Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach. Issue 1 (29th November 2022)
- Record Type:
- Journal Article
- Title:
- Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach. Issue 1 (29th November 2022)
- Main Title:
- Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach
- Authors:
- Koyama, Yukinori
Ikeno, Hidekazu
Harada, Masamichi
Funahashi, Shiro
Takeda, Takashi
Hirosaki, Naoto - Abstract:
- Abstract : Machine learning in conjunction with validation experiments uncovers new Eu 2+ -activated phosphor materials with a designed green-color luminescence. Abstract : 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 Eu 2+ -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 Eu 2+ -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 Eu 2+ -activated phosphors, Li2 Ca4 Si4 O13 :Eu 2+, Na2 Ca2 Si2 O7 :Eu 2+, and SrLaGaO4 :Eu 2+, successfully showAbstract : Machine learning in conjunction with validation experiments uncovers new Eu 2+ -activated phosphor materials with a designed green-color luminescence. Abstract : 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 Eu 2+ -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 Eu 2+ -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 Eu 2+ -activated phosphors, Li2 Ca4 Si4 O13 :Eu 2+, Na2 Ca2 Si2 O7 :Eu 2+, and SrLaGaO4 :Eu 2+, successfully show green or blue-green emissions as designed. … (more)
- Is Part Of:
- Materials advances. Volume 4:Issue 1(2023)
- Journal:
- Materials advances
- Issue:
- Volume 4:Issue 1(2023)
- Issue Display:
- Volume 4, Issue 1 (2023)
- Year:
- 2023
- Volume:
- 4
- Issue:
- 1
- Issue Sort Value:
- 2023-0004-0001-0000
- Page Start:
- 231
- Page End:
- 239
- Publication Date:
- 2022-11-29
- Subjects:
- 620.11
- Journal URLs:
- https://pubs.rsc.org/en/journals/journalissues/ma#!issueid=ma001002&type=current&issnonline=2633-5409 ↗
http://www.rsc.org/ ↗ - DOI:
- 10.1039/d2ma00881e ↗
- Languages:
- English
- ISSNs:
- 2633-5409
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital Store - Ingest File:
- 25184.xml