Predicting Glass Transition Temperature of Polyimide Homopolymers and Copolymers Using Interpretable QSPR-Based Machine Learning: The MFKit Framework

In this work, we address the problem of predicting the glass transition temperature of polyimides, a class of high-performance engineering thermoplastics, with an emphasis on both homopolymer and copolymer systems. While machine learning approaches have been widely applied to polymer property prediction, their extension to copolymers remains limited and often lacks the interpretability required for rational materials design. To overcome these limitations, we develop an interpretable QSPR-based framework (MFKit) that combines physically grounded descriptors for monomer featurization with Gaussian Process Regression (GPR). The model is trained on a dataset of polyimide homopolymers from PoLyInfo and achieves performance comparable to state-of-the-art approaches (R2 = 0.856, MAE = 20.7 °C on the test set). Causal inference and SHAP analysis reveal key structure–property relationships, identifying aromatic content, backbone flexibility and rotation, side chain volume and hydrogen bonding as dominant factors governing Tg. The framework is subsequently extended to polyimide copolymers by constructing copolymer representations through combination of monomer descriptor vectors using several aggregation schemes. The best-performing approach yields R2 = 0.832 and MAE = 23.3 °C on an external dataset compiled from the literature. Furthermore, guided by the identified structure–property relationships, polyimide copolymers are designed, synthesized, and experimentally characterized, each exhibiting increased Tg compared with the parent homopolymer. Validation against DSC measurements demonstrates strong agreement with predictions (MAE = 16.3 °C). Overall, the proposed approach provides a unified and interpretable framework for Tg prediction for both homopolymer and copolymer systems, enabling physically grounded and data-driven design of polyimide-based materials.