(736c) Using Graphs to Quantify Energetic and Structural (dis)Order in Organic Thin Films

The nanomorphology of polymer blend thin films critically affects performance especially in organic solar cells. However, many aspects of the underlying physics linking morphology to performance are still poorly understood. At the same time, there is increasing evidence that atomic organization can hold the key to efficient charge transport within organic electronic devices. In order to fully capitalize on these recent evidence, there is a need to quantify the atomistic and energy features of morphologies with respect to basic steps of photovoltaic process to enable the quantitative structure property relationship.

In this work, we take advantage of recent advances in molecular dynamic simulations and quantify atomic-scale morphological aspects of the thin films. Specifically, we present a graph-based technique that allows quantifying the point-cloud data (MD). In our approach, we first convert the point cloud data from atomistic simulation into a labelled, weighted, undirected graph and then use standard graph-based algorithms to calculate and quantify morphology features. The conversion of the CGMD-data into a graph preserves all the topological and geometric information about the internal structure, and local connectivity between individual atoms/beads (along and across the polymer chains). More importantly, the edges between individual beads on CGMD can be labelled by Euclidean distance, energy difference or hopping rate. Our method provides hierarchical information about the charge paths that a hole/electron needs to take to reach the electrode (path length, fraction of intra-molecular hops, path balance).

We showcase capabilities of our approach by analyzing coarse grained molecular simulations of several oligothiophene blends. We present how graph-based method allows to provide quantitative insight into the origins of few orders of magnitude difference in mobility. Taking into account the comparable dimensionality of samples, it is hard to rationalize such difference based solely on the physical distances. We combine the information from structural (Molecular Dynamics) and energy (Density Functional Theory) levels to explain large differences in mobility for these polymer blends.