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

Abstract

Predicting the glass transition temperature (Tg) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict Tg of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 Tg 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 Tg. 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 Tg of several new high-performance solar cell materials to a good approximation. The established predictive framework facilitates the design and prediction of Tg of complex CPs, paving the way for addressing device stability issues that have hampered the field from developing stable organic electronics.