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

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. Finding materials with large magnetization is highly desirable for technological applications. Here, a machine learning autonomous search and automated combinatorial synthesis reveal that multi-element alloys with Ir and Pt impurities have a magnetization exceeding the Slater-Pauling limit of Fe75Co25.

Automated summary

The discovery of new magnets with high magnetization is crucial for technological advancements. This study utilized an autonomous materials search system and machine learning to identify multi-element alloys with Ir and Pt impurities exceeding the magnetization limit of Fe3Co1.