Structure Prediction of Epitaxial Inorganic Interfaces by Lattice and Surface Matching with Ogre

TL;DR

This paper introduces an enhanced version of the Ogre Python package that predicts epitaxial inorganic interface structures efficiently using lattice and surface matching, combining geometric scoring with DFT validation.

Contribution

The new Ogre version automates interface structure prediction with lattice and surface matching, incorporating Bayesian optimization and a geometric score function to reduce computational costs.

Findings

Accurately predicts interface structures with reduced DFT calculations.

Successfully applied to Al/InAs and Fe/InSb interfaces.

Streamlines DFT calculations for interface energy and electronic properties.

Abstract

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.