# An interpretable linear model bridging data-driven analysis and chemical intuition for Eu                    <sup>2+</sup>                    -phosphor emissions

https://mdr.nims.go.jp/datasets/1d1a663a-050f-4dda-971e-3e016c46ee51

## File

- [An interpretable linear model bridging data-driven analysis and chemical intuition for Eu2 -phosphor emissions.pdf](https://mdr.nims.go.jp/filesets/ea74ce67-b7ce-46b1-a1ae-fab2610a9278/download) ([Detail](https://mdr.nims.go.jp/filesets/ea74ce67-b7ce-46b1-a1ae-fab2610a9278.md))
- [tstm_a_2691688_sm0813.csv](https://mdr.nims.go.jp/filesets/66ac811d-d8ee-433a-ab8c-06632e77932d/download) ([Detail](https://mdr.nims.go.jp/filesets/66ac811d-d8ee-433a-ab8c-06632e77932d.md))
- [tstm_a_2691688_sm0812.pdf](https://mdr.nims.go.jp/filesets/01190d12-6b92-44e7-9c9e-51ec7c8528c2/download) ([Detail](https://mdr.nims.go.jp/filesets/01190d12-6b92-44e7-9c9e-51ec7c8528c2.md))

## Id

1d1a663a-050f-4dda-971e-3e016c46ee51

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-07-07T05:27:23.692133Z

## Updated at

2026-07-07T05:31:04.177660Z

## Published at

2026-07-07T09:24:09.658541Z

## Doi



## First published url

https://doi.org/10.1080/27660400.2026.2691688

## Date published

2026-12-31

## Recorded date published

2026-12-31

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: |-
    An interpretable linear model bridging data-driven analysis and chemical intuition for Eu
                        <sup>2+</sup>
                        -phosphor emissions
  title_type: original
  lang: en

## Description

- description: Recent advances in phosphor informatics have achieved high predictive
    accuracy with complex machine learning models. However, this often comes at the
    cost of physical interpretability, creating an interpretability dilemma. To address
    this challenge, we propose an approach that prioritizes model interpretability.
    We constructed a simple linear model (ridge regression) for a curated dataset
    of 118 Eu2+-activated phosphors, using only the chemical composition (atomic fractions)
    as features. This approach enabled us to successfully quantify the contribution
    of each constituent element to the peak emission wavelength as a physically interpretable
    "elemental contribution coefficient" (ECC). The trends derived from the ECCs agree
    remarkably with fundamental chemical intuitions in phosphor chemistry and with
    established empirical physical rules, thereby demonstrating the scientific validity
    of our model. Furthermore, analysis of the systematic discrepancies revealed that
    a principal limitation of the model is its inability to decouple the competing
    physical effects of the centroid shift and crystal field splitting. This study
    demonstrates a pathway to elevate machine learning from a mere predictor to an
    analytical tool. Such a tool can interpret underlying scientific relationships
    in data and deepen our understanding of science. This approach bridges the gap
    between data-driven science and materials science.
  description_type: abstract
  lang: und

## Creator

- name: Yukinori Koyama
  role: author
  orcid: https://orcid.org/0000-0002-7090-4430
- name: Ryusei Hayasaka
  role: author
- name: Yuta Matsushima
  role: author
  orcid: https://orcid.org/0000-0001-5826-1551
- name: Takayuki Nakanishi
  role: author
  orcid: https://orcid.org/0000-0003-3412-2842
- name: Takashi Takeda
  role: author
  orcid: https://orcid.org/0000-0003-2510-4562
- name: Naoto Hirosaki
  role: author

## Contact agent



## Publisher

organization: Informa UK Limited

## Managing organization



## Keyword

- subject: materials informatics
  schema: not_defined
- subject: phosphor
  schema: not_defined
- subject: europium
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: interpretability
  schema: not_defined
- subject: linear model
  schema: not_defined
- subject: composition-based feature
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: 'Science and Technology of Advanced Materials: Methods'
  issn: '27660400'
  volume: '6'
  issue: '1'
  article_number: '2691688'

## Conference



## Related item



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## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



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## Computational method



## Energy level/transition state



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## Fileset

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  filename: An interpretable linear model bridging data-driven analysis and chemical
    intuition for Eu2 -phosphor emissions.pdf
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  filename: tstm_a_2691688_sm0813.csv
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- id: '01190d12-6b92-44e7-9c9e-51ec7c8528c2'
  filename: tstm_a_2691688_sm0812.pdf
  content_type: application/pdf
  size: 477517
  md5: ed24717edff00d1560c7eca20ede4a0b

## Thumbnail

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filename: tstm_a_2691688_sm0812.pdf