  The ability of a feed-forward neural network to learn and classify different
states of polymer configurations is systematically explored. Performing
numerical experiments, we find that a simple network model can, after adequate
training, recognize multiple structures, including gas-like coil, liquid-like
globular, and crystalline anti-Mackay and Mackay structures. The network can be
trained to identify the transition points between various states, which compare
well with those identified by independent specific-heat calculations. Our study
demonstrates that neural network provides an unconventional tool to study the
phase transitions in polymeric systems.
