  Organic solar cells have the potential for widespread usage due to their
promise of low cost, roll-to-roll manufacturability, and mechanical
flexibility. However, deployment is impeded by their relatively low power
conversion efficiencies. The last decade has seen significant progress in
enhancing the power conversion of these devices through various strategies. One
such approach is based on morphology control. This is because morphology
affects all phenomena involved in solar conversion: light absorption and
electron-hole pair (exciton) generation; exciton diffusion and dissociation
into free charges; and transport of charges to the electrodes. Progress in
experimental characterization and computational modeling now allow
reconstruction and imaging of the thin film morphology. This opens up the
possibility of rationally linking fabrication with morphology, as well as
morphology with performance. In this context, a comprehensive set of
computational tools to rapidly quantify and classify the heterogeneous internal
structure of thin films will be invaluable in linking process, structure and
property. We present a novel graph-based framework to efficiently construct a
broad suite of physically meaningful morphology descriptors. These morphology
descriptors are further classified according to the physical subprocesses
within an organic solar cells. The approach is motivated by the equivalence
between a discretized morphology and a labeled, weighted, undirected graph. We
utilize this approach to pose key questions related to structure
characterization. We subsequently construct estimates and upper bounds of
various efficiencies. The approach is showcased by characterizing the effect of
thermal annealing on time-evolution of a thin film morphology. We conclude by
formulating natural extensions to characterize crystallinity and anisotropy of
the morphology using the framework.
