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.