  Increased demand for high-performance permanent magnets in the electric
vehicle and wind turbine industries has prompted the search for cost-effective
alternatives.Discovering new magnetic materials with the desired intrinsic and
extrinsic permanent magnet properties presents a significant challenge to
researchers because of issues with the global supply of rare-earth elements,
material stability, and a low maximum magnetic energy product BH$_{max}$.While
first-principle density functional theory (DFT) predicts materials' magnetic
moments, magneto-crystalline anisotropy constants, and exchange interactions,
it cannot compute coercivity ($H_c$).Although it is possible to calculate $H_c$
theoretically with micromagnetic simulations, the predicted value is larger
than the experiment by almost an order of magnitude, due to the Brown
paradox.To circumvent these, we employ machine learning (ML) methods on an
extensive database obtained from experiments, DFT calculations, and
micromagnetic modeling.The use of a large dataset enables realistic $H_c$
predictions for materials such as Ce-doped Nd$_2$Fe$_{14}$B, comparing
favorably against micromagnetically simulated coercivities.Remarkably, our ML
model accurately identifies uniaxial magneto-crystalline anisotropy as the
primary contributor to $H_c$. With DFT calculations, we predict the Nd-site
dependent magnetic anisotropy behavior in Nd$_2$Fe$_{14}$B, confirming that Nd
$4g$-sites mainly contribute to uniaxial magneto-crystalline anisotropy, and
also calculate Curie temperature (T$_{C}$).Both calculated results are in good
agreement with experiment.The coupled experimental dataset and ML modeling with
DFT input predict $H_c$ with far greater accuracy and speed than was previously
possible using micromagnetic modeling.Further, we reverse-engineer the
inter-grain exchange coupling with micromagnetic simulations by employing the
ML predictions.
