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

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 ensemble-based regression algorithms are more successful than the traditional ones.