Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content

Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content

Rare-earth elements like neodymium, terbium and dysprosium are crucial to the performance of permanent magnets used in various green-energy technologies like hybrid or electric cars. To address the supply risk of those elements, we applied machine-learning techniques to design magnetic materials wit...

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Bibliographic Details
Main Authors: Alexander Kovacs, Johann Fischbacher, Harald Oezelt, Alexander Kornell, Qais Ali, Markus Gusenbauer, Masao Yano, Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Yuan Hong, Stéphane Grenier, Thibaut Devillers, Nora M. Dempsey, Tetsuya Fukushima, Hisazumi Akai, Naoki Kawashima, Takashi Miyake, Thomas Schrefl
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-01-01
Series:Frontiers in Materials
Subjects:
machine learning–ML
materials design
NdFeB permanent magnet
combinatorial sputtering
rare-earth element (REE)
optimization
Online Access:https://www.frontiersin.org/articles/10.3389/fmats.2022.1094055/full

Internet

https://www.frontiersin.org/articles/10.3389/fmats.2022.1094055/full

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