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Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit

Physics

Machine learning autonomous identification of magnetic alloys beyond the Slater-Pauling limit

Y. Iwasaki, R. Sawada, et al.

Discover a groundbreaking magnetic alloy that surpasses the known limits of magnetization, achieved through advanced machine learning and ab-initio calculations by authors Yuma Iwasaki, Ryohto Sawada, Eiji Saitoh, and Masahiko Ishida. The inclusion of Ir and Pt impurities has unveiled unprecedented magnetization properties, confirmed through experimental synthesis.

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Playback language: English
Abstract
This paper presents the discovery of a magnetic alloy with magnetization exceeding that of Fe3Co1, the previously known limit defined by the Slater-Pauling rule. This was achieved using an autonomous materials search system that combines machine learning and ab-initio calculations. The system, after six weeks of exploration, unexpectedly identified Ir and Pt impurities as enhancing the magnetization of FeCo alloys. Experimental synthesis and characterization confirmed the enhanced magnetization in FeₓCoᵧIr₁₋ₓ₋ᵧ and FeₓCoᵧPt₁₋ₓ₋ᵧ alloys.
Publisher
Communications Materials
Published On
Jan 01, 2021
Authors
Yuma Iwasaki, Ryohto Sawada, Eiji Saitoh, Masahiko Ishida
Tags
magnetic alloy
magnetization
machine learning
ab-initio calculations
FeCo alloys
Ir impurities
Pt impurities

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