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Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials by Prashant Singh; Tyler Del Rose; Andriy Palasyuk; Yaroslav Mudryk is a Materials Science article available to read on EtoBox.

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High-performance permanent magnets with a high Curie temperature, containing less critical materials, are integral to zero-carbon energy solutions. We built a machine-learning model trained over available experimentally measured Curie temperature values to predict the T C of multicomponent magnetic materials. We chose two compositions from a pseudo-binary (Zr 1-x Ce x )Fe 2 system, namely, (Zr 0.16 Ce 0.84 )Fe 2 and (Zr 0.94 Ce 0.06 )Fe 2 , to experimentally validate the ability of our model to predict the Curie temperature of novel compounds. We also provided a detailed discussion on the correlation of the Curie temperature with the de Gennes scaling factor in rare-earth intermetallic compounds and its breakdown below a certain rare-earth content. The electronic structure calculations (density of states and Fermi surface) were performed using the density functional theory on selected compounds (Zr 0.16 Ce 0.84 )Fe 2 and (Zr 0.94 Ce 0.06 )Fe 2 to understand the electronic origin of a strong magnetic exchange. We found that the change in the electronic density of states and electron/hole fillings at the Fermi level directly correlate with the Curie temperature. Notably, our model wa

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Author

Prashant Singh; Tyler Del Rose; Andriy Palasyuk; Yaroslav Mudryk

Publisher

American Chemical Society (ACS)

Published

2023

Language

EN

Field

Materials Science (Physical Sciences)