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Deciphering the structure of nanosystems with machine learning

June 30th, 2020 by Patrick Rinke

Hybrid organic-inorganic films are important nanosystems for novel applications. Their specific function depends on their structure, in particular how the organic molecules orient on the inorganic component (here a metal surface). The CEST group teamed up with Oliver Hofmann's research group at Technical University Graz in Austria to investigate a specific organic-inorganic hybrid system: films of tetracyanoethylene (TCNE) molecules in contact with copper surface.

By combining two machine learning methods with quantum mechanical density-functional theory calculations, we investigated the structure of TCNE films on the copper surface. We observed a phase transition of flat lying molecules at low coverage to upright standing molecules at high coverage. Our results refute earlier claims that the TCNE molecules are always flat lying and that long-range charge transfer sets in at increased coverage. The solution of this long-standing puzzles opens up further research into the nanostructured behavior of hybrid organic-inorganic materials.

More information:
Alexander T. Egger et al. Charge Transfer into Organic Thin Films: A Deeper Insight through Machine‐Learning‐Assisted Structure Search, Advanced Science (2020). DOI: 10.1002/advs.202000992

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