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説明:

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.

権利情報:

• 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.

キーワード: phosphor, machine learning, europium, oxidation state

刊行年月日: 2024-12-10

出版者: American Chemical Society (ACS)

掲載誌:

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

研究助成金:

• JST JPMJCR19J2 (CREST)

原稿種別: 著者最終稿 (Accepted manuscript)

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

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

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更新時刻: 2025-11-25 08:30:03 +0900

MDRでの公開時刻: 2025-11-25 08:21:34 +0900