Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity-Reference-Cited by-同舟云学术

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Interpretable machine learning-assisted design of Fe-based nanocrystalline alloys with high saturation magnetic induction and low coercivity

Published:2024-07 Issue: Volume:188 Page:73-83
ISSN:1005-0302
Container-title:Journal of Materials Science & Technology
language:en
Short-container-title:Journal of Materials Science & Technology

Author:

Zhang Ning,He Aina,Zhang Gan,Cai Peng,Zhang Bojun,Ling Yufan,Dong Yaqiang,Li Jiawei,Man Qikui,Shen Baogen

Funder

Key Research and Development Program of Zhejiang Province

Youth Innovation Promotion Association of the Chinese Academy of Sciences

National Key Research and Development Program of China

Natural Science Foundation of Ningbo Municipality

Publisher

Elsevier BV

Reference105 articles.

1. Magnetic Materials and Devices for the 21st Century: Stronger, Lighter, and More Energy Efficient

2. Soft magnetic materials for a sustainable and electrified world

3. Amorphous and nanocrystalline materials for applications as soft magnets

4. Modern soft magnets: Amorphous and nanocrystalline materials

5. New Fe‐based soft magnetic alloys composed of ultrafine grain structure

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Investigations on the Surface Crystallization Behavior and Magnetic Properties of Fe81.5+xSi3B10−xP3.5C0.2Cu0.8Nb1 Soft Magnetic Nanocrystalline Alloys;Journal of Materials Engineering and Performance;2024-09-03

2. Interpretable Machine Learning for Accelerating Reverse Design and Optimizing CO₂ Methanation Catalysts with High Activity at Low Temperatures;Industrial & Engineering Chemistry Research;2024-08-07

3. Optimizing Glass Formation Ability and Magnetic Flux Density of High-Fe-Content Fe-Based Amorphous Alloys Using Interpretable Machine Learning;2024

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