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.
Authors
- Fumiyasu Oba (ORCID: https://orcid.org/0000-0001-7178-5333)
- Ryo Tamura (ORCID: https://orcid.org/0000-0002-0349-358X)
- Shin Kiyohara (ORCID: https://orcid.org/0000-0003-2890-5760)
- Nobuya Sato (ORCID: https://orcid.org/0000-0002-8661-0410)
- Kei Terayama (ORCID: https://orcid.org/0000-0003-3914-248X)
- Akira Takahashi (ORCID: https://orcid.org/0000-0002-3159-9007)
Institutions
- 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)
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
- 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