Machine learning-driven development of niobium-containing optical glasses

Authors

DOI:

https://doi.org/10.33448/rsd-v11i9.31290

Keywords:

Optical Glass; Niobium; Refractive Index; Abbe number; Artificial intelligence; Machine learning.

Abstract

High refractive index glasses are essential for old and new optical systems, such as microscopes, telescopes and novel augmented reality lenses and micro projectors. However, a fair portion of these glasses use toxic components, such as PbO, BaO, As2O3, and TeO2, which lead to high refractive indexes and facilitate the melting operation, but are harmful for human beings and the environment. On the other hand, it is known that niobium significantly increases the refractive index and is a non-toxic element. The objective of this paper was to develop new optical glass compositions containing Nb2O5 with a relatively high refractive index (nd > 1.65), intermediate Abbe number (35 < Vd < 55) and fair glass transition temperature, Tg. To this end, we used a machine learning algorithm titled GLAS, which was recently developed at DEMA-UFSCar to produce new optical glasses composition. After running the algorithm 13 times, two of the most promising compositions were chosen and tested for their glass forming ability and other properties. The best composition was analyzed in respect to the refractive index, glass transition temperature and chemical durability. A comparison between the laboratory results and predictions of the artificial neural network indicates that the GLAS algorithm provides adequate formulations and can be immediately used for accelerating the design of new glasses, substantially reducing the laboratory testing effort. Also, the results indicate that niobium glasses might offer some advantages over its main competitor (La2O3).

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Published

05/07/2022

How to Cite

MENEZES, A. D.; TEIXEIRA, E. P. .; FINZER, J. R. D.; OLIVEIRA, R. B. de. Machine learning-driven development of niobium-containing optical glasses . Research, Society and Development, [S. l.], v. 11, n. 9, p. e13811931290, 2022. DOI: 10.33448/rsd-v11i9.31290. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/31290. Acesso em: 13 aug. 2022.

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Section

Engineerings