Implementation of Machine Learning Classification Technique for Detecting the Thermo-mechanical Properties of Chalogenide Glass Datasets

Chalcogenide glasses are based on the chalcogen elements S, Se, and Te. These glasses are formed by the addition of other elements such as Ge, As, Sb, Ga, etc. These glasses are low-phonon-energy materials and are generally transparent from the visible up to infrared. Chalcogenide glasses can be doped by rare-earth elements, such as Er, Nd, Pr, etc., and hence numerous applications of active optical devices have been proposed. These glasses are optically highly non-linear and could therefore be useful for all-optical switching. Chalcogenide glasses are sensitive to the absorption of electromagnetic radiation and show a variety of photoinduced effects as a result of illumination. The proposed paper presents an artificial intelligence approach in determining the thermo-mechanical properties of Chalogenide glass datasets. K-Nearest Neighbor (KNN) machine learning classification technique is used for the prediction purpose. The RMSE of values 13.88 and R2 Score is predicted as 0.81.