Hybrid Interfaces in Thermodynamic Equilibrium (HI-TEq)

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Phase diagrams contain crucial information for both fundamental and applied research, revealing under which pressure (p) and temperature (T) conditions specific phases form. However, for organic-inorganic interfaces, experimental phase diagrams (as function of T and p) are de facto non-existent. At the same time, the computation of phase diagrams, which relies on ab-initio thermodynamics, typically suffers from severe approximations, as vibrational and thermal effects are often neglected.The objective of this project is to develop strategies for obtaining p-T-phase diagrams for organic-inorganic interfaces. On the experimental side, this requires developing approaches to grow interfaces close to thermodynamic equilibrium under well-defined conditions. On the theoretical side, the role of thermal and vibrational effects on the relative energies of different polymorphs need to be assessed. We hypothesize that including these effects is indispensable for accurate predictions and devise a way to incorporate them with the aid of machine learning. To extract maximum insight from the phase diagrams, we also provide an in-depth analysis of which interactions stabilize specific phases.The interfaces will be grown near thermodynamic equilibrium in a novel vacuum chamber housing an almost closed cryoshield and using small molecules to facilitate the theoretical evaluation. The structures will be investigated in situ using distortion-corrected low-energy electron diffraction. The computational prediction and evaluation of phase diagrams a specialized structure search algorithm relying on a combination of density functional theory and machine learning in the form of Bayes Linear Regression.p-T-phase diagrams for organic-inorganic interfaces are presently a blind spot on the map of surface science that urgently needs to be filled. So far, this endeavor could not be undertaken, due to the lack of adequate experimental equipment and due to the computational difficulty to determine systems that are likely to show phase transitions in the experimentally accessible region. Only with the advent of modern DFT methods and machine-learning techniques, such systems can now be tackled.The project will be executed by a collaboration between the FSU Jena, Germany and the TU Graz, Austria. The experiments will be performed in the group of Torsten Fritz and Roman Forker, who are experts for growing organic-inorganic interfaces and the determination of their structures. The theoretical studies will be performed by Oliver T. Hofmann, who is an expert in machine learning and DFT band structure calculations and who will coordinate the project, and by Egbert Zojer, who has extensive experience in modelling organic semiconductors and their interfaces.