Advanced Machine Learning Methods for the Prediction of the Optical Parameters of Tellurite Glasses

Fahimeh Ahmadi, Mohsen Hajihassani, Tryfon Sivenas, Stefanos Papanikolaou, Panagiotis G. Asteris

This study evaluates the predictive performance of advanced machine learning models, including DeepBoost, XGBoost, CatBoost, RF, and MLP, in estimating the Ω2, Ω4, and Ω6 parameters based on a comprehensive set of input variables. Among the models, DeepBoost consistently demonstrated the best performance across the training and testing phases. For the Ω2 prediction, DeepBoost achieved an R2 of 0.974 and accuracy of 99.895% in the training phase, with corresponding values of 0.971 and 99.902% in the testing phase. In comparison, XGBoost ranked second with an R2 of 0.929 and accuracy of 99.870% during testing. For Ω4, DeepBoost achieved a training phase R2 of 0.955 and accuracy of 99.846%, while the testing phase results included an R2 of 0.945 and accuracy of 99.951%. Similar trends were observed for Ω6, where DeepBoost obtained near-perfect training phase results (R2 = 0.997, accuracy = 99.968%) and testing phase performance (R2 = 0.994, accuracy = 99.946%). These findings are further supported by violin plots and correlation analyses, underscoring DeepBoost’s superior predictive reliability and generalization capabilities. This work highlights the importance of model selection in predictive tasks and demonstrates the potential of machine learning for capturing complex relationships in data.