Machine learning prediction of glass transition temperature of conjugated polymers from chemical structure

Predicting the glass transition temperature (T_g) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict T_g of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 T_g data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for T_g. MD simulations further unravel the fundamental roles of those chemical building blocks in dynamical heterogeneity and local mobility of CPs at a molecular level. The developed ML model is demonstrated for its capability of predicting T_g of several new high-performance solar cell materials to a good approximation. The established predictive framework facilitates the design and prediction of T_g of complex CPs, paving the way for addressing device stability issues that have hampered the field from developing stable organic electronics.