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For rapid and efficient development of new phosphors, a suitable method that proposes promising candidates is expected to focus time-consuming trial-and-error experiments. A data-driven approach to discover new phosphor materials with a designed luminescence color is demonstrated in this paper. To screen compounds for a desirable luminescence color, a machine learning model has been developed for predicting emission peak wavelengths from a dataset composed of 129 Eu2+-activated phosphors. General-purpose compositional and structural features are used to represent host compounds of phosphors. Bootstrap aggregation with the gradient boosted regression trees method is adopted to obtain high predictive performance and to avoid overfitting. The predictive performance of the machine learning model is estimated to be 25 nm of mean absolute error (MAE) and 33 nm of root mean squared error (RMSE) by 10-fold cross validation. To discover new green-emitting Eu2+-activated phosphors, twenty candidate compounds have been selected to have predicted emission peak wavelengths of about 500–550 nm from a materials database, and the candidates have been synthesized and characterized by experiments. Three new Eu2+-activated phosphors, Li2Ca4Si4O13:Eu2+, Na2Ca2Si2O7:Eu2+, and SrLaGaO4:Eu2+, successfully show green or blue-green emissions as designed.

権利情報:

• Creative Commons BY Attribution 4.0 International
  Creative Commons BY Attribution 4.0 International

キーワード: phosphor, materials design, machine learning, luminescence, emission spectrum, Eu2+

刊行年月日: 2022-11-29

出版者: Royal Society of Chemistry

掲載誌:

• Materials Advances (ISSN: 26335409) vol. 4 issue. 1 p. 231-239

研究助成金:

• JST JPMJCR19J2 (CREST)

原稿種別: 出版者版 (Version of record)

MDR DOI: https://doi.org/10.48505/nims.3845

公開URL: https://doi.org/10.1039/D2MA00881E

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更新時刻: 2024-01-05 22:11:23 +0900

MDRでの公開時刻: 2023-01-20 10:13:46 +0900