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Highly ordered epitaxial interfaces between organic semiconductors are considered as a promising avenue for enhancing the performance of organic electronic devices including solar cells, light emitting diodes, and transistors, thanks to their well-controlled, uniform electronic properties and high carrier mobilities. Although the phenomenon of organic epitaxy has been known for decades, computational methods for structure prediction of epitaxial organic interfaces have lagged far behind the existing methods for their inorganic counterparts. 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, shown to be in excellent agreement with density functional theory (DFT). The application of Ogre is demonstrated for an 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 48, 371 (2017)]. 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.

Majorana zero modes, with prospective applications in topological quantum computing, are expected to arise in superconductor/semiconductor interfaces, such as $\beta$-Sn and InSb. However, proximity to the superconductor may also adversely affect the semiconductor's local properties. A tunnel barrier inserted at the interface could resolve this issue. We assess the wide band gap semiconductor, CdTe, as a candidate material to mediate the coupling at the lattice-matched interface between $\alpha$-Sn and InSb. To this end, we use density functional theory (DFT) with Hubbard U corrections, whose values are machine-learned via Bayesian optimization (BO) [npj Computational Materials 6, 180 (2020)]. The results of DFT+U(BO) are validated against angle resolved photoemission spectroscopy (ARPES) experiments for $\alpha$-Sn and CdTe. For CdTe, the z-unfolding method [Advanced Quantum Technologies, 5, 2100033 (2022)] is used to resolve the contributions of different $k_z$ values to the ARPES. We then study the band offsets and the penetration depth of metal-induced gap states (MIGS) in bilayer interfaces of InSb/$\alpha$-Sn, InSb/CdTe, and CdTe/$\alpha$-Sn, as well as in tri-layer interfaces of InSb/CdTe/$\alpha$-Sn with increasing thickness of CdTe. We find that 16 atomic layers (3.5 nm) of CdTe can serve as a tunnel barrier, effectively shielding the InSb from MIGS from the $\alpha$-Sn. This may guide the choice of dimensions of the CdTe barrier to mediate the coupling in semiconductor-superconductor devices in future Majorana zero modes experiments.

We present a first-principles study of the electronic and magnetic properties of epitaxial interfaces between the Heusler compounds Ti$_2$MnIn and Ni$_2$MnIn and the III-V semiconductors, InSb and InAs, respectively. We use density functional theory (DFT) with a machine-learned Hubbard $U$ correction determined by Bayesian optimization. We evaluate these interfaces for prospective applications in Majorana-based quantum computing and spintronics. In both interfaces, states from the Heusler penetrate into the gap of the semiconductor, decaying within a few atomic layers. The magnetic interactions at the interface are weak and local in space and energy. Magnetic moments of less than 0.1 $\mu_B$ are induced in the two atomic layers closest to the interface. The induced spin polarization around the Fermi level of the semiconductor also decays within a few atomic layers. The decisive factor for the induced spin polarization around the Fermi level of the semiconductor is the spin polarization around the Fermi level in the Heusler, rather than the overall magnetic moment. As a result, the ferrimagnetic narrow-gap semiconductor Ti$_2$MnIn induces a more significant spin polarization in the InSb than the ferromagnetic metal Ni$_2$MnIn induces in the InAs. This is explained by the position of the transition metal $d$ states in the Heusler with respect to the Fermi level. Based on our results, these interfaces are unlikely to be useful for Majorana devices but could be of interest for spintronics.

We combined in-situ scanning tunneling microscopy (STM) with the conventional growth characterization methods of atomic force microscopy (AFM) and reflection high energy electron diffraction (RHEED) to simultaneously assess atomic-scale impurities and the larger-scale surface morphology of molecular beam epitaxy (MBE) grown homoepitaxial InAs(111)A. By keeping a constant substrate temperature and indium flux while increasing the As$_2$ flux, we find two differing MBE growth parameter regions for optimized surface roughness on the macro and atomic scale. In particular, we show that a pure step-flow regime with strong suppression of hillock formation can be achieved, even on substrates without intentional offcut. On the other hand, an indium adatom deficient, low atomic defect surface can be observed for a high hillock density. We identify the main remaining point defect on the latter surface by comparison to STM simulations. Furthermore, we provide a method for extracting root-mean-square surface roughness values and discuss their use for surface quality optimization by comparison to scale-dependent, technologically relevant surface metrics. Finally, mapping the separately optimized regions of the growth parameter space should provide a guide for future device engineering involving epitaxial InAs(111)A growth.

