Accurate Prediction of Magnetic Properties of Permanent Magnets Using Machine Learning

Discovering permanent magnets that do not rely on costly rare-earth elements yet exhibit

performance on par with the current leading neo-magnets is a pressing challenge for scientists

to fulfill the skyrocketing demand for high-performance magnets in electric automotive

industries. Theoretically, this endeavor involves predicting intrinsic and extrinsic magnetic

properties to identify optimal materials. While traditional ab initio density functional theory (DFT) proves

useful in calculating properties like saturation magnetization, magnetic anisotropy, and the

Curie temperature for simpler systems, we cannot compute coercivity. Moreover, the

theoretical determination of macroscopic coercive properties is poor, largely due to Brown's

paradox. To address this limitation, we employ DFT by

incorporating machine learning (ML) to synthesize experimentally measured magnetic properties and utilize micromagnetic

modeling. This innovative ML methodology enables the precise and accurate prediction of

macroscopic magnetic properties, including the coercivity. The approach is verified on Ce-doped Nd₂Fe₁₄B, and the predicted coercivities are

compared with available experimental data.