Abstract
Eight glasses with chemical compositions (50-x)B2O3–28SrF2–22Li2O-xCeO2-xYb2O3 (x = 0 (CY0), 0.025 (CY1), 0.05 (CY2), 0.075 (CY3), 0.10 (CY4), 0.20 (CY5), 0.50 (CY6), and 1 (CY7) mol%) were synthesized using the melt quenching process. Density of the prepared glass was measured experimentally by Archimedes' principle. It was varied from 2.4582 g cm−3 to 4.6587 g cm−3. The density prediction based on the chemical composition of the glass and experimental density as inputs for various machine learning algorithms has been investigated. The Polynomial regression successfully fit the glass data and best density prediction obtained at 10-degree polynomial with R2 values 0.8726. The Artificial neural network also predicted the glass data using different activation functions and best density prediction obtained for tanh activation function with R2 = 0.8740 which a best regression values compare to polynomial fit. The Random forest regression (RFR) predicted the best density prediction compare to other Artificial intelligence models and predicted values are very close the experimental values with R2 = 0.985 which is best fit for the glass data.
•Newly glasses with chemical compositions B2O3–SrF2–Li2O–CeO2-Yb2O3 have been synthesized.•Physical characteristics have been investigated.•Artificial intelligence (AI) density models are applied.