Accurate machine-learning predictions of coercivity in high-performance permanent magnets

Bhandari, Churna; Nop, Gavin N.; Smith, Jonathan D. H.; Paudyal, Durga

doi:10.1103/physrevapplied.22.024046

Accurate machine-learning predictions of coercivity in high-performance permanent magnets

Journal Article · Fri Aug 16 00:00:00 EDT 2024 · Physical Review Applied

DOI:https://doi.org/10.1103/physrevapplied.22.024046· OSTI ID:2448523

^[1]; Nop, Gavin N. ^[1]; ^[1]; ^[1]

Ames Lab., and Iowa State Univ., Ames, IA (United States)

Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering 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-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as 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 issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic H_c predictions for materials such as Ce-doped Nd₂⁢Fe₁₄⁢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 Nd₂⁢Fe₁₄⁢B, confirming that Nd 4⁢g sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (T_c). Finally, both calculated results are in good agreement with the experiments. 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 grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.

View Accepted Manuscript (DOE)

Research Organization:: Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Advanced Materials & Manufacturing Technologies Office (AMMTO); USDOE Office of Science (SC), Office of Workforce Development for Teachers & Scientists (WDTS)

Grant/Contract Number:: AC02-07CH11358

OSTI ID:: 2448523

Report Number(s):: IS-J--11,417

Journal Information:: Physical Review Applied, Journal Name: Physical Review Applied Journal Issue: 2 Vol. 22; ISSN 2331-7019

Publisher:: American Physical Society (APS)Copyright Statement

Country of Publication:: United States

Language:: English

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