# Exploring new useful phosphors by combining experiments with machine learning

https://mdr.nims.go.jp/datasets/5e14f87c-6152-45c6-a708-051a7472412c

## File

- [2024_Exploring new useful phosphors by combining experiments with machine learning.pdf](https://mdr.nims.go.jp/filesets/ac9d3a18-2ad2-4bea-90f6-ab99407a7e7a/download) ([Detail](https://mdr.nims.go.jp/filesets/ac9d3a18-2ad2-4bea-90f6-ab99407a7e7a.md))

## Id

5e14f87c-6152-45c6-a708-051a7472412c

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-12-04T01:54:45.490504Z

## Updated at

2024-12-05T07:30:28.753218Z

## Published at

2024-12-05T07:30:30.557726Z

## Doi



## First published url

https://doi.org/10.1080/14686996.2024.2421761

## Date published

2024-12-31

## Recorded date published

2024-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Exploring new useful phosphors by combining experiments with machine learning
  title_type: original
  lang: en

## Description

- description: "New phosphor has been consistently demanded for advances in solid
    state lighting and display. Conventional exploring experiment with trial and error
    for new phosphor take much time. If a phosphor host suitable to the target luminescent
    property can be proposed from computational science, the\r\ndevelopment speed
    of new phosphors will significantly increase, and unexpected/overlooked compositions
    can be proposed in the candidates. In this paper, as more practical approach for
    developing new phosphor having target luminescent property, the combination of
    experiment and machine learning is reviewed on the topics of emission wavelength,
    full width at half maximum (FWHM) of emission spectrum, temperature dependence
    of emission spectrum (thermal quenching), new phosphor with new  chemical composition/new
    crystal structure, and high-throughput experiment."
  description_type: abstract
  lang: und

## Creator

- name: Takashi Takeda
  role: author
  orcid: https://orcid.org/0000-0003-2510-4562
- name: Yukinori Koyama
  role: author
  orcid: https://orcid.org/0000-0002-7090-4430
- name: Hidekazu Ikeno
  role: author
- name: Satoru Matsuishi
  role: author
  orcid: https://orcid.org/0000-0001-8905-0255
- name: Naoto Hirosaki
  role: author
  orcid: https://orcid.org/0000-0001-9218-9557

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: Phosphor
  schema: not_defined
- subject: High-throughput experiment
  schema: not_defined
- subject: Machine learning
  schema: not_defined
- subject: Local structure
  schema: not_defined
- subject: Europium
  schema: not_defined

## Rights

- description: This is an Open Access article distributed under the terms of the Creative
    Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which
    permits unrestricted use, distribution, and reproduction in any medium, provided
    the original work is properly cited. The terms on which this article has been
    published allow the posting of the Accepted Manuscript in a repository by the
    author(s) or with their consent.
  identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Science and Technology of Advanced Materials
  issn: '14686996'
  volume: '25'
  issue: '1'

## Conference



## Related item



## Funding

- identifier: JPMJCR19J2
  funder_name: JST
  description: CREST

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



## Energy level/transition state



## Software



## Custom property



## Fileset

- id: ac9d3a18-2ad2-4bea-90f6-ab99407a7e7a
  filename: 2024_Exploring new useful phosphors by combining experiments with machine
    learning.pdf
  content_type: application/pdf
  size: 11434034
  md5: c5bac70826306f33258f716aebb9c54f

## Thumbnail

fileset_id: ac9d3a18-2ad2-4bea-90f6-ab99407a7e7a
filename: 2024_Exploring new useful phosphors by combining experiments with machine
  learning.pdf