Automated Critical Temperature Prediction and High-Throughput Search for Composite Quantum Materials

Composite materials with coexisting quantum phases offer exciting opportunities for solid-state device applications and exploring new physics emerging from the interplay between effects including topology, magnetism, and ferroelectricity. However, both the computational design and experimental realization of magnetic topological materials and multiferroics are confounded by the difficulties inherent to predicting and controlling magnetic behavior, which arises from strong electron correlations. We present a workflow to automate the calculation of exchange parameters and critical temperatures with density functional theory and Monte Carlo simulations. We use our recently developed Python Topological Materials package to screen materials for non-trivial band topology. We then apply this approach to suspected magnetic materials in the Materials Project database. By identifying layered materials, we also accelerate the discovery of van der Waals materials with composite topological quantum phases and multiferroic properties. Our method can be used to screen for thermodynamically stable, robust composite quantum materials for solid-state devices and exploring exotic physics like the quantum anomalous Hall effect and axion electrodynamics.