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• W4385487968 abstract "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 TC of multicomponent magnetic materials. We chose two compositions from a pseudo-binary (Zr1–xCex)Fe2 system, namely, (Zr0.16Ce0.84)Fe2 and (Zr0.94Ce0.06)Fe2, 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 (Zr0.16Ce0.84)Fe2 and (Zr0.94Ce0.06)Fe2 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 was able to capture these key electronic structure trends, which show that physics-informed machine learning can play a crucial role in designing new high-performance magnets with improved properties for environmentally sustainable applications." @default.
• W4385487968 created "2023-08-03" @default.
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• W4385487968 date "2023-08-02" @default.
• W4385487968 modified "2023-09-26" @default.
• W4385487968 title "Physics-Informed Machine-Learning Prediction of Curie Temperatures and Its Promise for Guiding the Discovery of Functional Magnetic Materials" @default.
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• W4385487968 doi "https://doi.org/10.1021/acs.chemmater.3c00892" @default.
• W4385487968 hasPublicationYear "2023" @default.
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