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.

In this publication we introduce SAMPLE, a structure search approach for commensurate organic monolayers on inorganic substrates. Such monolayers often show rich polymorphism with diverse molecular arrangements in differently shaped unit cells. Determining the different commensurate polymorphs from first principles poses a major challenge due to the large number of possible molecular arrangements. To meet this challenge, SAMPLE employs coarse-grained modeling in combination with Bayesian linear regression to efficiently map the minima of the potential energy surface. In addition, it uses ab initio thermodynamics to generate phase diagrams. Using the example of naphthalene on Cu(111), we comprehensively explain the SAMPLE approach and demonstrate its capabilities by comparing the predicted with the experimentally observed polymorphs.

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.

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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.