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dc.contributor.authorTranås, Rasmus André
dc.contributor.authorLøvvik, Ole Martin
dc.contributor.authorTomic, Oliver
dc.contributor.authorBerland, Kristian
dc.date.accessioned2022-05-03T08:35:41Z
dc.date.available2022-05-03T08:35:41Z
dc.date.created2021-11-02T09:25:00Z
dc.date.issued2021
dc.identifier.citationComputational Materials Science. 2021, 202, 110938, .en_US
dc.identifier.issn0927-0256
dc.identifier.urihttps://hdl.handle.net/11250/2993784
dc.description.abstractLow lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed using density functional theory (DFT), typically at a high computational cost. Training machine learning models to predict lattice thermal conductivity could offer an effective procedure to identify low lattice thermal conductivity compounds. However, in doing so, we must face the fact that such compounds can be quite rare and distinct from those in a typical training set. This distinctness can be problematic as standard machine learning methods are inaccurate when predicting properties of compounds with features differing significantly from those in the training set. By computing the lattice thermal conductivity of 122 half-Heusler compounds, using the temperature-dependent effective potential method, we generate a data set to explore this issue. We first show how random forest regression can fail to identify low lattice thermal conductivity compounds with random selection of training data. Next, we show how active selection of training data using feature and principal component analysis can be used to improve model performance and the ability to identify low lattice thermal conductivity compounds. Lastly, we find that active learning without the use of DFT-based features can be viable as a quicker way of selecting samples.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectHalf-Heusler compoundsen_US
dc.subjectPrincipal component analysisen_US
dc.subjectMachine learningen_US
dc.subjectDensity functional theoryen_US
dc.subjectLattice thermal conductivityen_US
dc.titleLattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysisen_US
dc.title.alternativeLattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysisen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionsubmittedVersionen_US
dc.source.pagenumber9en_US
dc.source.volume202en_US
dc.source.journalComputational Materials Scienceen_US
dc.identifier.doi10.1016/j.commatsci.2021.110938
dc.identifier.cristin1950469
dc.relation.projectNorges forskningsråd: 314778en_US
dc.source.articlenumber110938en_US
cristin.ispublishedtrue
cristin.fulltextpreprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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