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

      PELIN YILDIRIM TASER GULNUR ONSAL ONUR UGURLU

      Volume 46 Published: 23 December 2022 Article ID 0001

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      https://www.ias.ac.in/article/fulltext/boms/046/0001

    • Keywords

       

      Liquid crystal; dielectric properties; machine learning.

    • Abstract

       

      In this study, we investigated the dielectric properties of the phthalocyanine (Pc)-doped nematic liquid crystal (NLC) composite structures. 4-Pentyl-4'-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 ensemblebased regression algorithms are more successful than the traditional ones.

    • Author Affiliations

       

      PELIN YILDIRIM TASER1 GULNUR ONSAL2 ONUR UGURLU2

      1. Department of Computer Engineering, Izmir Bakircay University, Izmir 35665, Turkey
      2. Department of Fundamental Sciences, Izmir Bakircay University, Izmir 35665, Turkey

    • Dates

       
      Published
      23 December 2022
      Manuscript received
      6 April 2022
      Accepted
      16 August 2022
      Final version published online
      23 December 2022

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