  Chiral indices determine important properties of carbon nanotubes (CNTs).
Unfortunately, their determination from high-resolution transmission electron
microscopy (HRTEM) images, the most accurate method for assigning chirality, is
a tedious task. We develop a Convolutional Neural Network that automatizes this
process. A large and realistic training data set of CNT images is obtained by
means of atomistic computer simulations coupled with the multi-slice approach
for image generation. In most cases, results of the automated assignment are in
excellent agreement with manual classification, and the origin of failures is
identified. The current approach, which combines HRTEM imaging and deep
learning algorithms allows the analysis of a statistically significant number
of HRTEM images of carbon nanotubes, paving the way for robust estimates of
experimental chiral distributions.
