Machine Learning-Driven Characterization of Optical Materials: Predicting JO Parameters in Rare-Earth Doped Glasses

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Machine Learning-Driven Characterization of Optical Materials: Predicting JO Parameters in Rare-Earth Doped Glasses

This paper presents a machine learning-driven approach for predicting the spectroscopic properties of rare-earth (RE) doped glass systems, with a focus on Dy3+ ions. Glass compositions of 0.25 PbO–0.2 SiO2–(0.55−x) B2O3–x Dy2O3 were synthesized using the melt-quenching technique, and their density,...

書誌詳細
出版年:	Chemical Review and Letters
第一著者:	Singh J.P.; Newaz A.A.H.; Shirke M.B.; Humanante P.M.T.; Lee M.D.; Pandey V.K.
フォーマット:	論文
言語:	English
出版事項:	Iranian Chemical Science and Technologies Association 2025
オンライン･アクセス:	https://www.scopus.com/inward/record.uri?eid=2-s2.0-85215939814&doi=10.22034%2fcrl.2024.488643.1474&partnerID=40&md5=b85ab45849a8633605d3963ea72ce0cf

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