journal article·2025-06

Machine learning guided design of RE-Fe-B(RE=PrNd,La,Ce) with comprehensive high performance

Zheng Wang, Shiyi Zhang, Jing Wang, Ming Zhang, Yunzhong Chen, Baohe Li, Tongyun Zhao, Minggang Zhu, Fengxia Hu, Baogen Shen, Wei Li

Acta Materialia· Volume 292
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Abstract

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OpenAlex topics: Magnetic Properties of Alloys · Rare-earth and actinide compounds · Advanced Materials Characterization Techniques

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References (42)

Show source-provided references
1. New material for permanent magnets on a base of Nd and Fe10.1063/1.333572 ↗
2. Perspective and prospects for rare earth permanent magnets10.1016/j.eng.2018.11.034 ↗
3. Magnetic materials and devices for the 21st century: stronger, lighter, and more energy efficient10.1002/adma.201002180 ↗
4. Microstructure of fine-grained Nd–Fe–B sintered magnets with high coercivity10.1016/j.scriptamat.2011.05.006 ↗
5. High-coercivity Nd-Fe-B magnets obtained with the electrophoretic deposition of submicron TbF3 followed by the grain-boundary diffusion process10.1016/j.actamat.2016.06.003 ↗
6. Epoxy resin effect on anisotropic Nd–Fe–B rubber-bonded magnets performance10.1016/j.jallcom.2010.09.210 ↗
7. Overview of Nd–Fe–B magnets and coercivity (invited)10.1063/1.361565 ↗
8. Tuning Ce distribution for high performanced Nd-Ce-Fe-B sintered magnets10.1016/j.jmmm.2016.06.048 ↗
9. Preparation and characterization of La2-Xcexfe14b compounds
10. Cerium: an unlikely replacement of dysprosium in high performance Nd–Fe–B permanent magnets10.1002/adma.201404892 ↗
11. HDDR treatment of Ce-substituted Nd2Fe14B-based permanent magnet alloys - phase structure evolution, intergranular processes and magnetic property development10.1016/j.jallcom.2019.152215 ↗
12. Method construction of structure-property relationships from data by machine learning assisted mining for materials design applications10.1016/j.matdes.2020.109194 ↗
13. A data-driven statistical model for predicting the critical temperature of a superconductor10.1016/j.commatsci.2018.07.052 ↗
14. Deep learning model for finding new superconductors10.1103/PhysRevB.103.014509 ↗
15. Can machine learning identify the next high-temperature superconductor? Examining extrapolation performance for materials discovery10.1039/C8ME00012C ↗
16. Machine learning modeling of superconducting critical temperature10.1038/s41524-018-0085-8 ↗
17. Prediction of the composition and hardness of high-entropy alloys by machine learning10.1007/s11837-019-03704-4 ↗
18. Machine-learning model for predicting phase formations of high-entropy alloys
19. Machine learning-enabled high-entropy alloy discovery10.1126/science.abo4940 ↗
20. Machine learning assisted design of high entropy alloys with desired property10.1016/j.actamat.2019.03.010 ↗
21. Uncovering the eutectics design by machine learning in the Al-Co-Cr-Fe-Ni high entropy system10.1016/j.actamat.2019.10.043 ↗
22. Machine learning and theoretical prediction of highly spin-polarized Cr2COx MXene with enhanced curie temperature10.1002/adfm.202411170 ↗
23. Coupling a crystal graph multilayer descriptor to active learning for rapid discovery of 2D ferromagnetic semiconductors/half-metals/metals10.1002/adma.202002658 ↗
24. Accelerated design of Fe-based soft magnetic materials using machine learning and stochastic optimization10.1016/j.actamat.2020.05.006 ↗
25. Integrating ultra-high saturation magnetization intensity and extreme-low coercivity in FeCoBSiCu alloy assisted by machine learning10.1016/j.actamat.2024.120643 ↗
26. Predicting the Curie temperature of Sm-Co-based alloys via data-driven strategy10.1016/j.actamat.2024.120026 ↗
27. Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis10.1016/j.actamat.2023.119563 ↗
28. Electronegativity, atomic pseudoradii and rare earth permanent magnet materials
29. Heuristic algorithms
30. A review on genetic algorithm: past, present, and future10.1007/s11042-020-10139-6 ↗
31. Differential evolution: a recent review based on state-of-the-art works10.1016/j.aej.2021.09.013 ↗
32. Particle swarm optimization algorithm and its applications: a systematic review10.1007/s11831-021-09694-4 ↗
33. A survey of ensemble learning: concepts, algorithms, applications, and prospects10.1109/ACCESS.2022.3207287 ↗
34. Scikit-learn: machine learning in Python
35. Pymoo: multi-objective optimization in Python10.1109/ACCESS.2020.2990567 ↗
36. Global potential of rare earth resources and rare earth demand from clean technologies10.3390/min7110203 ↗
37. R2FE14B Materials - intrinsic-properties and technological aspects10.1103/RevModPhys.63.819 ↗
38. Nd2Fe14B permanent magnets substituted with non-critical light rare earth elements (Ce, La): a review10.1016/j.jmmm.2023.170768 ↗
39. Tuning the structure and intrinsic magnetic properties of Ce2Fe14B alloys by elimination of CeFe2 with La substitution10.1016/j.jmmm.2020.166747 ↗
40. Manipulating Ce valence in RE2Fe14B tetragonal compounds by La-Ce Co-doping: resultant crystallographic and magnetic anomaly
41. Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning10.1039/D2TA10043F ↗
42. There are fluctuations in rare earth prices, so we chose the April 2023 prices as a median for our calculations, the price data was from RARE EARTH INFORMATION 04(2024) and converted to the US dollar before the calculation.