Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds

Author:

Sato Nobuya1ORCID,Takahashi Akira1ORCID,Kiyohara Shin12ORCID,Terayama Kei345ORCID,Tamura Ryo67ORCID,Oba Fumiyasu15ORCID

Affiliation:

1. Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology R3‐7, 4259 Nagatsuta, Midori‐ku 226‐8501 Japan

2. Institute for Materials Research Tohoku University 2‐2‐1 Katahira Aoba‐ku Sendai 980‐8577 Japan

3. Graduate School of Medical Life Science Yokohama City University 1‐7‐29 Suehiro‐cho, Tsurumi‐ku 230‐0045 Japan

4. RIKEN Center for Advanced Intelligence Project 1‐4‐1 Nihonbashi, Chuo‐ku Tokyo 103‐0027 Japan

5. MDX Research Center for Element Strategy International Research Frontiers Initiative Tokyo Institute of Technology SE‐6, 4259 Nagatsuta Midori‐ku Yokohama 226‐8501 Japan

6. Center for Basic Research on Materials National Institute for Materials Science 1‐1 Namiki Tsukuba 305‐0044 Japan

7. Graduate School of Frontier Sciences The University of Tokyo 5‐1‐5 Kashiwa‐no‐ha Kashiwa 277‐8568 Japan

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.

Publisher

Wiley

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