Order classification is particularly important in photonics, optoelectronics, nanotechnology, biology, and biomedicine, as self-assembled and living systems tend to be ordered well but not perfectly. Engineering sets of experimental protocols that can accurately reproduce specific desired patterns can be a challenge when (dis)ordered outcomes look visually similar. Robust comparisons between similar samples, especially with limited data sets, need a finely tuned ensemble of accurate analysis tools. Here we introduce our numerical Mathematica package disLocate, a suite of tools to rapidly quantify the spatial structure of a two-dimensional dispersion of objects. The full range of tools available in disLocate give different insights into the quality and type of order present in a given dispersion, accessing the translational, orientational and entropic order. The utility of this package allows for researchers to extract the variation and confidence range within finite sets of data (single images) using different structure metrics to quantify local variation in disorder. Containing all metrics within one package allows for researchers to easily and rapidly extract many different parameters simultaneously, allowing robust conclusions to be drawn on the order of a given system. Quantifying the experimental trends which produce desired morphologies enables engineering of novel methods to direct self-assembly.

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Self-assembly of planar molecules can be a critical route to control morphology in organic optoelectronic systems. In this study, Monte Carlo simulations were performed with polygonal disc analogues to planar semiconducting molecules under confinement. By examining statistically the molecular density and configurations of such analogues, we have observed that the symmetry of the confining medium can have a greater impact on the final densified particle configurations than the intramolecular interactions. Using the steric frustration imparted by confinement, novel self-assembled (partially) ordered phases are available. Our Monte Carlo simulations suggest new avenues to control ordering and morphology of planar molecules, which are critical for high-performance organic optoelectronic devices.

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