  Machine learning (ML) and artificial intelligence (AI) have the remarkable
ability to classify, recognize, and characterize complex patterns and trends in
large data sets. Here, we adopt a subclass of machine learning methods viz.,
deep learnings and develop a general-purpose AI tool - dPOLY for analyzing
molecular dynamics trajectory and predicting phases and phase transitions in
polymers. An unsupervised deep neural network is used within this framework to
map a molecular dynamics trajectory undergoing thermophysical treatment such as
cooling, heating, drying, or compression to a lower dimension. A supervised
deep neural network is subsequently developed based on the lower dimensional
data to characterize the phases and phase transition. As a proof of concept, we
employ this framework to study coil to globule transition of a model polymer
system. We conduct coarse-grained molecular dynamics simulations to collect
molecular dynamics trajectories of a single polymer chain over a wide range of
temperatures and use dPOLY framework to predict polymer phases. The dPOLY
framework accurately predicts the critical temperatures for the coil to globule
transition for a wide range of polymer sizes. This method is generic and can be
extended to capture various other phase transitions and dynamical crossovers in
polymers and other soft materials.
