Predicting interface structures: From SrTiO$_3$ to graphene
Georg Schusteritsch, Chris J. Pickard

TL;DR
This paper introduces a first-principles method using ab initio random structure searching (AIRSS) combined with DFT to predict atomic interface structures, successfully identifying new low-energy configurations in graphene and SrTiO3 interfaces.
Contribution
The paper presents a transferable, efficient, and simple first-principles approach for predicting interface structures applicable to various materials.
Findings
Discovered new low-energy grain boundary structures in graphene.
Identified previously unknown low-energy structures in SrTiO3 with different stoichiometries.
Predicted long-range distortions emanating from the interface into the bulk.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
