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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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• In Copyright
  

  This document is the Accepted Manuscript version of a Published Work that appeared in final form in Chemistry of Materials, copyright © 2024 American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.chemmater.4c01981.

Keyword: phosphor, machine learning, europium, oxidation state

Date published: 2024-12-10

Publisher: American Chemical Society (ACS)

Journal:

• Chemistry of Materials (ISSN: 08974756) vol. 36 issue. 23 p. 11412-11420

Funding:

• JST JPMJCR19J2 (CREST)

Manuscript type: Author's version (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5148

First published URL: https://doi.org/10.1021/acs.chemmater.4c01981

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Updated at: 2025-11-25 08:30:03 +0900

Published on MDR: 2025-11-25 08:21:34 +0900