Towards autonomous prediction and synthesis of novel magnetic materials
https://www.sciencedaily.com/releases/2022/07/220707100912.htm
Materials scientists are constantly on the lookout for new "functional materials" with favorable properties directed towards some application. For instance, finding novel functional magnetic materials could open doors to energy-efficient spintronic devices. In recent years, the development of spintronics devices like magnetoresistive random access memory -- an electronic device in which a single magnetoresistive element is integrated as one bit of information -- has been progressing rapidly, for which magnetic materials with high magnetocrystalline anisotropy (MCA) are required. Ferromagnetic materials, which retain their magnetization without an external magnetic field, are of particular interest as data storage systems, therefore. For instance, L10-type ordered alloys consisting of two elements and two periods, such as L10-FeCo and L10-FeNi, have been studied actively as promising candidates for next-generation functional magnetic materials. However, the combination of constituent elements is extremely limited, and materials with extended element type, number, and periodicity have rarely been explored.
What impedes this exploration? Scientists point at combinatorial explosions that can occur easily in multilayered films, requiring a great deal of time and effort in the selection of the constituent elements and material fabrication, as the major reason. Besides, it is extremely difficult to predict the function of MCA because of the complex interplay of various parameters including crystal structure, magnetic moment, and electronic state, and the conventional protocol relies largely on trial and error. Thus, there is much scope and need for developing an efficient route to discovering new high-performance magnetic materials.