  We report a deep learning (DL) framework viz. deep autoencoder that
autonomously discovers an appropriate order parameter from molecular dynamics
(MD) simulation data to characterize the coil to globule phase transition of a
polymer. The deep autoencoder encodes the 3N dimensional MD trajectory of a
polymer in a one-dimensional feature space and, subsequently, decodes the
one-dimensional feature to its original 3N dimensional polymer trajectory. The
feature space representation of a polymer provides a new order parameter that
accurately describes the coil to globule phase transition as a function of
temperature. This method is very generic and extensible to identify flexible
order parameters to characterize wide range of phase transitions that take
place in polymers and other soft materials. Moreover, this MD-DL approach is
computational very efficient than a pure MD based characterization of phase
transition, and has potential implications in accelerating phase prediction.
