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説明:

In this study, we addressed the data scarcity problem in materials science by constructing a comprehensive dataset of dielectric materials using the Starrydata2 web system, collecting experimental data from over 20,000 samples. Using this dataset, we developed high-performance machine learning models and identified important descriptors through recursive feature elimination. As the models functioned as black boxes, we employed dimensionality reduction and clustering techniques to visualize trends in dielectric properties. By combining key factors with material clustering, we visualized the relationship between crystal lattice and dielectric permittivity in ABO3 systems, revealing a nearly linear relationship. These analyses provide an important foundation for data-driven materials research.

権利情報:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

キーワード: Materials data analysis, database, data curation, dielectric materials, ferroelectric, machine learning, dimensionality reduction

刊行年月日: 2025-12-31

出版者: Informa UK Limited

掲載誌:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 5 issue. 1

研究助成金:

• the Japan Science and Technology Agency JPMJCR19J1 (新規結晶の大規模探索に基づく革新的機能材料の開発)

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/27660400.2025.2485018

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更新時刻: 2025-06-12 16:30:20 +0900

MDRでの公開時刻: 2025-06-12 16:25:29 +0900