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Machine learning-driven discovery of hard magnetic materials using high-throughput computation and screening
Halder, Anita Paudyal, Durga Sanvito, Stefano Takáč, Martin Ucar, Huseyin
Halder, Anita
Paudyal, Durga
Sanvito, Stefano
Takáč, Martin
Ucar, Huseyin
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Department
Machine Learning
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Journal article
Date
2025
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Language
English
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Abstract
We present a machine-learning-driven framework for discovering high-performance rare-earth-free hard magnetic materials integrating machine learning, a universal graph deep-learning interatomic potential, and density functional theory validation. Key contributions include the identification of FeCo-based ternary alloys with remarkable magnetic properties, such as uniaxial anisotropy constant, K1, Curie temperature, TC, and saturation magnetization, MS. Notable examples include Fe6CoB2 and FeCo5B, which exhibit K1 values of 1.76 MJ/m3 and 1.00 MJ/m3, respectively, with MS above 1.3 T, and TC exceeding 600 K. These properties align with the needs of high-temperature and high-performance applications. The universal graph deep-learning interatomic potential M3GNet accelerates the structural relaxation process, enabling the efficient screening of 48,000 candidate structures, while density functional theory validates the top performers with energy product (BH)max reaching more than 600 kJ/m3. Our study highlights a scalable, efficient pipeline for advancing the discovery of permanent magnets, reducing reliance on rare-earth elements.
Citation
A. Halder, D. Paudyal, S. Sanvito, M. Takáč, and H. Ucar, “Machine learning-driven discovery of hard magnetic materials using high-throughput computation and screening,” Acta Mater, vol. 297, p. 121347, Sep. 2025, doi: 10.1016/J.ACTAMAT.2025.121347
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Acta Materialia
Conference
Keywords
Curie temperature prediction, FeCo-based alloys, High-throughput materials discovery, Magnetocrystalline anisotropy energy, Permanent magnets
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Publisher
Elsevier