Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session Z18: Data science, AI, and machine learning in physics II
11:30 AM–2:18 PM,
Friday, March 8, 2024
Room: M100I
Sponsoring
Units:
GDS GMED
Chair: Neha Goswami, University of Illinois Urbana-Champaign
Abstract: Z18.00013 : Accurate Prediction of Magnetic Properties of Permanent Magnets Using Machine Learning*
1:54 PM–2:06 PM
Presenter:
Churna B Bhandari
(Iowa State University)
Authors:
Churna B Bhandari
(Iowa State University)
Gavin N Nop
(Iowa State University)
Durga Paudyal
(Ames National Laboratory)
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.
*This work is supported by the Critical Materials Institute, an Energy Innovation Hub funded bythe U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, AdvancedMaterials & Manufacturing Technologies Office. The Ames National Laboratory is operated forthe U.S. Department of Energy by Iowa State University of Science and Technology underContract No. DE-AC02-07CH11358.
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