  Magnetic topological insulators and semi-metals have a variety of properties
that make them attractive for applications including spintronics and quantum
computation, but very few high-quality candidate materials are known. In this
work, we use systematic high-throughput density functional theory calculations
to identify magnetic topological materials from 40000 three-dimensional
materials in the JARVIS-DFT database (https://jarvis.nist.gov/jarvisdft).
First, we screen materials with net magnetic moment > 0.5 {\mu}B and spin-orbit
spillage > 0.25, resulting in 25 insulating and 564 metallic candidates. The
spillage acts as a signature of spin-orbit induced band-inversion. Then, we
carry out calculations of Wannier charge centers, Chern numbers, anomalous Hall
conductivities, surface bandstructures, and Fermi-surfaces to determine
interesting topological characteristics of the screened compounds. We also
train machine learning models for predicting the spillage, bandgaps, and
magnetic moments of new compounds, to further accelerate the screening process.
We experimentally synthesize and characterize a few candidate materials to
support our theoretical predictions.
