Knowing the magnetic structure of crystalline materials is critical to many applications, including data storage, high-resolution imaging, spintronics, superconductivity, and quantum computing. Information of this sort, however, is difficult to come by. Although magnetic structures can be obtained from neutron diffraction and scattering studies, the number of machines that can support these analyses—and the time available at these facilities—is severely limited.
Click here for original story, The task of magnetic classification suddenly looks easier, thanks to machine learning
Source: Phys.org