Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

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.

DOI： 10.1002/aisy.202400253

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Publishing type：Research paper (scientific journal)  

DOI： 10.1080/14686996.2024.2423600

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Publishing type：Research paper (scientific journal)  

DOI： 10.1039/D4TC01116C

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Total pages：X, 289p   Language：Japanese  

CiNii Books

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Total pages：424p   Language：Japanese  

CiNii Books

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Authorship：Lead author,　Corresponding author   Language：English  

We report an interpretation method for deep learning models that allows us to
handle high-dimensional spectral data in materials science. The proposed method
uses feature extraction and clustering analysis to categorize materials into
classes based on similarities in both spectral data and chemical
characteristics such as elemental composition and atomic arrangement. As a
demonstration, we apply this method to an atomistic line graph neural network
(ALIGNN) model trained on first-principles calculation data of 2,681 metal
oxides, chalcogenides, and related compounds for optical absorption spectrum
prediction. Our analysis reveals key elemental species and their coordination
environments that influence optical absorption onset characteristics. The
method proposed herein is broadly applicable to the classification and
interpretation of diverse spectral data, extending beyond the optical
absorption spectra of inorganic crystals.

DOI： 10.48550/arXiv.2510.17123

arXiv

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Other Link： http://arxiv.org/pdf/2510.17123v1

Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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Language：Japanese   Presentation type：Oral presentation (invited, special)  

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Language：Japanese   Presentation type：Public lecture, seminar, tutorial, course, or other speech  

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Language：Japanese   Presentation type：Oral presentation (general)  

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Language：Japanese   Presentation type：Oral presentation (general)  

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