorAires-de-Sousa, Joao
dc.contributor.institutionDQ - Departamento de Química
dc.contributor.institutionLAQV@REQUIMTE
dc.contributor.pblWiley
dc.date.accessioned2023-01-16T22:14:24Z
dc.date.available2023-08-06T00:31:16Z
dc.date.embargoedUntil2023-08-05
dc.date.issued2022-08-05
dc.description
dc.description.abstractMachine-learning models were developed to predict the composition profile of a three-compound mixture in liquid-liquid equilibrium (LLE), given the global composition at certain temperature and pressure. A chemoinformatics approach was explored, based on the MOLMAP technology to encode molecules and mixtures. The chemical systems involved an ionic liquid (IL) and two organic molecules. Two complementary models have been optimized for the IL-rich and IL-poor phases. The two global optimized models are highly accurate, and were validated with independent test sets, where combinations of molecule1+molecule2+IL are different from those in the training set. These results highlight the MOLMAP encoding scheme, based on atomic properties to train models that learn relationships between features of complex multi-component chemical systems and their profile of phase compositions.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent10
dc.format.extent647269
dc.identifier.doi10.1002/cphc.202200300
dc.identifier.issn1439-4235
dc.identifier.otherPURE: 46583535
dc.identifier.otherPURE UUID: 44a737e7-dc21-4145-bd1b-ccf312cf3fbb
dc.identifier.otherScopus: 85137366281
dc.identifier.otherWOS: 000859198300001
dc.identifier.otherORCID: /0000-0001-9615-8678/work/125847261
dc.identifier.urihttp://hdl.handle.net/10362/147671
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEQU-EQU%2F30060%2F2017/PT
dc.relationARTificial INTELligence applied in PHASe EQuilibrium composition
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.relationAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
dc.subjectbig data
dc.subjectchemoinformatics
dc.subjectcodification
dc.subjectionic liquid
dc.subjectphase behaviour
dc.titlePrediction of the Phase Composition Profile of Three-Compound Mixtures in Liquid-Liquid Equilibrium: A Chemoinformatics Approachen
dc.typejournal article
degois.publication.issue24
degois.publication.titleChemPhysChem
degois.publication.volume23
dspace.entity.typePublication
oaire.awardNumberPTDC/EQU-EQU/30060/2017
oaire.awardNumberUIDB/50006/2020
oaire.awardNumberUIDP/50006/2020
oaire.awardTitleARTificial INTELligence applied in PHASe EQuilibrium composition
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardTitleAssociated Laboratory for Green Chemistry - Clean Technologies and Processes
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FEQU-EQU%2F30060%2F2017/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50006%2F2020/PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication8cb95fb1-62f5-4836-8095-85fbdd8faaee
relation.isProjectOfPublicationadc84c24-ba1d-4bcd-b753-2128ce9a5faa
relation.isProjectOfPublication4d9a4d40-4803-4f3a-976b-d6eaaef42510
relation.isProjectOfPublication.latestForDiscovery4d9a4d40-4803-4f3a-976b-d6eaaef42510

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