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dc.contributor.authorTabib, Mandar
dc.contributor.authorLøvvik, Ole Martin
dc.contributor.authorJohannessen, Kjetil Andre
dc.contributor.authorRasheed, Adil
dc.contributor.authorSagvolden, Espen
dc.contributor.authorRustad, Anne Marthine
dc.date.accessioned2019-03-05T08:58:28Z
dc.date.available2019-03-05T08:58:28Z
dc.date.created2018-11-19T15:22:41Z
dc.date.issued2018
dc.identifier.citationLecture Notes in Computer Science. 2018, 11139 LNCS 392-401.nb_NO
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11250/2588680
dc.description.abstractThis work involves the use of combined forces of data-driven machine learning models and high fidelity density functional theory for the identification of new potential thermoelectric materials. The traditional method of thermoelectric material discovery from an almost limitless search space of chemical compounds involves expensive and time consuming experiments. In the current work, the density functional theory (DFT) simulations are used to compute the descriptors (features) and thermoelectric characteristics (labels) of a set of compounds. The DFT simulations are computationally very expensive and hence the database is not very exhaustive. With an anticipation that the important features can be learned by machine learning (ML) from the limited database and the knowledge could be used to predict the behavior of any new compound, the current work adds knowledge related to (a) understanding the impact of selection of influence of training/test data, (b) influence of complexity of ML algorithms, and (c) computational efficiency of combined DFT-ML methodology.nb_NO
dc.description.abstractDiscovering Thermoelectric Materials Using Machine Learning: Insights and Challengesnb_NO
dc.language.isoengnb_NO
dc.titleDiscovering Thermoelectric Materials Using Machine Learning: Insights and Challengesnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber392-401nb_NO
dc.source.volume11139 LNCSnb_NO
dc.source.journalLecture Notes in Computer Sciencenb_NO
dc.identifier.doi10.1007/978-3-030-01418-6_39
dc.identifier.cristin1632289
dc.relation.projectNorges forskningsråd: 194068nb_NO
dc.relation.projectNorges forskningsråd: nn2615knb_NO
cristin.unitcode7401,90,26,0
cristin.unitcode7401,80,62,0
cristin.unitnameMathematics and Cybernetics
cristin.unitnameBærekraftig energiteknologi
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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