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Organic solar cells have the potential for widespread usage due to theirpromise of low cost, roll-to-roll manufacturability, and mechanicalflexibility. However, deployment is impeded by their relatively low powerconversion efficiencies. The last decade has seen significant progress inenhancing the power conversion of these devices through various strategies. Onesuch approach is based on morphology control. This is because morphologyaffects all phenomena involved in solar conversion: light absorption andelectron-hole pair (exciton) generation; exciton diffusion and dissociationinto free charges; and transport of charges to the electrodes. Progress inexperimental characterization and computational modeling now allowreconstruction and imaging of the thin film morphology. This opens up thepossibility of rationally linking fabrication with morphology, as well asmorphology with performance. In this context, a comprehensive set ofcomputational tools to rapidly quantify and classify the heterogeneous internalstructure of thin films will be invaluable in linking process, structure andproperty. We present a novel graph-based framework to efficiently construct abroad suite of physically meaningful morphology descriptors. These morphologydescriptors are further classified according to the physical subprocesseswithin an organic solar cells. The approach is motivated by the equivalencebetween a discretized morphology and a labeled, weighted, undirected graph. Weutilize this approach to pose key questions related to structurecharacterization. We subsequently construct estimates and upper bounds ofvarious efficiencies. The approach is showcased by characterizing the effect ofthermal annealing on time-evolution of a thin film morphology. We conclude byformulating natural extensions to characterize crystallinity and anisotropy ofthe morphology using the framework.

A novel graph-based formulation for characterizing morphology with application to organic solar cells