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In this work, we conducted machine learning on an experimental dataset of SmFe12-based alloys with ThMn12-type crystal structure. The dataset comprised 908 samples collected from articles and our own experiments on the alloys which were obtained either by mechanical alloying or by melt-spinning followed by heat treatment. The descriptor of each sample consisted of the chemical composition and synthesis details. The importance of the features and their correlations were analyzed, then two gradient boosting regressors were trained to predict coercivity and remanence as the main targets. Next, a large feature space of (Sm,Zr)x(Fe,Ti,V)100-x melt spun samples with annealing temperatures ranging from 923 to 1373 K was examined to define a Pareto front of the competing targets. Finally, we proposed a list of the most prospective alloys for an experimental validation, the results of which will be reported, as well as other details of the machine learning on the SmFe12-based isotropic alloys.

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Keyword: Machine learning, Hard magnetic materials, SmFe12-based alloys

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Conference: IEEE Magnetic Frontiers Conference  (2024-09-15 - 2024-09-19)

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Manuscript type: Not a journal article

MDR DOI: https://doi.org/10.48505/nims.4857

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Updated at: 2024-10-17 08:30:37 +0900

Published on MDR: 2024-10-17 08:30:37 +0900