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

Aug 4, 2026

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Rare-earth elements such as neodymium, terbium, and dysprosium are essential for high-performance permanent magnets used in green-energy technologies, but supply risk motivates reducing critical-element dependence. The study develops physics-informed machine-learning methods that integrate physical models across length scales, from atomistic to micrometer-scale granular microstructures. Data assimilation merges experimental and simulation data to optimize chemical composition and microstructure while enabling interpretation via prediction-supporting analyses such as variable importance. Results indicate pathways to realize high-performance Nd-lean NdFeB magnets without terbium and dysprosium.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages19

Reading Time48 min

AccessFree to download

Introduction

Core performance requirements for traction-motor permanent magnets

Temperature limits and the role of coercive field

Rare-earth elements and supply-risk motivation

Physics-informed machine learning for materials design

TYPE Original Research PUBLISHED 18 January 2023 DOI 10.3389/fmats.2022.1094055 OPEN ACCESS EDITED BY Kinnari Parekh, Charotar University of Science and Technology, India REVIEWED BY Tadeusz Szumiata, Kazimierz Pułaski University of Technology and Humanities in Radom, Poland Tu Manh Le, Phenikaa University, Vietnam *CORRESPONDENCE Thomas Schrefl, [thomas.schrefl@donau-uni.ac.at](thomas.schrefl@donau-uni.ac.at) SPECIALTY SECTION This article was submitted to Energy Materials, a section of the journal Frontiersin Materials RECEIVED 09 November 2022 ACCEPTED 23 December 2022 PUBLISHED 18 January 2023 CITATION Kovacs A, Fischbacher J, Oezelt H, Kornell A, Ali Q, Gusenbauer M, Yano M, Sakuma N, Kinoshita A, Shoji T, Kato A, Hong Y, Grenier S, Devillers T, Dempsey NM, Fukushima T, Akai H, Kawashima N, Miyake T and Schrefl T (2023), Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content. Front. Mater. 9:1094055 . doi: 10.3389/fmats.2022.1094055 COPYRIGHT © 2023 Kovacs, Fischbacher, Oezelt, Kornell, Ali, Gusenbauer, Yano, Sakuma, Kinoshita, Shoji, Kato, Hong, Grenier, Devillers, Dempsey, Fukushima, Akai, Kawashima, Miyake and Schrefl. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Physics-informed machine learning combining experiment and simulation for the design of neodymium-iron-boron permanent magnets with reduced critical-elements content Alexander Kovacs 1,2, Johann Fischbacher 1,2, Harald Oezelt 1,2, Alexander Kornell 1,2, Qais Ali 1,2, Markus Gusenbauer 1,2, Masao Yano 3, Noritsugu Sakuma 3, Akihito Kinoshita 3, Tetsuya Shoji 3, Akira Kato 3, Yuan Hong 4, Stéphane Grenier 4, Thibaut Devillers 4, Nora M. Dempsey 4, Tetsuya Fukushima 5, Hisazumi Akai 5, Naoki Kawashima 5, Takashi Miyake 6 and Thomas Schrefl 1,2* 1Christian Doppler Laboratory for magnet design through physics informed machine learning, Danube University Krems, Wiener Neustadt, Austria, 2 Department for Integrated Sensor Systems, Danube University Krems, Wiener Neustadt, Austria, 3Advanced Materials Engineering Division, Toyota Motor Corporation, Susono, Japan, 4 Université Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, Grenoble, France, 5The Institute for Solid State Physics, The University of Tokyo, Kashiwa, Japan, 6 National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan 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 with reduced neodymium content and without terbium and dysprosium. However, the performance of the magnet intended to be used in electric motors should be preserved. We developed machine-learning methods that assist materials design by integrating physical models to bridge the gap between length scales, from atomistic to the micrometer-sized granular microstructure of neodymium-ironboron permanent magnets. Through data assimilation, we combined data from experiments and simulations to build machine-learning models which we used to optimize the chemical composition and the microstructure of the magnet. We applied techniques that help to understand and interpret the results of machine learning predictions. The variables importance shows how the main design variables influence the magnetic properties. High-throughput

FAQs

Why focus on neodymium-iron-boron permanent magnets for electric vehicles?

They support traction motor applications where performance depends on the usable magnetic field and the ability to withstand opposing magnetic fields under operating temperatures.

What is the main challenge addressed by the study?

Reducing supply risk by lowering neodymium content and removing terbium and dysprosium while maintaining the magnet performance required for electric-motor use.

How does the proposed method combine experiments and simulations?

It uses data assimilation to merge experimental measurements with simulation-derived information, building machine-learning models that optimize chemical composition and microstructure across multiple length scales.

Aug 4, 2026

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