データ科学，計算科学，実験的手法を活用した超電導物質探索と設計

カテゴリ: 論文誌(論文単位)

グループ名: 【A】基礎・材料・共通部門

発行日: 2024/09/01

タイトル(英語): Data Science, Simulation, and Experimental Methods for Exploration and Design of Superconducting Materials

著者名: 堀出　朋哉（名古屋大学），伊豫　彰（産業技術総合研究所），一野　祐亮（愛知工業大学）

著者名(英語): Tomoya Horide (Nagoya University), Akira Iyo (National Institute of Advanced Industrial Science and Technology (AIST)), Yusuke Ichino (Aichi Institute of Technology)

キーワード: 超電導物質探索，機械学習，データベース，インフォマティクス，シミュレーション　　exploration of new superconductors，machine learning，database，informatics，simulation

要約(英語): The discovery of new superconductors has a significant impact on the scientific and engineering communities, unraveling interesting physical phenomena and providing unique applications in energy and devices. Superconductors with a high critical temperature are limited to a few families, such as cuprates, iron-based compounds, and hydrides under ultra-high pressure. In traditional studies, the exploration of new superconductors relies on theories, experiments, and simulations. However, recent advances in data science have made machine learning available in a variety of fields, including materials informatics. Utilizing superconductor databases and various regression methods, machine learning has proposed several new superconductors. The chemical descriptors are widely used, and the descriptor of the crystalline structure is being developed for more accurate prediction. In this review, the theoretical and experimental studies for the discovery of new superconductors are explained. The available database and data-driven studies are also shown. Furthermore, after reviewing the recent machine learning studies for the discovery of new superconductors and other materials, future aspects in this field are discussed.

本誌: 電気学会論文誌A（基礎・材料・共通部門誌） Vol.144 No.9 （2024） 特集：超電導材料創出に向けたインフォマティクス応用の最前線

本誌掲載ページ: 344-349 p

原稿種別: 解説／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejfms/144/9/144_344/_article/-char/ja/