Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences

2024/12/12 by Yukinori Koyama, Yukako Kohriki, Masamichi Harada +2
Materials Science · Engineering · #Machine Learning in Materials Science #Lanthanide and Transition Metal Complexes #Advanced Chemical Sensor Technologies

paper · doi:10.48505/nims.5148

Abstract

An approach is presented to accelerate the discovery of host compounds for novel Eu2+-activated phosphor materials by integrating systematic data collection, machine learning, and experimental validation. A data set of Eu2+- and Eu3+-activated phosphors has been constructed using systematic data collection methodology from numerous academic articles. A machine-learning classification model has been developed using the collected data set to predict the oxidation states of Eu ions in potential hosts regarding luminescence. The model considers the nonexclusive nature of the divalent and trivalent oxidation states of Eu ions in phosphor applications. A comprehensive exploration of a materials database was conducted to identify host candidates for novel Eu2+-activated phosphor materials, leading to attempts to synthesize them. Photoluminescence analysis revealed the successful synthesis of 12 new Eu2+-activated phosphors, demonstrating the potential of the proposed approach for accelerating material discovery.

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