  Machine learning algorithms have been available since the 1990s, but it is
much more recently that they have come into use also in the physical sciences.
While these algorithms have already proven to be useful in uncovering new
properties of materials and in simplifying experimental protocols, their usage
in liquid crystals research is still limited. This is surprising because
optical imaging techniques are often applied in this line of research, and it
is precisely with images that machine learning algorithms have achieved major
breakthroughs in recent years. Here we use convolutional neural networks to
probe several properties of liquid crystals directly from their optical images
and without using manual feature engineering. By optimizing simple
architectures, we find that convolutional neural networks can predict physical
properties of liquid crystals with exceptional accuracy. We show that these
deep neural networks identify liquid crystal phases and predict the order
parameter of simulated nematic liquid crystals almost perfectly. We also show
that convolutional neural networks identify the pitch length of simulated
samples of cholesteric liquid crystals and the sample temperature of an
experimental liquid crystal with very high precision.
