Machine learning-driven discovery of hard magnetic materials using high-throughput computation and screening

Anita Halder, Durga Paudyal, Stefano Sanvito, Martin Takáč, Huseyin Ucar,
Machine learning-driven discovery of hard magnetic materials using high-throughput computation and screening,
Acta Materialia,
Volume 297,
2025,
121347,
ISSN 1359-6454,
https://doi.org/10.1016/j.actamat.2025.121347.
(https://www.sciencedirect.com/science/article/pii/S1359645425006330)
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.
Keywords: Permanent magnets; FeCo-based alloys; Magnetocrystalline anisotropy energy; Curie temperature prediction; High-throughput materials discovery