Engineering Commons^™

Machine Learning Prediction Of Glass Transition Temperature Of Conjugated Polymers From Chemical Structure, Amirhadi Alesadi, Zhiqiang Cao, Zhaofan Li, Song Zhang, Haoyu Zhao, Xiaodan Gu, Wenjie Xia

Faculty Publications

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 …