First-principles calculations are accurate but expensive. We train machine-learning models on their results to predict electronic and structural properties much faster, and to explore large families of materials.
We are interested in models that respect the symmetries of the crystal, in estimating how reliable their predictions are, and in using them to guide theoretical work on quantum materials.