Refractive-index Prediction for High-refractive-index Optical Glasses Based on the B₂O₃-La₂O₃-Ta₂O₅-SiO₂ System Using Machine Learning

• Published in: Current optics and photonics 2024, 8(3), , pp.230-238
• Main Authors: Hong, Seok Jin, Lee, Jung Hee, Gelija, Devarajulu, Chung, Woon Jin
• Format: Article
• Language: English
• Subjects: Algorithms; Boron oxides; Composition; Design parameters; Gadolinium oxides; Glass; Heavy metal oxides; Lanthanum oxides; Machine learning; Niobium oxides; Refractivity; Silicon dioxide; Tantalum; Tantalum oxides; 물리학
• ISSN: 2508-7266; 2508-7274
• DOI: 10.3807/COPP.2024.8.3.230

The refractive index is a key material-design parameter, especially for high-refractive-index glasses, which are used for precision optics and devices. Increased demand for high-precision optical lenses produced by the glass-mold-press (GMP) process has spurred extensive studies of proper glass materials. B2O3, SiO2, and multiple heavy-metal oxides such as Ta2O5, Nb2O5, La2O3, and Gd2O3 mostly compose the high-refractive-index glasses for GMP. However, due to many oxides including up to 10 components, it is hard to predict the refractivity solely from the composition of the glass. In this study, the refractive index of optical glasses based on the B2O3-La2O3-Ta2O5-SiO2 system is predicted using machine learning (ML) and compared to experimental data. A dataset comprising up to 271 glasses with 10 components is collected and used for training. Various ML algorithms (linear-regression, Bayesian-ridge-regression, nearest-neighbor, and random-forest models) are employed to train the data. Along with composition, the polarizability and density of the glasses are also considered independent parameters to predict the refractive index. After obtaining the best-fitting model by R2 value, the trained model is examined alongside the experimentally obtained refractive indices of B2O3-La2O3-Ta2O5-SiO2 quaternary glasses.