Designing High Glass Transition Temperature Fluoropolymers using Transfer Learning

In this study, we propose a novel machine learning approach to accurately predict fluoropolymers' glass transition temperature (T_g) and demonstrate its potential in guiding the design of high T_g copolymers. Firstly, we utilize the QM9 dataset for model pre-training, providing robust molecular representations for subsequent transfer learning on a specialized copolymer dataset. Our pre-trained model expertly encodes complicated molecular structures and general molecular properties using atom-level (graph) and global molecular-level (global state) features. This extensive feature set is processed via a dual network system, with the outputs merged to form a comprehensive molecular descriptor. Dealing with a small copolymer dataset, we encounter significant discrepancies between individual models, a common issue in limited data. We address this problem by adopting an ensemble approach and providing more reliable and robust predictions. Finally, we can navigate a vast chemical space comprising 61 monomers and identify promising candidates for developing high T_g fluoropolymers. Our work shows the potential of machine learning in materials design and discovery and the effectiveness of ensemble models in addressing the challenges associated with small datasets.