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  1. Abstract Electron backscattered diffraction (EBSD) is a technique regularly used to obtain crystallographic information from inorganic samples. When EBSD is acquired simultaneously with emitting diodes data, a sample can be thoroughly characterized both structurally and compositionally. For organic materials, coherent Kikuchi patterns do form when the electron beam interacts with crystalline material. However, such patterns tend to be weak due to the low average atomic number of organic materials. This is compounded by the fact that the patterns fade quickly and disappear completely once a critical electron dose is exceeded, inhibiting successful collection of EBSD maps from them. In this study, a new approach is presented that allows successful collection of EBSD maps from organic materials, here the extreme example of a hydrocarbon organic molecular thin film, and opens new avenues of characterization for crystalline organic materials. 
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  2. Abstract

    Output from multidimensional datasets obtained from spectroscopic imaging techniques provides large data suitable for machine learning techniques to elucidate physical and chemical attributes that define the maximum variance in the specimens. Here, a recently proposed technique of dimensional stacking is applied to obtain a cumulative depth over several LaAlO3/SrTiO3heterostructures with varying thicknesses. Through dimensional reduction techniques via non‐negative matrix factorization (NMF) and principal component analysis (PCA), it is shown that dimensional stacking provides much more robust statistics and consensus while still being able to separate different specimens of varying parameters. The results of stacked and unstacked samples as well as the dimensional reduction techniques are compared. Applied to four LaAlO3/SrTiO3heterostructures with varying thicknesses, NMF is able to separate 1) surface and film termination; 2) film; 3) interface position; and 4) substrate attributes from each other with near perfect consensus. However, PCA results in the loss of data related to the substrate.

     
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