Abstract
Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. Currently, the binary alloy Fe3Co1 exhibits the largest magnetization of any stable alloys explained by the Slater-Pauling rule. A multi-element system is expected to include alloys with magnetization beyond that of Fe3Co1, but it has been difficult to identify appropriate elements and compositions because of combinatorial explosion. In this work, we identified an alloy with magnetization beyond that of Fe3Co1 by using an autonomous materials search system combining machine learning and ab-initio calculation. After an autonomous and automated exploration in the large material space of multi-element alloys for six weeks, the system unexpectedly indicated that Ir and Pt impurities would enhance the magnetization of FeCo alloys, despite both impurity elements having small magnetic moments. To confirm this experimentally, we synthesized FexCoyIr1-x-y and FexCoyPt1-x-y alloys and found that some of them have magnetization beyond that of Fe3Co1.
Highlights
Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices
Some of metastable alloys, where the crystal structure and lattice constant are fixed by force, exhibit the high magnetization
Machine learning is used to statistically decide the target material from the data obtained in step II′, with consideration of the trade-off between exploration and exploitation by using an upper confidential bound strategy (UCB)[31]
Summary
Discovery of new magnets with high magnetization has always been important in human history because it has given birth to powerful motors and memory devices. After an autonomous and automated exploration in the large material space of multi-element alloys for six weeks, the system unexpectedly indicated that Ir and Pt impurities would enhance the magnetization of FeCo alloys, despite both impurity elements having small magnetic moments. Machine learning is used to statistically decide the target material from the data obtained in step II′, with consideration of the trade-off between exploration and exploitation by using an upper confidential bound strategy (UCB)[31]. In other words, these machine learning techniques are adjusted to select a target material with a better property from materials dissimilar to those that have already been tried in steps I′ and II′. Since the amount of learning data increases with repetition of this automated cycle, the machine learning model gradually improves and suggests better materials
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