Evidence for Anisotropic Electronic Coupling of Charge Transfer States in Weakly Interacting Organic Semiconductor Mixtures.

We present a comprehensive investigation of the charge-transfer (CT) effect in weakly interacting organic semiconductor mixtures. The donor-acceptor pair diindenoperylene (DIP) and N,N'-bis(2-ethylhexyl)-1,7-dicyanoperylene-3,4/9,10-bis(dicarboxyimide) (PDIR-CN2) has been chosen as a model system. A wide range of experimental methods was used in order to characterize the structural, optical, electronic, and device properties of the intermolecular interactions. By detailed analysis, we demonstrate that the partial CT in this weakly interacting mixture does not have a strong effect on the ground state and does not generate a hybrid orbital. We also find a strong CT transition in light absorption as well as in photo- and electroluminescence. By using different layer sequences and compositions, we are able to distinguish electronic coupling in-plane vs out-of-plane and, thus, characterize the anisotropy of the CT state. Finally, we discuss the impact of CT exciton generation on charge-carrier transport and on the efficiency of photovoltaic devices.

Thermally driven smoothening of molecular thin films: Structural transitions in n-alkane layers studied in real-time.

We use thermal annealing to improve smoothness and to increase the lateral size of crystalline islands of n-tetratetracontane (TTC, C44H90) films. With in situ x-ray diffraction, we find an optimum temperature range leading to improved texture and crystallinity while avoiding an irreversible phase transition that reduces crystallinity again. We employ real-time optical phase contrast microscopy with sub-nm height resolution to track the diffusion of TTC across monomolecular step edges which causes the unusual smoothing of a molecular thin film during annealing. We show that the lateral island sizes increase by more than one order of magnitude from 0.5 µm to 10 µm. This desirable behavior of 2d-Ostwald ripening and smoothing is in contrast to many other organic molecular films where annealing leads to dewetting, roughening, and a pronounced 3d morphology. We rationalize the smoothing behavior with the highly anisotropic attachment energies and low surface energies for TTC. The results are technically relevant for the use of TTC as passivation layer and as gate dielectric in organic field effect transistors.

Visual human+machine learning.

In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combines the vast search and processing power of the computer with the superior reasoning and pattern recognition capabilities of the human user. An evolutionary search algorithm has been adapted to assist in the fuzzy logic formalization of hypotheses that aim at explaining features inside multivariate, volumetric data. Up to now, users solely rely on their knowledge and expertise when looking for explanatory theories. However, it often remains unclear whether the selected attribute ranges represent the real explanation for the feature of interest. Other selections hidden in the large number of data variables could potentially lead to similar features. Moreover, as simulation complexity grows, users are confronted with huge multidimensional data sets making it almost impossible to find meaningful hypotheses at all. We propose an interactive cycle of knowledge-based analysis and automatic hypothesis generation. Starting from initial hypotheses, created with linking and brushing, the user steers a heuristic search algorithm to look for alternative or related hypotheses. The results are analyzed in information visualization views that are linked to the volume rendering. Individual properties as well as global aggregates are visually presented to provide insight into the most relevant aspects of the generated hypotheses. This novel approach becomes computationally feasible due to a GPU implementation of the time-critical parts in the algorithm. A thorough evaluation of search times and noise sensitivity as well as a case study on data from the automotive domain substantiate the usefulness of the suggested approach.

Importance-driven focus of attention.

This paper introduces a concept for automatic focusing on features within a volumetric data set. The user selects a focus, i.e., object of interest, from a set of pre-defined features. Our system automatically determines the most expressive view on this feature. A characteristic viewpoint is estimated by a novel information-theoretic framework which is based on the mutual information measure. Viewpoints change smoothly by switching the focus from one feature to another one. This mechanism is controlled by changes in the importance distribution among features in the volume. The highest importance is assigned to the feature in focus. Apart from viewpoint selection, the focusing mechanism also steers visual emphasis by assigning a visually more prominent representation. To allow a clear view on features that are normally occluded by other parts of the volume, the focusing for example incorporates cut-away views.