  Machine learning methods are becoming increasingly important for the
development of materials science. In spite of this, the use of image analysis
in the development of these systems is still recent and underexplored,
especially in materials often studied via optical imaging techniques such as
liquid crystals. Here we apply the recently proposed method of ordinal networks
to map optical textures obtained from experimental samples of liquid crystals
into complex networks and use this representation jointly with a simple
statistical learning algorithm to investigate different physical properties of
these materials. Our research demonstrates that ordinal networks formed by only
24 nodes encode crucial information about liquid crystal properties, thus
allowing us to train simple machine learning models capable of identifying and
classifying mesophase transitions, distinguishing among different doping
concentrations used to induce chiral mesophases, and predicting sample
temperatures with outstanding accuracy. The precision and scalability of our
approach indicate it can be used to probe properties of different materials in
situations involving large-scale datasets or real-time monitoring systems.
