Predicting Glass Transition of Amorphous Polymers by Application of Cheminformatics and Molecular Dynamics Simulations

abstract Predicting glass-transition temperature ( T g ) of glass-forming polymers is of critical importance as it governs the thermophysical properties of polymeric materials, such as relaxation dynamics, modulus, specific heat, dielectric properties. The cheminformatics approaches based on machine learning algorithms are becoming very useful in predicting the quantitative relationships between key molecular descriptors and various physical properties of materials. In this work, we developed a modeling framework by integrating cheminformatics methods and coarse-grained molecular dynamics (CG-MD) simulations to predict T g of diverse sets of polymers. The best predictive machine learning-based QSPR model identified the most prominent molecular descriptors influencing the T g of a hundred of polymers. Informed by the model, CG-MD simulations are performed to further delineate mechanistic interpretation and systematic dependence of these influential molecular features on T g by investigating three major CG model parameters, namely the cohesive interaction, chain stiffness, and grafting density. The CG-MD simulations reveal that the higher intermolecular interaction and chain stiffness elevates the T g of CG polymers, where their relative influences are coupled with the existence of side chains grafted on the backbone. This synergistic modeling approach provides valuable insights into the roles of key molecular features influencing the T g of polymers, paving the way to establish a materials-by-design framework for polymeric materials via molecular engineering.