  Machine learning methods are changing the way data is analyzed. One of the
most powerful and widespread applications of these techniques is in image
segmentation wherein disparate objects of a digital image are partitioned and
classified. Here we present an image segmentation program incorporating a
series of unsupervised clustering algorithms for the automatic thickness
identification of two-dimensional materials from digital optical microscopy
images. The program identifies mono- and few-layer flakes of a variety of
materials on both opaque and transparent substrates with a pixel accuracy of
roughly 95%. Contrasting with previous attempts, application generality is
achieved through preservation and analysis of all three digital color channels
and Gaussian mixture model fits to arbitrarily shaped data clusters. Our
results provide a facile implementation of data clustering for the universal,
automatic identification of two-dimensional materials exfoliated onto any
substrate.
