  We present a new version of the Ogre open source Python package with the
capability to perform structure prediction of epitaxial inorganic interfaces by
lattice and surface matching. In the lattice matching step a scan over
combinations of substrate and film Miller indices is performed to identify the
domain-matched interfaces with the lowest mismatch. Subsequently, surface
matching is conducted by Bayesian optimization to find the optimal interfacial
distance and in-plane registry between the substrate and film. For the
objective function, a geometric score function is proposed, based on the
overlap and empty space between atomic spheres at the interface. The score
function reproduces the results of density functional theory (DFT) at a
fraction of the computational cost. The optimized interfaces are pre-ranked
using a score function based on the similarity of the atomic environment at the
interface to the bulk environment. Final ranking of the top candidate
structures is performed with DFT. Ogre streamlines DFT calculations of
interface energies and electronic properties by automating the construction of
interface models. The application of Ogre is demonstrated for two interfaces of
interest for quantum computing and spintronics, Al/InAs and Fe/InSb.
