Description: Inorganic/Organic interfaces play a crucial role for the performance of organic electronic devices, such as OLED-TVs and photovoltaic cell. Of particular interest is the interface geometry, which crucially defines the device performance. However, finding optimal process parameters for optimal geometries is a tedious efforts mostly governed by tiresome trial-and-error approaches. There is, therefore, a strong need of theoretical support. To provide this support, my group and I have recently developed a Machine Learning based algorithm that allows determining the interface structure with a higher precision, and at a lower cost, than any other existing approach.


The aim of this PhD thesis is to critically examine the approximations that traditionally go into the prediction of process parameters when using ab-initio thermodynamics. These approximations are generally well tested for inorganic materials, but hitherto basically unexplored for molecular materials. The work will be done in close collaboration with experimental colleagues in Jena, Germany, who are building a dedicated experimental setup to create benchmark phase diagrams, thus providing an unique basis for this work

• A friendly group with a productive atmosphere
• Training in industry-relevant skills, including machine learning, computational material science, and organic electronics
• Visiting multiple conferences (Covid-19 permitting)

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