Kayumov Makhmadzoir Makhmarajabovich, Bozorov Shamsuddin Alomuddinovich, Ashurov Anushervon Sirojiddinovich

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This study presents a data analysis approach for classifying inorganic phosphores and identifying functional substitutes for rare earth elements (RE). Using a dataset of 3911 phosphorous materials, where machine learning methods were applied to classify materials and perform cluster analysis based on their optical properties. The main result was the identification of spectral regions in which activators without RE (Mn2⁺, Bi3⁺) exhibit excitation and emission profiles identical to traditional RE (Tb3⁺, Eu3⁺) dopants, confirming their potential as stable alternatives for solid-state lighting.

Keywords: inorganic phosphors, rare-earth elements, machine learning, cluster analysis, spectral overlap, solid-state lighting, functional substitutes.