Recently, the EuS/InAs interface has attracted attention for the possibility of inducing magnetic exchange correlations in a strong spin-orbit semiconductor, which could be useful for topological quantum devices. We use density functional theory (DFT) with a machine-learned Hubbard $U$ correction [npj Comput. Mater. 6, 180 (2020)] to elucidate the effect of the bonding configuration at the interface on the electronic structure. For all interface configurations considered here, we find that the EuS valence band maximum (VBM) lies below the InAs VBM. In addition, dispersed states emerge at the top of the InAs VBM at the interface, which do not exist in either material separately. These states are contributed mainly by the InAs layer adjacent to the interface. They are localized at the interface and may be attributed to charge transfer from the EuS to the InAs. The interface configuration affects the position of the EuS VBM with respect to the InAs VBM, as well as the dispersion of the interface state. For all interface configurations studied here, the induced magnetic moment in the InAs is small. This suggests that this interface, in its coherent form studied here, is not promising for inducing equilibrium magnetic properties in InAs.

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.

First principles simulations are conducted to shed light on the question of whether a two-dimensional topological insulator (2DTI) phase may be obtained at the interface between InAs and GaSb. To this end, the InAs/GaSb interface is compared and contrasted with the HgTe/CdTe interface. Density functional theory (DFT) simulations of these interfaces are performed using a machine-learned Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. For the HgTe/CdTe interface our simulations show that band crossing is achieved and an inverted gap is obtained at a critical thickness of 5.1 nm of HgTe, in agreement with experiment and previous DFT calculations. In contrast, for InAs/GaSb the gap narrows with increasing thickness of InAs; however the gap does not close for interfaces with up to 50 layers (about 15 nm) of each material. When an external electric field is applied across the InAs/GaSb interface, the GaSb-derived valence band maximum is shifted up in energy with respect to the InAs-derived conduction band minimum until eventually the bands cross and an inverted gap opens. Our results show that it may be possible to reach the topological regime at the InAs/GaSb interface under the right conditions. However, it may be challenging to realize these conditions experimentally, which explains the difficulty of experimentally demonstrating an inverted gap in InAs/GaSb.

The electronic structure of surfaces plays a key role in the properties of quantum devices. However, surfaces are also the most challenging to simulate and engineer. Here, we study the electronic structure of InAs(001), InAs(111), and InSb(110) surfaces using a combination of density functional theory (DFT) and angle-resolved photoemission spectroscopy (ARPES). We were able to perform large-scale first principles simulations and capture effects of different surface reconstructions by using DFT calculations with a machine-learned Hubbard U correction [npj Comput. Mater. 6, 180 (2020)]. To facilitate direct comparison with ARPES results, we implemented a "bulk unfolding" scheme by projecting the calculated band structure of a supercell surface slab model onto the bulk primitive cell. For all three surfaces, we find a good agreement between DFT calculations and ARPES. For InAs(001), the simulations clarify the effect of the surface reconstruction. Different reconstructions are found to produce distinctive surface states. For InAs(111) and InSb(110), the simulations help elucidate the effect of oxidation. Owing to larger charge transfer from As to O than from Sb to O, oxidation of InAs(111) leads to significant band bending and produces an electron pocket, whereas oxidation of InSb(110) does not. Our combined theoretical and experimental results may inform the design of quantum devices based on InAs and InSb semiconductors, e.g., topological qubits utilizing the Majorana zero modes.

