  We present a method for the unsupervised segmentation of electron microscopy
images, which are powerful descriptors of materials and chemical systems.
Images are oversegmented into overlapping chips, and similarity graphs are
generated from embeddings extracted from a domain$\unicode{x2010}$pretrained
convolutional neural network (CNN). The Louvain method for community detection
is then applied to perform segmentation. The graph representation provides an
intuitive way of presenting the relationship between chips and communities. We
demonstrate our method to track irradiation$\unicode{x2010}$induced amorphous
fronts in thin films used for catalysis and electronics. This method has
potential for "on$\unicode{x2010}$the$\unicode{x2010}$fly" segmentation to
guide emerging automated electron microscopes.
