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

A Alesadi and ZQ Cao and ZF Li and S Zhang and HY Zhao and XD Gu and WJ Xia, CELL REPORTS PHYSICAL SCIENCE, 3, 100911 (2022).

DOI: 10.1016/j.xcrp.2022.100911

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.

Return to Publications page