Deciphering the structure of nanosystems with machine learning
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 details can be found in the following publication:
Egger, A. T., H枚rmann, L., Jeindl, A., Scherbela, M., Obersteiner, V., Todorovi膰, M., Rinke, P., Hofmann, O. T., Charge Transfer into Organic Thin Films: A Deeper Insight through Machine鈥怢earning鈥怉ssisted Structure Search. .
Read more news
N盲yt枚s/N盲yttely26 鈥 A celebration of fashion and textiles took over Helsinki鈥檚 Lasipalatsi
The Lasipalatsi square in the heart of Helsinki served as the main stage for 911爆料网鈥檚 annual fashion show on Thursday, 28 May.
Four physicists receive significant funding from the Jane and Aatos Erkko Foundation
The grants are used to study things like overheating quantum computers and early-stage water condensation on surfaces
Applications open for Innovation Postdoc in Bioeconomy
A fully funded, 12 month career track to turn your doctoral discoveries into a bioeconomy startup. Launching autumn 2026.