The performance of organic electronic devices is strongly influenced by the charge-carrier mobility of the organic component. Recently, computational scientists at the TU Munich have predicted a novel molecule with the potential to exhibit outstanding characteristics. So far, however, experimental verification was unsuccessful. This is because the materialís properties depend not only on its chemistry, but also on its crystal structure, i.e. the polymorph that it assumes. This, in turn, is strongly influenced by the deposition conditions and the nature of the substrate.
My group and I have developed one of the first codes able to investigate the structural complexity of organic materials on potential electrode substrates. This is done using a combination of density functional theory and machine learning. The main purpose of this PhD assignment is to apply this code in order to guide experiments and help them finding the ideal substrate / deposition conditions in order to realize the predicted, extraordinary charge carrier mobilities.
> A friendly group with a productive atmosphere
> Training in skills relevant for industry, including machine learning, computational material science, and organic electronics
> Visiting multiple (international) conferences per year
> Collaboration with theoretical and experimental groups at the TU Munich
> An extended abroad stay at a renowned international research facility
We seek: Highly motivated, self-propelled students with an interest in solid state physics and computational material science.
Compensation: Ä 2.112,40 per month, 14x per year (funded by the START-prize)
Oliver Hofmann email: email@example.com
Tel: 0316 873 8964 http://www.if.tugraz.at/hofmann
Or talk to the students in office PH 02 152 (2nd floor, right by the stairs)