Machine Learning of Phase Transitions and Dynamical Crossovers in Polymers

Phase transitions and dynamical crossovers in polymers are governed by correlated microscopic interactions and relaxation of their large number of atoms and segments over multiple time and length scales. Given the wide variation in microscopic degrees of freedom and macroscopic properties exhibited by polymers, identifying new types of phase transitions and corresponding states can be challenging. Moreover, there is no universal order parameter or straight forward approach to characterize wide range of crossovers in polymers including metastable phase transitions, vitrification, jamming, gel formation and localization transition. Here, we report a generic machine learning framework for autonomous identification and characterization of phase transitions and dynamical crossovers in molecular dynamics trajectory of polymers. We demonstrate this framework for coil to globule transition, crystallization and glass formation during cooling of polymers, and provide new physical insights of these processes. This framework does not need any a-priory knowledge of the crossover and is extensible to predict other phase transitions and dynamical crossover during thermophysical processes such as cooling, drying, and compression of polymers.