28 件のレコードが見つかりました。
Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics
- ジャーナル論文
- 著者
-
Quentin Bizot (author) (この著者で検索)
https://orcid.org/0000-0002-4314-2266
(unauthenticated)
;
Ryo Tamura (author) (この著者で検索)
;
Guillaume Deffrennes (author) (この著者で検索)
https://orcid.org/0000-0002-3752-2537
(unauthenticated)
- キーワード
- CALPHAD, Machine learning
- 刊行年月日
- 2026-02-07
- 更新時刻
- 2026-04-01 13:44:57 +0900
Elemental Reactivity Maps for Materials Discovery
- ジャーナル論文
- 著者
-
Yuki Inada (author) (この著者で検索)
https://orcid.org/0000-0002-9104-1320
(unauthenticated)
;
Masaya Fujioka (author) (この著者で検索)
https://orcid.org/0000-0002-5829-6591
(unauthenticated)
;
;Haruhiko Morito (author) (この著者で検索)Tohru Sugahara (author) (この著者で検索)
https://orcid.org/0000-0001-5842-5392
(unauthenticated)
;
Hisanori Yamane (author) (この著者で検索)
https://orcid.org/0000-0002-7931-5210
(unauthenticated)
;
Yukari Katsura (author) (この著者で検索)
- キーワード
- Crystal structure, Elements, Machine learning, Materials, Reactivity
- 刊行年月日
- 2025-03-25
- 更新時刻
- 2026-02-21 16:30:08 +0900
Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation
- ジャーナル論文
- 著者
-
N. Kulesh (author) (この著者で検索)National Institute for Materials Science
;
A. Bolyachkin (author) (この著者で検索)National Institute for Materials Science
;
I. Suzuki (author) (この著者で検索)
https://orcid.org/0000-0002-8932-8226
(unauthenticated)
National Institute for Materials Science
;
Y.K. Takahashi (author) (この著者で検索)National Institute for Materials Science
;
H. Sepehri-Amin (author) (この著者で検索)National Institute for Materials Science
;
K. Hono (author) (この著者で検索)National Institute for Materials Science
- キーワード
- Heat-assisted magnetic recording (HAMR), FePt, Deep learning, Machine learning, Image segmentation
- 刊行年月日
- 2023-05-27
- 更新時刻
- 2025-05-27 08:30:20 +0900
Automated odor-blending with one-pot Bayesian optimization
- ジャーナル論文
- 著者
-
;Yota Fukui (author) (この著者で検索)Kosuke Minami (author) (この著者で検索)National Institute for Materials Science
;
Kota Shiba (author) (この著者で検索)National Institute for Materials Science
;
Genki Yoshikawa (author) (この著者で検索)National Institute for Materials Science
;
Koji Tsuda (author) (この著者で検索)National Institute for Materials Science
;
Ryo Tamura (author) (この著者で検索)National Institute for Materials Science
- キーワード
- Machine learning, MSS, Bayesian optimization, Olfactory sensor
- 刊行年月日
- 2024-04-16
- 更新時刻
- 2024-06-10 12:30:19 +0900
Machine Learning Approach for Evaluation of Nanodefects and Magnetic Anisotropy in FePt Granular Films
- ジャーナル論文
- 著者
-
;E. Dengina (author) (この著者で検索)National Institute for Materials ScienceA. Bolyachkin (author) (この著者で検索)National Institute for Materials Science
;
H. Sepehri-Amin (author) (この著者で検索)National Institute for Materials Science
;
K. Hono (author) (この著者で検索)National Institute for Materials Science
- キーワード
- Magnetic recording, Micromagnetic simulations, Machine learning, FePt media
- 刊行年月日
- 2022-05-13
- 更新時刻
- 2024-10-15 16:30:13 +0900
Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography
- ジャーナル論文
- 著者
-
;Sitanan Sartyoungkul (author) (この著者で検索);Balasubramaniyan Sakthivel (author) (この著者で検索)Pavel Sidorov (author) (この著者で検索)
https://orcid.org/0000-0001-6462-702X
(unauthenticated)
;
Yuuya Nagata (author) (この著者で検索)
https://orcid.org/0000-0001-5926-5845
(unauthenticated)
- キーワード
- Automated synthesis, Fragment descriptor, Machine learning, Retention time prediction, Supercritical fluid chromatography
- 刊行年月日
- 2025-11-26
- 更新時刻
- 2025-12-24 15:05:55 +0900
Machine Learning-Based Experimental Design in Materials Science
- 書籍
- 著者
-
Tsuda, Koji (author) (この著者で検索)
;
Dieb, Thaer M. (author) (この著者で検索)
- キーワード
- Materials design, Machine learning, Optimal experiment design
- 刊行年月日
- 2018-01-16
- 更新時刻
- 2022-10-03 01:58:06 +0900
材料開発における4つのインフォマティクス
- 報告書
- 著者
-
岩﨑 悠真 (author) (この著者で検索)物質・材料研究機構 マテリアル基盤研究センター/材料設計分野/データ駆動型材料設計グループ
- キーワード
- Machine learning
- 刊行年月日
- [2024年7月]
- 更新時刻
- 2024-12-02 16:31:05 +0900