Estimation of Dielectric Constant of Ni(II)Pc and CdSeS/ZnS QDs Dope Liquid Crystal Structures by Machine Learning Algorithms

Abstract

In this study, the dielectric properties of Ni(II)Pc (nickel(II)phthalocyanine) and CdSeS/ZnS (cadmium selenide sulfide/zinc sulfide) quantum dot-doped 5CB (4-pentyl-4′-cyanobiphenyl) nematic liquid crystal composite structures along with the prediction of these properties by machine learning algorithms are reported. In order to determine the effects of the concentration ratio on the dielectric properties, 2% and 5% wt/wt Ni(II)Pc and CdSeS/ZnS quantum dots were doped into the 5CB nematic liquid crystal. The dielectric measurements of the samples were carried out using the dielectric spectroscopy method. Moreover, k-Nearest Neighbor, Decision Tree and Random Forest algorithms were used for the estimation of the real (ε^') and imaginary components (ε^'') of the dielectric constant, and the prediction performances of the algorithms were examined comparatively. According to the results obtained, the best estimation performance of the dielectric constant was obtained with the Random Forest algorithm.  

Keywords

• Nematic liquid crystal; Dielectric properties
• Machine learning

Ni(II)Pc ve CdSeS/ZnS Kuantum Nokta Katkılı Sıvı Kristal Yapıların Dielektrik Sabitinin Makine Öğrenmesi Algoritmaları ile Tahminlenmesi

Abstract

Bu çalışmada, Ni(II)Pc (nikel(II)ftalosiyanin) ve CdSeS/ZnS (cadmium selenide sulfide/zinc sulfide) kuantum nokta katkılı 5CB (4-pentyl-4′-cyanobiphenyl) nematik sıvı kristal kompozit yapıların dielektrik özellikleri ile birlikte bu özelliklerin makine öğrenmesi algoritmaları ile tahminlenmesi rapor edilmektedir. Konsantrasyon oranının dielektrik özelliklere etkilerini saptamak için 5CB nematik sıvı kristal yapıya ağırlıkça %2 ve %5 oranında Ni(II)Pc ve CdSeS/ZnS kuantum nokta katkılanmıştır. Numunelerin dielektrik ölçümleri, dielektrik spektroskopi yöntemi kullanılarak gerçekleştirilmiştir. Ayrıca, dielektrik sabitinin reel (ε^') ve sanal bileşenlerinin (ε^'') tahmini için k-En Yakın Komşu, Karar Ağacı, Rastgele Orman algoritmaları kullanmış ve algoritmaların tahmin performansları karşılaştırmalı olarak incelenmiştir. Algoritmalarda girdi parametreleri frekans, voltaj ve katkı oranı; çıktı parametreleri ise, dielektrik sabitinin reel (ε^') ve sanal bileşenleri (ε^'') olarak belirlenmiştir. Elde edilen sonuçlara göre dielektrik sabitinin en iyi tahmin performansına Rastgele Orman algoritması ile ulaşılmıştır. 

Keywords

• Nematik sıvı kristal
• Dielektrik özellikler
• Makine öğrenmesi
• Nematic liquid crystal; Dielectric properties
• Machine learning

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