Data-driven studies of the magnetic anisotropy of two-dimensional magnetic materials

Two-dimensional materials with intrinsic ferromagnetic order are at the forefront of condensed matter research. How many of these materials exist in nature? What is the relationship between thier crystal structure and magnetic properties? Remarkably, atomically-thin magnetic structures can exhibit novel spin properties which do not exist in the corresponding bulk materials. We use first-principles calculations, based on density functional theory, and machine learning to study the magnetocrystalline anisotropy of monolayer transition metal trichalcogenides of the form A₂B₂X₆. That is, we created permutations of the chemical composition of the ferromagnetic semiconductor Cr₂Ge₂Te₆. Specifically, we identify trends in their magnetocrystalline anisotropy data. We find that the X site dominates the machine learning prediction of the magnetocrystalline anisotropy of an A₂B₂X₆ monolayer. Our data-driven study aims to uncover physical insights into the microscopic origins of magnetism in reduced dimensions and to demonstrate the success of a high-throughput computational approach for the targeted design of quantum materials with potential applications from sensing to data storage.