Organic-inorganic interfaces play a crucial role in many modern electronic devices such as OLED displays and organic photovoltaic cells. Of particular importance to technical applications is the geometry of a given interface, which can assume many different structures (polymorphs). To efficiently determine thermodynamically stable polymorphs, we pioneered a machine learning based structure search algorithm and utilized it to successfully determine stable polymorphs for multiple organic-metal interfaces.

For many applications, organic-semiconductor interfaces are also of pertinence. However, in this case the bulk doping concentration of the semiconductor can lead to charge transfer from the semiconductor to the organic adlayer. It is not clear how strongly the charge transfer caused by doping can affects the polymorphism of organic- semiconductor interfaces.

The aim of this master’s thesis is to determine the impact of the doping-induced charge transfer on the polymorphism of organic-semiconductor interfaces by applying density functional theory and our machine-learning based structure search algorithm to a representative organic-semiconductor interface.

COMPENSATION: € 440,-- Forschungsbeihilfe for 6 months
CONTACT: Oliver Hofmann (o.hofmann@tugraz.at)