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. The discovery of vdW materials with intrinsic magnetic order in 2017 has given rise to
new avenues for the study of emergent phenomena in reduced dimensions. These materials are at
the forefront of condensed matter physics research. How many vdW magnetic materials exist in
nature? What are their properties? How do these properties change with the number of layers? A
conservative estimate for the number of candidate vdW materials (including monolayers,
bilayers and trilayers) exceeds ~106
. In this talk, we will use materials informatics (machine
learning combined with materials science) as a tool to efficiently explore this large materials
space and attempt to discover magnetic vdW materials with desirable spin properties. We will
focus on crystal structures based on monolayer Cr2Ge2Te6, of the form A2B2X6, which 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 vdW magnetic materials. In addition, data analytics provides insights into the
microscopic origins of magnetic ordering in two dimensions. We also explore how our study of
magnetic monolayers [1] can be extended, with proper modification, to vdW materials with both
magnetic and topological order. This non-traditional approach to materials research paves the
way for the rapid discovery of magnetic vdW materials with possible applications in data
storage, spintronics and quantum computing.
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