We use density functional theory calculations to study the electronic structure of epitaxial (111) interfaces of the topological crystalline insulators SnSe and SnTe with the magnetic insulator EuS and the non-magnetic insulator CaTe, respectively. We consider both interface slab models with a vacuum region and periodic heterostructures without vacuum. We find that gaps of 21 meV at the $\Gamma$ point and 9 meV at the M point arise in the topological state at the SnSe/EuS interface, due to the magnetic proximity effect, which breaks the time reversal symmetry. The surface state at $\Gamma$ is shifted below the Fermi level by 88 meV and the surface state at M is shifted above the Fermi level by 47 meV, owing to band bending at the interface. By comparison, the topological state at the interface of SnTe/CaTe is unperturbed by the presence of non-magnetic CaTe.

Genarris is an open-source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. An MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. For all polymorphs, the final pool either contained the experimental structure, or structures with similar lattice parameters, symmetry, and packing motifs.

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.

We present the implementation of GAtor, a massively parallel, first principles genetic algorithm (GA) for molecular crystal structure prediction. GAtor is written in Python and currently interfaces with the FHI-aims code to perform local optimizations and energy evaluations using dispersion-inclusive density functional theory (DFT). GAtor offers a variety of fitness evaluation, selection, crossover, and mutation schemes. Breeding operators designed specifically for molecular crystals provide a balance between exploration and exploitation. Evolutionary niching is implemented in GAtor by using machine learning to cluster the dynamically updated population by structural similarity and then employing a cluster-based fitness function. Evolutionary niching promotes uniform sampling of the potential energy surface by evolving several sub-populations, which helps overcome initial pool biases and selection biases (genetic drift). The various settings offered by GAtor increase the likelihood of locating numerous low-energy minima, including those located in disconnected, hard to reach regions of the potential energy landscape. The best structures generated are re-relaxed and re-ranked using a hierarchy of increasingly accurate DFT functionals and dispersion methods. GAtor is applied to a chemically diverse set of four past blind test targets, characterized by different types of intermolecular interactions. The experimentally observed structures and other low-energy structures are found for all four targets. In particular, for Target II, 5-cyano-3-hydroxythiophene, the top ranked putative crystal structure is a $Z^\prime$=2 structure with P$\bar{1}$ symmetry and a scaffold packing motif, which has not been reported previously.

Singlet fission (SF), the spontaneous down-conversion of a singlet exciton into two triplet excitons residing on neighboring molecules, is a promising route to improving organic photovoltaic (OPV) device efficiencies by harvesting two charge carriers from one photon. However, only a few materials have been discovered that exhibit intermolecular SF in the solid state, most of which are acene derivatives. Recently, there has been a growing interest in rylenes as potential SF materials. We use many-body perturbation theory in the GW approximation and the Bethe-Salpeter equation (BSE) to investigate the possibility of intermolecular SF in crystalline perylene and quaterrylene. A new method is presented for determining the percent charge transfer character (%CT) of an exciton wave-function from double-Bader analysis. This enables relating exciton probability distributions to crystal packing. Based on comparison to known and predicted SF materials with respect to the energy conservation criterion (ES-2ET) and %CT, crystalline quaterrylene is a promising candidate for intermolecular SF. Furthermore, quaterrylene is attractive for OPV applications thanks to its high stability and narrow optical gap. Perylene is not expected to exhibit SF, however it is a promising candidate for harvesting sub-gap photons by triplet-triplet annihilation.

In order to design clusters with desired properties, we have implemented a suite of genetic algorithms tailored to optimize for low total energy, high vertical electron affinity (VEA), and low vertical ionization potential (VIP). Applied to (TiO$_2$)$_n$ clusters, the property-based optimization reveals the underlying structure-property relations and the structural features that may serve as active sites for catalysis. High VEA and low VIP are correlated with the presence of several dangling-O atoms and their proximity, respectively. We show that the electronic properties of (TiO$_2$)$_n$ up to n=20 correlate more strongly with the presence of these structural features than with size., Comment: 4 figs, 5 pages