Density functional theory calculations are combined with machine learning to investigate the coverage‐dependent charge transfer at the tetracyanoethylene/Cu(111) hybrid organic/inorganic interface. The study finds two different monolayer phases, which exhibit a qualitatively different charge‐transfer behavior. Our results refute previous theories of long‐range charge transfer to molecules not in direct contact with the surface. Instead, they demonstrate that experimental evidence supports our hypothesis of a coverage‐dependent structural reorientation of the first monolayer. Such phase transitions at interfaces may be more common than currently envisioned, beckoning a thorough reevaluation of organic/inorganic interfaces.

Tuning the transport properties of molecular junctions by chemically modifying the molecular structure is one of the key challenges for advancing the field of molecular electronics. In the present contribution, we investigate current–voltage characteristics of differently linked metal–molecule–metal systems that comprise either a single molecule or a molecular assembly. This is achieved by employing density functional theory in conjunction with a Green’s function approach. We show that the conductance of a molecular system with a specific anchoring group is fundamentally different depending on whether a single molecule or a continuous monolayer forms the junction. This is a consequence of collective electrostatic effects that arise from dipolar elements contained in the monolayer and from interfacial charge rearrangements. As a consequence of these collective effects, the “ideal” choice for an anchoring group is clearly different for monolayer and single molecule devices. A particularly striking effect is observed for pyridine-docked systems. These are subject to Fermi-level pinning at high molecular packing densities, causing an abrupt increase of the junction current already at small voltages.

A microscopic understanding of charge transport through molecule-based systems is essential for advancing the field of molecular electronics. In the present paper we highlight fundamental differences between devices with an individual molecule or a homogeneous monolayer as the active element. These differences arise from collective electrostatic effects that govern the electronic level alignment in monolayer-based devices, but are absent in the case of individual molecules. Employing density functional theory in conjunction with a Green’s function approach, we show that depending on the chemical nature of the employed molecules collective electrostatic effects can result in either a significant increase or a significant decrease of the current per molecule as a function of the packing density, in certain cases even resulting in a change in the transport polarity. Understanding the underlying principles also allows for designing molecules in which dipolar effects cancel, and thus, the transport characteristics of individual molecules and monolayers remain similar.

Structure determination and prediction pose a major challenge to computational material science, demanding efficient global structure search techniques tailored to identify promising and relevant candidates. A major bottleneck is the fact that due to the many combinatorial possibilities, there are too many possible geometries to be sampled exhaustively. Here, an innovative computational approach to overcome this problem is presented that explores the potential energy landscape of commensurate organic/inorganic interfaces where the orientation and conformation of the molecules in the tightly packed layer is close to a favorable geometry adopted by isolated molecules on the surface. It is specifically designed to sample the energetically lowest lying structures, including the thermodynamic minimum, in order to survey the particularly rich and intricate polymorphism in such systems. The approach combines a systematic discretization of the configuration space, which leads to a huge reduction of the combinatorial possibilities with an efficient exploration of the potential energy surface inspired by the Basin-Hopping method. Interfacing the algorithm with first-principles calculations, the power and efficiency of this approach is demonstrated for the example of the organic molecule TCNE (tetracyanoethylene) on Au(111). For the pristine metal surface, the global minimum structure is found to be at variance with the geometry found by scanning tunneling microscopy. Rather, our results suggest the presence of surface adatoms or vacancies that are not imaged in the experiment.

The rich polymorphism exhibited by inorganic/organic interfaces is a major challenge for materials design. In this work we present a method to efficiently explore the potential energy surface and predict the formation energies of polymorphs and defects. This is achieved by training a machine learning model on a list of only 100 candidate structures that are evaluated via dispersion-corrected Density Functional Theory (DFT) calculations. We demonstrate the power of this approach for tetracyanoethylene on Ag(100) and explain the anisotropic ordering that is observed experimentally.