Predicting the glass transition behaviors of polymers via integration of molecular simulations, theory, and machine learning

Understanding and predicting the glass transition behavior of glass-forming polymers are of critical importance from both physical and practical standpoints. The substantial change in polymer relaxation and dynamics upon cooling causes major change in most physical properties, including mechanical modulus, density, specific heat, damping characteristics, dielectric properties of a polymer. The cheminformatics-based approach based on machine learning (ML) algorithms is often applied to draw the quantitative relationships between key molecular parameters/descriptors and properties of polymers. In this work, we develop an innovative framework by integrating cheminformatics and coarse-grained molecular dynamics (MD) simulations to predict the glass transition temperature of diverse sets of hundred polymers. Moreover, the use of generalized entropy theory in conjunction with ML uncovers the critical roles of key molecular parameters (i.e., cohesive interactions, chain stiffness, and branching) in influencing the glass transition temperature as well as other characteristic temperatures associated with glass formation of polymers.