Magnetic iron-cobalt silicides discovered using machine-learning

We employ a machine learning (ML) framework coupled with first principles calculations to discover rare-earth-free magnetic iron-cobalt silicide compounds. Deep machine learning models are used to screen over 350,000 hypothetical structures to extract promising a small subset of structures and compositions for further studies by first-principles calculations. We use an adaptive genetic algorithm to search for new lower energy structures based on the promising chemical compositions. This ML-guided approach considerably accelerates the pace of materials discovery. Our study discovered five new ternary Fe-Co-Si compounds that exhibit high magnetization (Js > 1.0 Tesla), easy-axis magnetic anisotropy (K1 ≥ 1.0 MJ/m^3), and Curie temperature (Tc > 840 K). The formation energies of these compounds are within 70 meV/atom relative to the ternary convex hull, suggesting that these compounds could be synthesized.