Thermal stability prediction of copolymerized polyimides via an interpretable transfer learning model

Abstract

To address the issues with molecular representation of copolymerized polyimides (PIs) and the mini dataset of PI powders. We constructed an interpretable machine learning (ML) model for PI films using the weighted-additive Morgan Fingerprints with Frequency descriptors and developed an interpretable transfer learning model for PI powders. To enhance Thermal Stability (Temperature at 5% weight loss) of PI films and powders, it is recommended to add conjugated functional groups to diamines, control phenyl ring side chains, and reduce pyridine and hydroxyl groups; select copolyimides (co-PIs); ensure that anhydride is directly connected to the benzene ring in dianhydrides, avoiding aliphatic cycles. It is noteworthy that the close alignment between experimental results and model predictions serves to confirm the model is a reliable prediction tool. It is hoped that this polymer informatics approach will provide further implementation for practical applications of other functional materials.