Informatics Study of Magnetic Two-Dimensional Materials

When the dimensionality of an electron system is reduced from three-dimensions to two-dimensions, new behavior emerges. This has been demonstrated in gallium arsenide quantum Hall systems since the 1980’s, and more recently in van der Waals (vdW) materials, such as graphene. This talk will discuss the behavior of electrons in reduced dimensions with a focus on their spin properties. We highlight our recent study of vdW materials with intrinsic magnetic order.  These materials are at the forefront of condensed matter physics research. We use a materials informatics (machine learning applied to materials research) approach to study the magnetic properties and chemical stability of vdW materials. Crystal structures based on monolayer Cr₂Ge₂Te₆, of the form A₂B₂X₆, are studied using density functional theory (DFT) calculations and machine learning methods. Magnetic properties, such as the magnetic moment are determined. The formation energies are also calculated and used to estimate the chemical stability. We show that machine learning methods, combined with DFT, can provide a computationally efficient means to predict properties of two-dimensional (2D) magnetic materials. In addition, data analytics provides novel insights into the microscopic origins of magnetic ordering in two dimensions. Analysis of DFT data highlights that the X site strongly affects the magnetic coupling between neighboring A sites - driving magnetic ordering. This novel approach to materials research paves the way for the rapid discovery of magnetic 2D materials that are chemically stable.