Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting The Solubility Parameters of Fullerenes.

The solubility of organic semiconductors in environmentally benign solvents is an important prerequisite for the widespread adoption of organic electronic appliances. Solubility can be determined by considering the cohesive forces in a liquid via Hansen solubility parameters (HSP). We report a numerical approach to determine the HSP of fullerenes using a mathematical tool based on artificial neural networks (ANN).

Classification of additives for organic photovoltaic devices.

The use of additives to improve the performance of organic photovoltaic cells has been intensely researched in recent years. However, so far, no system has been reported for the classification of additives and their functions. In this report, a system for classifying additives according to the fundamental mechanism by which they influence microstructure formation for P3HT:PCBM is suggested.