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Advances in hybrid organic/inorganic architectures for optoelectronics can be achieved by understanding how the atomic and electronic degrees of freedom cooperate or compete to yield the desired functional properties. Here we show how work-function changes are modulated by the structure of the organic components in model hybrid systems. We consider two cyano-quinodimethane derivatives (F4-TCNQ and F6-TCNNQ), which are strong electron-acceptor molecules, adsorbed on H-Si(111). From systematic structure searches employing range-separated hybrid HSE06 functional including many body van der Waals contributions, we predict that despite their similar composition, these molecules adsorb with significantly different densely-packed geometries in the first layer, due to strong intermolecular interaction. F6-TCNNQ shows a much stronger intralayer interaction (primarily due to van der Waals contributions) than F4-TCNQ in multilayered structures. The densely-packed geometries induce a large interface-charge rearrangement that result in a work-function increase of 1.11 and 1.76 eV for F4-TCNQ and F6-TCNNQ, respectively. Nuclear fluctuations at room temperature produce a wide distribution of work-function values, well modeled by a normal distribution with {sigma}=0.17 eV. We corroborate our findings with experimental evidence of pronounced island formation for F6-TCNNQ on H-Si(111) and with the agreement of trends between predicted and measured work-function changes.
Using in situ low-energy electron microscopy and density functional theory, we studied the growth structure and work function of bilayer graphene on Pd(111). Low-energy electron diffraction analysis established that the two graphene layers have multi
We present a systematic study of the atomic and electronic structure of the Si(111)-(5x2)-Au reconstruction using first-principles electronic structure calculations based on the density functional theory. We analyze the structural models proposed by
The nature of the atomic defects on the hydrogen passivated Si (100) surface is analyzed using deep learning and scanning tunneling microscopy (STM). A robust deep learning framework capable of identifying atomic species, defects, in the presence of
We demonstrate that it is possible to mechanically exfoliate graphene under ultra high vacuum conditions on the atomically well defined surface of single crystalline silicon. The flakes are several hundred nanometers in lateral size and their optical
Organic semiconductors exhibit properties of individual molecules and extended crystals simultaneously. The strongly bound excitons they host are typically described in the molecular limit, but excitons can delocalize over many molecules, raising the