# Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds

https://mdr.nims.go.jp/datasets/4022f7ed-bcdb-4f3b-b544-7feae8bcf6b1

## File

- [Advanced Intelligent Systems - 2024 - Sato - Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds.pdf](https://mdr.nims.go.jp/filesets/7a1366cc-60c5-48b3-bedd-6450cf6a0b40/download) ([Detail](https://mdr.nims.go.jp/filesets/7a1366cc-60c5-48b3-bedd-6450cf6a0b40.md))

## Id

4022f7ed-bcdb-4f3b-b544-7feae8bcf6b1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2024-08-14T21:17:37.395316Z

## Updated at

2024-08-19T07:30:18.181829Z

## Published at

2024-08-19T07:30:18.279919Z

## Doi



## First published url

https://doi.org/10.1002/aisy.202400253

## Date published

2024-08-05

## Recorded date published

2024-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds
  title_type: original
  lang: en

## Description

- description: The cluster analysis of materials categorizes them according to similarities
    based on the features of materials, providing insight into the relationship between
    the materials. Conventional cluster analyses typically use basic features derived
    from the chemical composition and crystal structure without considering target
    properties such as band gap and dielectric constant. However, such approaches
    do not meet demands for grading materials according to properties of interest
    simultaneously with chemical and structural similarities. In this article, we
    propose a clustering method grouping similar materials in terms of both the target
    properties and features. We compare the clustering considering the cohesive energy
    with that considering the band gap of metal oxides, showing that their categorizations
    are clearly different. We further analyze several clusters classified by the band
    gap and reveal coordination environments related to each range of the band gap.
    The clustering for the electronic static dielectric constant identifies a cluster
    involving several perovskite-type oxides and balancing with the band gap near
    the Pareto front. Our method enables analyses with different viewpoints from those
    of the conventional clustering and feature importance analyses by taking the relationship
    between the target property and the features into account.
  description_type: abstract
  lang: und

## Creator

- name: Nobuya Sato
  role: author
  orcid: https://orcid.org/0000-0002-8661-0410
- name: Akira Takahashi
  role: author
  orcid: https://orcid.org/0000-0002-3159-9007
- name: Shin Kiyohara
  role: author
  orcid: https://orcid.org/0000-0003-2890-5760
- name: Kei Terayama
  role: author
  orcid: https://orcid.org/0000-0003-3914-248X
- name: Ryo Tamura
  role: author
  orcid: https://orcid.org/0000-0002-0349-358X
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Fumiyasu Oba
  role: author
  orcid: https://orcid.org/0000-0001-7178-5333

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: Cluster Analysis
  schema: not_defined
- subject: Inorganic Compounds
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Advanced Intelligent Systems
  issn: '26404567'

## Conference



## Related item



## Funding



## 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



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## Fileset

- id: 7a1366cc-60c5-48b3-bedd-6450cf6a0b40
  filename: Advanced Intelligent Systems - 2024 - Sato - Target Material Property‐Dependent
    Cluster Analysis of Inorganic Compounds.pdf
  content_type: application/pdf
  size: 2674529
  md5: 6dcb22a93396fb89fea84ae5b0822bae

## Thumbnail

fileset_id: 7a1366cc-60c5-48b3-bedd-6450cf6a0b40
filename: Advanced Intelligent Systems - 2024 - Sato - Target Material Property‐Dependent
  Cluster Analysis of Inorganic Compounds.pdf