Bi4Sr3Ca3Cu4O16 galss and superconducting glass ceramics

Density of Bismuth Boro Zinc Glasses Using Machine Learning Techniques

Machine learning techniques have been employed to predict the glass densities of xBi₂O₃–(70 − x)B₂O₃–20Li₂O–5Sb₂O₃–5ZnO glasses using a data set of 2000 various B₂O₃ rich glasses using their chemical composition and ionic radius. The experimental density of present glasses strongly depends on Bi₂O₃ content which is increasing with bismuth content. The increasing density in bismuth doped glasses because the BO₃ are converted into BO₄ units, and besides BO₃ units are less heavy than the BO₄ units. The FTIR studies also confirm that the intensity of B–O–B bond decreasing with increasing Bi₂O₃ content which suggested that B–O–B bond in bond ring isolated to BO₃ units transformed into BO₄ units. In Raman Spectra the stretching vibrations of BO₄ units shifting towards higher wavelengths with increasing Bi₂O₃ content. This shifting conforms that there is a structural changes in the glass-matrix and borate units converting from BO₃ to BO₄ units. The prepared glasses along with B₂O₃ rich glass data set train on various AI model such as gradient descent, Random Forest regression and Neural Networks to predict present density of glasses. Among the various models RF regression analysis model is successfully acceptable for the glass data with the highest R² value 0.983 which end result conform that the predicted and experimental values correlated. ANNs stood the effective technique in prediction of glass density with the optimum performance resulting with Tanh as the activation function (R² = 0.950). The minimum cost 0.018 obtained in the case of gradient decent function which also shows the better performance of regression model.