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

Abstract

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. • Machine learning approach is employed to predict glass transition temperature ( T g ) • Aromatic rings and alkyl side chains are dominant building blocks to determine T g • Molecular dynamics simulations unravel role of diverse building blocks in dynamics • Experimental measurements of T g confirm the predictive performance of the model Alesadi et al. present an integrated framework to predict the glass transition temperature of conjugated polymers having diverse chemistry through the integration of machine learning, molecular dynamics simulations, and experiments. The predictive model takes simplified “geometry” of six key chemical building blocks as molecular features to predict glass transition temperature.