The field of molecular electronics, i.e. the application of single molecules as the building blocks of electronic equipment is among the research directions searching for novel information technologies. There are already well-established techniques to form single molecule nanowires or even more complex single molecule devices.

The so-called break junction technique is a widely used method for the statistical analysis of single molecule nanowires. The typical datasets include ten thousands of conductance vs. electrode separation traces with hundreds of thousands of datapoints on each trace. Our group has developed some statistical methods (like 2D correlation analysis) that can be effectively used to analyze these data, and to understand the temporal evolution of the few atom and single molecule nanowires in more depth than the information supplied by simple conductance histograms. Within the diploma thesis the applicant would broaden our statistical analysis toolbox towards the application of modern machine learning and data mining techniques. This would include the analysis of single molecule break junction data with neural network algorithms, principal component analysis methods and advanced clustering methods. These techniques could help to identify patterns in the data, which are hardly recognized by traditional approaches, and thus would provide a better understanding of the underlying physical processes. Concerning the neural network based analysis, it is an important goal to apply simple enough neural networks, such that one can get insight to the decision taking mechanism of the network.