Novel of neodymium nanoparticles zinc tellurite glasses in experimental and theoretical elastic properties using artificial intelligence approach

• Main Authors: Awshah, A. A. A., Nazrin, S. N., Effendy, N., Khaliq, M. A. S. M. S., Azlan, M. N., Awad, A. Ibraheem
• Format: Article
• Published: Elsevier 2023
• Subjects: Q Science (General); QC Physics
• Online Access: http://eprints.um.edu.my/38832/

All the parameters under experimental elastic properties showed non-linear variations for Nd2O3 nanoparticles. Rocherulle and Makishima-Mackenzie's model involving theoretical elastic was retrieved and compared, along with bond compression and ring deformation models and experimental elastic properties. The corresponding set of data indicates comparable values with Makishima-Mackenzie and Rocherulle models. Although, the experimental elastic moduli exhibit higher values from bond compression model data in comparison with similar experimental values. Therefore, this model is not suitable for this glass system. Predictions from the artificial neural network (ANN) model interpreted through the relationship between the predicted and the experimental values provide an excellent R2 coefficient, lying between 0.9916 to 1.0000 for all values. This great approach via artificial neural network model has proven its validity for future glass research.