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Physical Interpretation of Machine Learning Results      >> more >>

The advent of Machine Learning methods has unlocked great potential in computational studies. In particular the exploration of surface structures, that was previously thought to be completely unfeasible, has surged in the recent years. At the same time, machine learning studies are often criticized for their lack of physical insight.

In this master project, we will investigate Bisphenol A aggregates on Ag(111). This system shows an interesting peculiarity: The molecules adsorb in two different ways. The rotational barriers between those two differ, such that one kind of molecules is immobile at room temperature, while the other remains mobile and cannot be sharply imaged in STM studies. The target of this thesis is to investigate the interface using an in-house developed machine learning algorithm and study the relative contribution of covalent, ionic, and van-der-Waals contributions, in order to provide an explanation for the difference between the two adsorption sites.

We seek: Highly motivated, self-propelled students with an interest in solid state physics and computational material science. Basic knowledge of Matlab and/or Python is recommended.


Compensation: 440,-- Forschungsbeihilfe for 6 months.

Contact:
Oliver Hofmann email: o.hofmann@tugraz.at
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)