On cobalt zinc boro sodium fluoride glasses doped with Y2O3: Synthesis, artificial intelligence density prediction and dielectric spectroscopy

Abstract

Artificial intelligence density prediction and dielectric spectroscopy of five glass samples with chemical compositions 64.9B2O3-25NaF-10ZnO-0.1CoO-xY2O3: x = 0.0 (Y0.0), 0.2 (Y0.2), 0.4 (Y0.4), 0.8 (Y0.8), and 1.0 (Y1.0) mol% have been investigated. Fabrication process has been achieved by melting quenching method at melting temperature 1050–1100 °C. Density of the Y0.0-Y1.0 samples experimentally was measured and varied from 2.945 to 3.016 g/cm3. The chemical composition of the prepared glasses successfully mapped with various machine learning (ML) algorithms to predict densities using 10,000 glass data set. The density prediction done by various machine learning (ML) techniques like the logistic regression, linear regression, and decision tree (DT), random forest regression (RFR), and artificial neural network (ANN). A decision tree algorithm being fitted to the training set, estimating the test outcome utilizing the confusion matrix. The accuracy of 78.261 as well as a classification error equal 0.392 obtained for our data set which was best values to predict density for various ML techniques. The logistic regression also predicting successfully the glass data with R2 0.850 compare to linear regression. The random forest regression dominating in density prediction in current study with maximum R2 values 0.987. In addition, the dielectric spectroscopy of the prepared Y0.0-Y1.0 glasses is examined through various frequencies at room temperature (RT) to investigate the structure changes by yttrium doping on dielectric spectroscopy items. Results revealed that the insertion of yttrium ion causes an enhancement in dielectric constant and the dependency of dielectric loss (tan δ) on applied frequency has relaxation peaks at around 700 and 7000 Hz.