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

Iwasaki, Yuma*; Saito, Eiji; 2 of others*

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 FeCo 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 FeCo, 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 FeCo 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 FexCoyIr and FexCoyPt alloys and found that some of them have magnetization beyond that of FeCo.