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Increased demand for high-performance permanent magnets in the electricvehicle and wind turbine industries has prompted the search for cost-effectivealternatives. Nevertheless, the discovery of new magnetic materials with thedesired intrinsic and extrinsic permanent magnet properties presents asignificant challenge. Traditional density functional theory (DFT) accuratelypredicts intrinsic permanent magnet properties such as magnetic moments,magneto-crystalline anisotropy constants, and exchange interactions. However,it cannot compute extrinsic macroscopic properties, such as coercivity ($H_c$),which are influenced by factors like microscopic defects and internal grainstructures. Although micromagnetic simulation helps compute $H_c$, itoverestimates the values almost by an order of magnitude due to Brown'sparadox. To circumvent these limitations, we employ machine learning (ML)methods in an extensive database obtained from experiments, DFT calculations,and micromagnetic modeling. Our novel ML approach is computationally muchfaster than the micromagnetic simulation program, the mumax$^3$. Wesuccessfully utilize it to predict $H_c$ values for materials like cerium-doped$\mathrm{Nd}_2\mathrm{Fe}_{14}\mathrm{B}$, and subsequently compare thepredicted values with experimental results. Remarkably, our ML model accuratelyidentifies uniaxial magnetic anisotropy as the primary contributor to $H_c$.With DFT calculations, we predict the Nd-site dependent magnetic anisotropybehavior in $\mathrm{Nd}_2\mathrm{Fe}_{14}\mathrm{B}$, confirming $4f$-siteplanar and $4g$-site uniaxial to crystalline $c$-direction in good agreementwith experiment. The Green's function atomic sphere approximation calculated aCurie temperature ($T_{\rm C}$) for $\mathrm{Nd}_2\mathrm{Fe}_{14}\mathrm{B}$that also agrees well with experiment.

Accurate Machine Learning Predictions of Coercivity in High-Performance Permanent Magnets