Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds

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 material properties such as the bandgap and dielectric constant. However, such approaches do not meet demands for grading materials according to properties of interest simultaneously with chemical and structural similarities. Herein, a clustering method grouping similar materials in terms of both the target properties and basic features is proposed. The clustering is compared considering the cohesive energy with that considering the bandgap of metal oxides, showing that their categorizations are clearly different. Further, several clusters classified by the bandgap are analyzed, and coordination environments related to each range of the bandgap are revealed. The clustering for the electronic static dielectric constant identifies a cluster involving several perovskite‐type oxides and balancing with the bandgap near the Pareto front. The 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 basic features into account.

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Publication Details

Journal
Advanced Intelligent Systems
Published
2024-08-05
DOI
https://doi.org/10.1002/aisy.202400253
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.35

Funders

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article

Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds

Fumiyasu Oba, Ryo Tamura, Shin Kiyohara, Nobuya Sato et al.
3 citations
Advanced Intelligent Systems
Machine Learning in Materials Science
0.35
article

Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds

Fumiyasu Oba, Ryo Tamura, Shin Kiyohara, Nobuya Sato, Kei Terayama, Akira Takahashi
article en
3 citations

Abstract

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 material properties such as the bandgap and dielectric constant. However, such approaches do not meet demands for grading materials according to properties of interest simultaneously with chemical and structural similarities. Herein, a clustering method grouping similar materials in terms of both the target properties and basic features is proposed. The clustering is compared considering the cohesive energy with that considering the bandgap of metal oxides, showing that their categorizations are clearly different. Further, several clusters classified by the bandgap are analyzed, and coordination environments related to each range of the bandgap are revealed. The clustering for the electronic static dielectric constant identifies a cluster involving several perovskite‐type oxides and balancing with the bandgap near the Pareto front. The 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 basic features into account.

Advanced Intelligent SystemsVol. 6(12)
Tokyo Institute of Technology (JP), Tohoku University (JP), National Institute for Materials Science (JP), RIKEN Center for Advanced Intelligence Project (JP), The University of Tokyo (JP), Yokohama City University (JP)
Kanagawa Institute of Industrial Science and Technology, Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology
Openalex Percentile: Top 45%
Machine Learning in Materials Science
0.35
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