Organic Photovoltaic (OPV) solar cells provide a promising alternative for harnessing solar energy. However, the efficient design of OPV materials that achieve better performance requires support by better-tailored visualization tools than are currently available, which is the goal of this thesis. One promising approach in the OPV field is to control the effective material of the OPV device, which is known as the Bulk-Heterojunction (BHJ) morphology. The BHJ morphology has a complex composition. Current BHJ exploration techniques deal with the morphologies as black boxes with no perception of the photoelectric current in the BHJ morphology. Therefore, this method depends on a trial-and-error approach and does not efficiently characterize complex BHJ morphologies. On the other hand, current state-of-the-art methods for assessing the performance of BHJ morphologies are based on the global quantification of morphological features. Accordingly, scientists in OPV research are still lacking a sufficient understanding of the best material design. To remove these limitations, we propose a new approach for knowledge-assisted visual exploration and analysis in the OPV domain. We develop new techniques for enabling efficient OPV charge transport path analysis. We employ, adapt, and develop techniques from scientific visualization, geometric modeling, clustering, and visual interaction to obtain new designs of visualization tools that are specifically tailored for the needs of OPV scientists. At the molecular scale, the user can use semantic rules to define clusters of atoms with certain geometric properties. At the nanoscale, we propose a novel framework for visual characterization and exploration of local structure-performance correlations. We also propose a new approach for correlating structural features to performance bottlenecks. We employ a visual feedback strategy that allows scientists to make intuitive choices about fabrication parameters. We furthermore propose a visual analysis framework to help answer domain science questions through parameter space exploration for local morphology features. This framework is built on the shape-based clustering of local regions (patches), which for the first time enables local analysis of BHJ morphologies. Using our proposed system, domain experts can interactively create and visualize new BHJ structures of interest at both the molecular and nanoscale levels in a relatively short time.
|Date of Award||Dec 5 2017|
|Original language||English (US)|
- Computer, Electrical and Mathematical Science and Engineering
|Supervisor||Markus Hadwiger (Supervisor)|
- Geometric Modeling
- material science
- Organic Photovoltaics