Predicting interface structures: From SrTiO${}_3$ to graphene

Abstract

We present here a fully first-principles method for predicting the atomic structure of interfaces. Our method is based on the {\it ab initio} random structure searching (AIRSS) approach, applied here to treat two dimensional defects. The method relies on repeatedly generating random structures in the vicinity of the interface and relaxing them within the framework of density functional theory (DFT). The method is simple, requiring only a small set of parameters that can be easily connected to the chemistry of the system of interest, and efficient, ideally adapted to high-throughput first-principles calculations on modern parallel architectures. Being first-principles, our method is transferable, an important requirement for a generic computational method for the determination of the structure of interfaces. Results for two structurally and chemically very different interfaces are presented here, grain boundaries in graphene and grain boundaries in strontium titanate (SrTiO${}_3$). We successfully find a previously unknown low energy grain boundary structure for the graphene system, as well as recover the previously known higher energy structures. For the SrTiO${}_3$ system we study both stoichiometric and non-stoichiometric compositions near the grain boundary and find previously unknown low energy structures for all stoichiometries. We predict that these low energy structures have long-range distortions to the ground state crystal structure emanating into the bulk from the interface.

17 pages, 5 figures; corrected typos and corrected references

References in corpus (10)

• Ab initio Random Structure Searching
• Electronic transport in polycrystalline graphene
• High-pressure phases of silane
• Cones, pringles, and grain boundary landscapes in graphene topology
• Persistence and eventual demise of oxygen molecules at terapascal pressures
• Hydrogen/silicon complexes in silicon from computational searches
• What Is Armchair-Zigzag Grain Boundary Structure in Graphene?
• Hydrogen/nitrogen/oxygen defect complexes in silicon from computational searches
• Lithiation of silicon via lithium Zintl-defect complexes
• Understanding the Clean Interface between Covalent Si and Ionic Al2O3

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• Surface Phase Diagrams from Nested Sampling
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• Boundary Plane-Oriented Grain Boundary Model Generation
• RAFFLE: Active learning accelerated interface structure prediction