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 witho...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202400253 |
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| _version_ | 1846111022754037760 |
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| author | Nobuya Sato Akira Takahashi Shin Kiyohara Kei Terayama Ryo Tamura Fumiyasu Oba |
| author_facet | Nobuya Sato Akira Takahashi Shin Kiyohara Kei Terayama Ryo Tamura Fumiyasu Oba |
| author_sort | Nobuya Sato |
| collection | DOAJ |
| 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 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. |
| format | Article |
| id | doaj-art-1d8aa3932f954b9c801abb752611b8af |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-1d8aa3932f954b9c801abb752611b8af2024-12-23T13:10:42ZengWileyAdvanced Intelligent Systems2640-45672024-12-01612n/an/a10.1002/aisy.202400253Target Material Property‐Dependent Cluster Analysis of Inorganic CompoundsNobuya Sato0Akira Takahashi1Shin Kiyohara2Kei Terayama3Ryo Tamura4Fumiyasu Oba5Laboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology R3‐7, 4259 Nagatsuta, Midori‐ku 226‐8501 JapanLaboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology R3‐7, 4259 Nagatsuta, Midori‐ku 226‐8501 JapanLaboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology R3‐7, 4259 Nagatsuta, Midori‐ku 226‐8501 JapanGraduate School of Medical Life Science Yokohama City University 1‐7‐29 Suehiro‐cho, Tsurumi‐ku 230‐0045 JapanCenter for Basic Research on Materials National Institute for Materials Science 1‐1 Namiki Tsukuba 305‐0044 JapanLaboratory for Materials and Structures Institute of Innovative Research Tokyo Institute of Technology R3‐7, 4259 Nagatsuta, Midori‐ku 226‐8501 JapanThe 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.https://doi.org/10.1002/aisy.202400253clusteringinorganic compoundsinterpretable artificial intelligencerandom forest |
| spellingShingle | Nobuya Sato Akira Takahashi Shin Kiyohara Kei Terayama Ryo Tamura Fumiyasu Oba Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds Advanced Intelligent Systems clustering inorganic compounds interpretable artificial intelligence random forest |
| title | Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds |
| title_full | Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds |
| title_fullStr | Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds |
| title_full_unstemmed | Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds |
| title_short | Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds |
| title_sort | target material property dependent cluster analysis of inorganic compounds |
| topic | clustering inorganic compounds interpretable artificial intelligence random forest |
| url | https://doi.org/10.1002/aisy.202400253 |
| work_keys_str_mv | AT nobuyasato targetmaterialpropertydependentclusteranalysisofinorganiccompounds AT akiratakahashi targetmaterialpropertydependentclusteranalysisofinorganiccompounds AT shinkiyohara targetmaterialpropertydependentclusteranalysisofinorganiccompounds AT keiterayama targetmaterialpropertydependentclusteranalysisofinorganiccompounds AT ryotamura targetmaterialpropertydependentclusteranalysisofinorganiccompounds AT fumiyasuoba targetmaterialpropertydependentclusteranalysisofinorganiccompounds |