Data-driven analysis and visualization of dielectric properties curated from scientific literature

Data-driven analysis and visualization of dielectric properties curated from scientific literature

Data-driven methods are powerful tools for understanding and discovering materials. However, in certain domains of materials science, the lack of available datasets significantly restricts the research scope. To address this issue, we utilized Starrydata2 web system to compile a comprehensive datase...

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Bibliographic Details
Main Authors: Tomoki Murata, Naoto Saito, Eiji Koyama, Ton Nu Thanh Phuong, Ryusuke Misawa, Satoshi Yokomizo, Tomoya Mato, Yu Takada, Sakyo Hirose, Yukari Katsura
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Science and Technology of Advanced Materials: Methods
Subjects:
Materials data analysis
database
data curation
dielectric materials
ferroelectric
machine learning
Online Access:https://www.tandfonline.com/doi/10.1080/27660400.2025.2485018
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