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DS: Fachverband Dünne Schichten

DS 30: Poster: Organic Thin Films and Thin Oxides

DS 30.7: Poster

Mittwoch, 18. März 2020, 15:00–18:00, P1A

Predicting Organic Thin-Film Structures with DFT and Machine Learning — •Fabio Calcinelli and Oliver T. Hofmann — Institute of Solid State Physics, NAWI Graz, Graz University of Technology, Austria

The properties of a material depend on its structure, which for organic thin films often substantially differs from the bulk structure. A theoretical prediction of the most stable thin film structures through traditional, exhaustive first-principle studies is unfeasible due to the combinatorial explosion in the number of possible polymorphs.

The machine-learning based SAMPLE approach [1] can already circumvent this problem for monolayers, by using a few hundred DFT calculations to evaluate the energy of millions of polymorphs through Bayesian Linear Regression. It is our intention to extend the applicability of SAMPLE from monolayers to (meta)stable thin films.

As first step, we predict the best monolayers of a simple organic molecule on graphene, to verify SAMPLE’s effectiveness in describing adsorption on organic substrates. Subsequently, we study thin film structures of pentacenequinone or -tetrone, for which experimental results are available. On this basis we develop a representation for intermolecular interactions in three dimensions and improve our methodologies for local optimization. With this functionality SAMPLE will provide valuable insight into the packing geometries of thin films and into the forces that drive their formation.

[1] Hörmann et al., Computer Physics Communications 244, 143-155, 2019