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

https://mdr.nims.go.jp/datasets/6ffc8a71-859d-4299-af90-08cf6a49cc7a

## File

- [manuscript.docx](https://mdr.nims.go.jp/filesets/9fbced2c-d34a-4bc2-82f4-8065910a2b43/download) ([Detail](https://mdr.nims.go.jp/filesets/9fbced2c-d34a-4bc2-82f4-8065910a2b43.md))
- [supporting-information.pdf](https://mdr.nims.go.jp/filesets/3a16cf5d-e2dc-4835-bd8a-e8f163deb852/download) ([Detail](https://mdr.nims.go.jp/filesets/3a16cf5d-e2dc-4835-bd8a-e8f163deb852.md))

## Id

6ffc8a71-859d-4299-af90-08cf6a49cc7a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-12T07:13:22.955695Z

## Updated at

2025-11-24T23:30:03.860563Z

## Published at

2025-11-24T23:21:34.836604Z

## Doi

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

## First published url

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

## Date published

2024-12-10

## Recorded date published

2024-12-10

## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors
    through Machine Learning Classification of Europium Valences
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: und

## Creator

- name: Yukinori Koyama
  role: author
  orcid: https://orcid.org/0000-0002-7090-4430
- name: Yukako Kohriki
  role: author
  orcid: https://orcid.org/0000-0002-6858-1273
- name: Masamichi Harada
  role: author
  orcid: https://orcid.org/0000-0002-7321-0733
- name: Naoto Hirosaki
  role: author
  orcid: https://orcid.org/0000-0001-9218-9557
- name: Takashi Takeda
  role: author
  orcid: https://orcid.org/0000-0003-2510-4562

## Contact agent



## Publisher

organization: American Chemical Society (ACS)

## Managing organization



## Keyword

- subject: phosphor
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: europium
  schema: not_defined
- subject: oxidation state
  schema: not_defined

## Rights

- description: 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.
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2024-11-25
end_date: 2025-11-25

## Journal

- title: Chemistry of Materials
  issn: '08974756'
  volume: '36'
  issue: '23'
  start_page: 11412
  end_page: 11420

## Conference



## Related item



## Funding

- identifier: JPMJCR19J2
  funder_name: JST
  description: CREST

## Instrument



## Instrument operator



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Process for specimen treatment



## Computational method



## Energy level/transition state



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## Fileset

- id: 9fbced2c-d34a-4bc2-82f4-8065910a2b43
  filename: manuscript.docx
  content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document
  size: 618121
  md5: 6073c3f5af054f151a55fad9e90d535c
- id: 3a16cf5d-e2dc-4835-bd8a-e8f163deb852
  filename: supporting-information.pdf
  content_type: application/pdf
  size: 440772
  md5: e2b972495a6f77f1bb02004c7144501a

## Thumbnail

fileset_id: 9fbced2c-d34a-4bc2-82f4-8065910a2b43
filename: manuscript.docx