Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features-Reference-Cited by-同舟云学术

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Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features

Published:2023-01 Issue: Volume:811 Page:140231
ISSN:0009-2614
Container-title:Chemical Physics Letters
language:en
Short-container-title:Chemical Physics Letters

Author:

Kini Anoop,Kumar Choudhary Amit,Hohs Dominic,Jansche Andreas,Baumgartl Hermann,Büttner Ricardo,Bernthaler Timo,Goll Dagmar,Schneider Gerhard

Funder

Carl Zeiss Stiftung

Publisher

Elsevier BV

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Reference21 articles.

1. The partitioning of Dy and Tb in NdFeB magnets: A first-principles study;Liu;J. Appl. Phys.,2012

2. Improved thermal stability of Nd-Ce-Fe-B sintered magnets by Y substitution;Peng;Scr. Mater.,2017

3. High-Throughput Methods for Searching New Permanent Magnet Materials;Goll;IEEE Trans. Magn.,2014

4. Reaction sintering as a high-throughput approach for magnetic materials development;Goll;Scr. Mater.,2018

5. Novel Permanent Magnets by High-Throughput Experiments;Goll;JOM,2015

Cited by 1 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. A data-driven approach to predict the saturation magnetization for magnetic 14:2:1 phases from chemical composition;AIP Advances;2024-01-01

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