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dc.contributor.authorVenkatraman, Vishwesh
dc.contributor.authorEvjen, Sigvart
dc.contributor.authorKnuutila, Hanna K
dc.contributor.authorFiksdahl, Anne
dc.contributor.authorAlsberg, Bjørn Kåre
dc.date.accessioned2019-04-04T10:20:48Z
dc.date.available2019-04-04T10:20:48Z
dc.date.created2018-04-03T13:28:19Z
dc.date.issued2018
dc.identifier.citationJournal of Molecular Liquids. 2018, 264 318-326.nb_NO
dc.identifier.issn0167-7322
dc.identifier.urihttp://hdl.handle.net/11250/2593266
dc.description.abstractThe melting point (Tm) of an ionic liquid (IL) is of crucial importance in many applications. The Tm can vary considerably depending on the choice of the anion and cation. This study explores the use of various machine learning (ML) methods to predict the melting points (− 96 °C–359 °C range) of structurally diverse 2212 ILs based on a combination of 1369 cations and 141 anions. Among the ML models applied to independent training and test sets, tree-based ensemble methods (Cubist, random forest and gradient boosted regression) were found to demonstrate slightly better performance over support vector machines and k-nearest neighbour approaches. In comparison, quantum chemistry based COSMOtherm predictions were generally found to have significant deviations with respect to the experimental values. However, classification models were more efficient in discriminating between ILs with Tm > 100 °C and those below 100 °C.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titlePredicting ionic liquid melting points using machine learningnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber318-326nb_NO
dc.source.volume264nb_NO
dc.source.journalJournal of Molecular Liquidsnb_NO
dc.identifier.doi10.1016/j.molliq.2018.03.090
dc.identifier.cristin1576848
dc.relation.projectNorges forskningsråd: 233776nb_NO
dc.description.localcode© 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 29.3.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,66,25,0
cristin.unitcode194,66,30,0
cristin.unitnameInstitutt for kjemi
cristin.unitnameInstitutt for kjemisk prosessteknologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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