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 inplane 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.
At an interface between two materials physical properties and functionalities may be achieved, which would not exist in either material alone. Epitaxial inorganic interfaces are at the heart of semiconductor, spintronic, and quantum devices. First principles simulations based on density functional theory (DFT) can help elucidate the electronic and magnetic properties of interfaces and relate them to the structure and composition at the atomistic scale. Furthermore, DFT simulations can predict the structure and properties of candidate interfaces and guide experimental efforts in promising directions. However, DFT simulations of interfaces can be technically elaborate and computationally expensive. To help researchers embarking on such simulations, this review covers best practices for first principles simulations of epitaxial inorganic interfaces, including DFT methods, interface model construction, interface structure prediction, and analysis and visualization tools.
Highly ordered epitaxial
interfaces between organic semiconductors
are considered as a promising avenue for enhancing the performance
of organic electronic devices including solar cells and transistors,
thanks to their well-controlled, uniform electronic properties and
high carrier mobilities. The electronic structure of epitaxial organic
interfaces and their functionality in devices are inextricably linked
to their structure. We present a method for structure prediction of
epitaxial organic interfaces based on lattice matching followed by
surface matching, implemented in the open-source Python package, Ogre.
The lattice matching step produces domain-matched interfaces, where
commensurability is achieved with different integer multiples of the
substrate and film unit cells. In the surface matching step, Bayesian
optimization (BO) is used to find the interfacial distance and registry
between the substrate and film. The BO objective function is based
on dispersion corrected deep neural network interatomic potentials.
These are shown to be in qualitative agreement with density functional
theory (DFT) regarding the optimal position of the film on top of
the substrate and the ranking of putative interface structures. Ogre
is used to investigate the epitaxial interface of 7,7,8,8-tetracyanoquinodimethane
(TCNQ) on tetrathiafulvalene (TTF), whose electronic structure has
been probed by ultraviolet photoemission spectroscopy (UPS), but whose
structure had been hitherto unknown [Organic Electronics
2017, 48, 371]. We find that TCNQ(001)
on top of TTF(100) is the most stable interface configuration, closely
followed by TCNQ(010) on top of TTF(100). The density of states, calculated
using DFT, is in excellent agreement with UPS, including the presence
of an interface charge transfer state.
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