Surface state engineering of molecule-molecule interactions

Engineering the electronic structure of organics through interface manipulation, particularly the interface dipole and the barriers to charge carrier injection, is of essential importance to improved organic devices. This requires the meticulous fabrication of desired organic structures by precisely controlling the interactions between molecules. The well-known principles of organic coordination chemistry cannot be applied without proper consideration of extra molecular hybridization, charge transer and dipole formation at the interfaces. Here we identify the interplay between energy level alignment, charge transfer, surface dipole and charge pillow effect and show how these effects collectively determine the net force between adsorbed porphyrin 2H-TPP on Cu(111). We show that the forces between supported porphyrins can be altered by controlling the amount of charge transferred across the interface accurately through the relative alignment of molecular electronic levels with respect to the Shockley surface state of the metal substrate, and hence govern the self-assembly of the molecules

Visualizing class probability estimators

Abstract. Inducing classifiers that make accurate predictions on future data is a driving force for research in inductive learning. However, also of importance to the users is how to gain information from the models produced. Unfortunately, some of the most powerful inductive learning algorithms generate “black boxes”—that is, the representation of the model makes it virtually impossible to gain any insight into what has been learned. This paper presents a technique that can help the user understand why a classifier makes the predictions that it does by providing a two-dimensional visualization of its class probability estimates. It requires the classifier to generate class probabilities but most practical algorithms are able to do so (or can be modified to this end).