  Searching the optimal doping compositions of the Y-type hexaferrite
Ba2Mg2Fe12O22 remains a long-standing challenge for enhanced non-collinear
magnetic transition temperature (TNC). Instead of the conventional
trial-and-error approach, the composition-property descriptor is established
via a data driven machine learning method named SISSO (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 TNC, among which the
BaSrMg0.28Co1.72Fe10Al2O22 is then experimentally validated. Test results
indicate that, under appropriate external magnetic field conditions, the TNC of
this composition reaches up to 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.
