Data-driven design of B20 alloys with targeted magnetic properties guided by machine learning and density functional theory

Chiral magnets in the B20 crystal structure host a peculiar spin texture in the form of a topologically stable skyrmion lattice. However, the helical transition temperature (T-C) of these compounds is below room temperature, which limits their potential in spintronics applications. Here, a data-driven approach is demonstrated, which integrates density functional theory (DFT) calculations with machine learning (ML) in search of alloying elements that will enhance the T-C of known B20 compounds. Initial DFT screening led to the identification of chromium (Cr) and tin (Sn) as potential substituents for alloy design. Then, trained ML models predict Sn substitution to be more promising than Cr-substitution for tuning the T-C of FeGe. The magnetic exchange energy calculated from DFT validates the promise of Sn as an effective alloying element for enhancing the T-C in Fe(Ge,Sn) compounds. New B20 chiral magnets are recommended for experimental investigation.