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O: Fachverband Oberflächenphysik
O 99: Organic Molecules on Inorganic Substrates VI: Adsorption, Growth and Networks
O 99.4: Vortrag
Donnerstag, 19. März 2020, 11:30–11:45, GER 38
Explaining Misleading Surface Diffraction Patterns with DFT and Machine Learning — •Andreas Jeindl1, Jari Domke2, Malte Schulte3, Falko Sojka2, and Oliver T. Hofmann1 — 1Institute of Solid State Physics, NAWI Graz, Graz University of Technology, Austria — 2Institute of Solid State Physics, Friedrich Schiller University Jena, Germany — 3Experimental Physics 1, TU Dortmund, Germany
The determination of the structure of organic thin films and monolayers on inorganic substrates commonly relies on surface diffraction methods, such as LEED. Although the spectra obtained by these methods contain all the necessary information (except the phase) required to directly reconstruct the atomistic structure, in practice the interpretation is far from straightforward. In cases when multiple different models satisfactorily explain the experiment, good scientific practice suggests applying occam's razor and selecting the simplest structure possible.
Here, we highlight several instances where diffraction pattern misleadingly suggest too simple structures due to the presence of orientational domains, disorder, or small structure factors. Using caffeine on Au(111) and acenequinones on Ag(111) as examples, we perform a comprehensive first-principles structure search to independently determine the correct geometry, which is often complex. We discuss the difficulties and interpretation possibilities when trying to compare LEED-data of organic monolayers with theoretically predicted structures for different organic/inorganic interfaces.