Optimization of noncollinear magnetic ordering temperature in Y-type hexaferrite by machine learning

Searching the optimal doping compositions of the Y-type hexaferrite Ba2Mg2Fe12O22 remains a long-standing challenge for enhanced non-collinear magnetic transition temperature (T-NC). Instead of the conventional trial-and-error approach, the composition-property descriptor is established via a data driven machine learning method named sure independence screening and sparsifying operator. Based on the chosen efficient and physically interpretable descriptor, a series of Y-type hexaferrite compositions are predicted to hold high T-NC, among which the BaSrMg0.28Co1.72Fe10Al2O22 is then experimentally validated. Test results indicate that, under appropriate external magnetic field conditions, the T-NC of this composition reaches up to 568 K, and its magnetic transition temperature is also elevated to 735 K. This work offers a machine learning-based route to develop room temperature single phase multiferroics for device applications.