Linear refractive index and density prediction of transparent B₂O₃-CaO-Li₂O glasses reinforced with Sb₂O₃ utilizing machine learning techniques

In the present study, for the first time the machine learning (ML) based refractive index (n) approach is established depends on the density (ρ) parameter of glasses for a dataset of 2000 oxide glasses to predict refractive index of B₂O₃-CaO-Li₂O-Sb₂O₃ glasses. Density of the investigated glasses varied from 2.56 to 2.97 gm/cm³. The corresponding refractive index was changed from 2.540 to 2.405. The refractive index prediction based on density parameter derived from the density of glasses and constant ‘K’. For all M-L techniques including gradient descent (GD), artificial neural network (ANN), and random forest regression (RFR), the density factor is used as an independent variable and the experimental refractive index as a dependent variable. The data set of 10,000 oxide glass samples was employed to forecast density using a variety of machine learning approaches. In comparison to other models, the Random forest regression (RFR) model fitted the glass data with the highest R² value of 0.949 for refractive index prediction and 0.925 for density prediction. For both the prediction of density and refractive index, the R² is controlled to 0.932 and 0.9223, respectively. The highest R² values for refractive index and density prediction were gained when the tanh activation function was used in an artificial neural network (ANN) with varied activation functions.