  We use a data-driven approach to study the magnetic and thermodynamic
properties of van der Waals (vdW) layered materials. We investigate monolayers
of the form A$_2$B$_2$X$_6$, based on the known material Cr$_2$Ge$_2$Te$_6$,
using density functional theory (DFT) calculations and machine learning methods
to determine their magnetic properties, such as magnetic order and magnetic
moment. We also examine formation energies and use them as a proxy for chemical
stability. We show that machine learning tools, combined with DFT calculations,
can provide a computationally efficient means to predict properties of such
two-dimensional (2D) magnetic materials. Our data analytics approach provides
insights into the microscopic origins of magnetic ordering in these systems.
For instance, we find that the X site strongly affects the magnetic coupling
between neighboring A sites, which drives the magnetic ordering. Our approach
opens new ways for rapid discovery of chemically stable vdW materials that
exhibit magnetic behavior.
