Discovery of rare-earth-free magnetic ternary compounds using machine learning assisted adaptive genetic algorithms

The discovery of rare-earth-free permanent magnets is an active area of research. The absence of rare-earth elements will alleviate a pressing concern about the availability of rare-earth elements used in permanent magnets. These magnets are crucial for applications such as wind turbines, electric cars, and memory devices. Rare-earth magnets are special owing to a large magnetic anisotropy energy (𝐾₁). In contrast, iron cobalt phosphides hold promise since doping P into cubic FeCo can induce anisotropy, leading to a large coercivity, without introducing rare-earth elements. We present a comprehensive search over the Fe-Co-P ternary space for magnets, utilizing recently developed adaptive machine learning feedback to efficiently screen over 850 000 structures. We focus on machine learning acceleration as a paradigm for materials design. Further adaptive genetic algorithm searches and first-principles calculations aid in the identification of 16 new structures below the known convex hull. Five of them possess high magnetic polarization (𝐽𝑠> 1 T). The structures with desirable magnetic properties center on (Fe,Co)₂P. This supports conventional wisdom, which focuses on the mixture of the two known end compounds: Fe₂P and Co₂P. We find Fe₇CoP₄ shows the most promise (𝐽_𝑠=1.03T and 𝐾₁=0.83MJ/m3).