Comparison of experimental measurements and machine learning predictions of dielectric constant of liquid crystals

Basit Öğe Kaydı
dc.contributor.authorTaşer, Pelin Yıldırım
dc.contributor.authorÖnsal, Gülnur
dc.contributor.authorUğurlu, Onur
dc.date.accessioned2023-03-22T19:47:23Z
dc.date.available2023-03-22T19:47:23Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractIn this study, we investigated the dielectric properties of the phthalocyanine (Pc)-doped nematic liquid crystal (NLC) composite structures. 4-Pentyl-4 & PRIME;-cyanobiphenyl (5CB) NLC was dispersed with 1 and 3% wt/wt Pc to investigate the doping concentration effect. Dielectric measurements of the samples were carried out using the dielectric spectroscopy method. Moreover, the real and imaginary components of the dielectric constant values were estimated based on the input parameters (frequency, voltage value and dispersion rate) using two different traditional regression algorithms (k-Nearest Neighbor and Decision Tree Regression) and five different ensemble-based regression algorithms (Extreme Gradient Boosting, Random Forest, Extra Tree Regression, Voting and Bagging using k-Nearest Neighbor as a base learner). According to the obtained results, the Extra Tree Regression algorithm had the best prediction performance on real and imaginary components of the dielectric constant values. Moreover, it is seen from the obtained results that the ensemble-based regression algorithms are more successful than the traditional ones.en_US
dc.identifier.doi10.1007/s12034-022-02837-8
dc.identifier.issn0250-4707
dc.identifier.issn0973-7669
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85144611741en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1007/s12034-022-02837-8
dc.identifier.urihttps://hdl.handle.net/20.500.14034/666
dc.identifier.volume46en_US
dc.identifier.wosWOS:000903203400001en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIndian Acad Sciencesen_US
dc.relation.journalBulletin Of Materials Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLiquid crystalen_US
dc.subjectdielectric propertiesen_US
dc.subjectmachine learningen_US
dc.subjectElectrical-Propertiesen_US
dc.subjectNanoparticlesen_US
dc.subjectConductivityen_US
dc.subjectRegressionen_US
dc.subjectAnisotropyen_US
dc.subjectModelen_US
dc.titleComparison of experimental measurements and machine learning predictions of dielectric constant of liquid crystalsen_US
dc.typeArticleen_US

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