Machine learning study of two-dimensional magnetic materials

When the dimensionality of an electron system is reduced, new behavior emerges. This has been demonstrated in GaAs quantum Hall systems since the 1980’s, and more recently in van der Waals (vdW) materials. We discuss the behavior of electrons in reduced dimensions with a focus on their spin properties. We study vdW materials with intrinsic magnetic order, materials at the forefront of physics research. We use materials informatics (machine learning applied to materials science) to study the magnetic and thermodynamic properties of vdW materials. Crystal structures based on monolayer Cr2Ge2Te6, of the form A2B2X6, are studied using density functional theory (DFT) calculations and machine learning tools. Magnetic properties, such as the magnetic moment are determined. The formation energies are also calculated and used to estimate chemical stability. We show that machine learning, combined with DFT, provides a computationally efficient means to predict properties of two-dimensional (2D) magnets. In addition, data analytics provides insights into the microscopic origins of magnetic ordering in 2D. This novel approach to materials research paves the way for the rapid discovery of chemically stable 2D magnets.