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

  • Seok Jin Hong (Institute for Rare Metals and Division of Advanced Materials Engineering, Kongju National University) ;
  • Jung Hee Lee (Taihan Fiber Optics Co.) ;
  • Devarajulu Gelija (Institute for Rare Metals and Division of Advanced Materials Engineering, Kongju National University) ;
  • Woon Jin Chung (Institute for Rare Metals and Division of Advanced Materials Engineering, Kongju National University)
  • 투고 : 2024.03.12
  • 심사 : 2024.04.22
  • 발행 : 2024.06.25

초록

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.

키워드

과제정보

The authors are grateful to the Technology Innovation Program named High Refractive Optical Glass for GMP (Grant no. 20011325), and Korea Evaluation Institute of Industrial Technology (KEIT) (Grant no. G012001132504). This work was also supported by Korea Institute for Advancement of Technology (KIAT) grant (Grant no. G02P13780002112) funded by the Korea Government (MOTIE) Human Resource Development Program for Industrial Innovation (Grant no. P0017012).

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