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We present a method for the unsupervised segmentation of electron microscopyimages, which are powerful descriptors of materials and chemical systems.Images are oversegmented into overlapping chips, and similarity graphs aregenerated from embeddings extracted from a domain$\unicode{x2010}$pretrainedconvolutional neural network (CNN). The Louvain method for community detectionis then applied to perform segmentation. The graph representation provides anintuitive way of presenting the relationship between chips and communities. Wedemonstrate our method to track irradiation$\unicode{x2010}$induced amorphousfronts in thin films used for catalysis and electronics. This method haspotential for "on$\unicode{x2010}$the$\unicode{x2010}$fly" segmentation toguide emerging automated electron microscopes.

Unsupervised segmentation of irradiation$\unicode{x2010}$induced order$\unicode{x2010}$disorder phase transitions in electron microscopy