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arXiv · 2207.00456

Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features

Anoop Kini·Amit Kumar Choudhary·Dominic Hohs·Andreas Jansche·Hermann Baumgartl·Ricardo Buettner·Timo Bernthaler·Dagmar Goll·Gerhard Schneider
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Abstract

The Fe14Nd2B-based permanent magnets are technologically sought-after for energy conversion due to their unparalleled high energy product (520 kJ/m3). For such 14:2:1 phases of different compositions, determining the magnetization from the measured magnetic moment is often bottlenecked by lack of mass density. We present a machine learning (ML) mass density model for 14:2:1 phases using chemical composition-based features (representing 33 elements) and optionally lattice parameter (LP) features. The datasets for training and testing contain 190 phases (177 compositionally different) with their literature reported densities and LP. With an ML model with merely compositional features, we achieved a low mean-absolute-error of 0.51% on an unseen test-dataset.

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Anoop Kini, Amit Kumar Choudhary, Dominic Hohs, Andreas Jansche, Hermann Baumgartl, Ricardo Buettner, Timo Bernthaler, Dagmar Goll, Gerhard Schneider. 2022-07-01. Machine learning-based mass density model for hard magnetic 14:2:1 phases using chemical composition-based features. https://doi.org/10.1016/j.cplett.2022.140231

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