Predicting the Glass Transition of Complex Polymers via Integration of Machine Learning, Molecular Modeling and Experiments

Semiconducting conjugated polymers (CPs) are attractive organic electronic materials for a wide range of applications due to their easy processability, tunable electrical performance, and mechanical flexibility. Despite tremendous efforts, design and prediction of glass-transition temperature (T_g) remain notably challenging for CPs due to their complex chain architecture associated with diverse chemical building blocks. In this work, we establish an integrated framework based on machine learning (ML) and molecular simulations to predict T_g for a diverse set of CPs and other polymers with drastic differences in their chemical structures. Informed from informatics and molecular theory, the developed ML model takes the geometry of diverse chemical building blocks to define simplified structural features to make T_g prediction, which is further validated by experimental measurement. Moreover, the use of molecular modeling and theory in conjunction with ML uncovers the critical roles of key molecular features in influencing the glass transition temperature as well as dynamics heterogeneity associated with the glass formation of complex polymers. The established predictive framework and ML model could be ready to use for design of high-performance CPs and relevant materials via molecular engineering.