Glass Transition Temperature Prediction of Polymers via Graph Reinforcement Learning

• Published in: Langmuir Vol. 40; no. 35; pp. 18568 - 18580
• Main Authors: Dong, Caibo, Li, Dazi, Liu, Jun
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 03-09-2024
• ISSN: 0743-7463; 1520-5827; 1520-5827
• DOI: 10.1021/acs.langmuir.4c01906

An expansive array of graph-based models has been utilized for accurate prediction of the structure–property relation of polymers. However, these approaches notably underutilize unsupervised structural information. Concentrating on the domain of heterocyclic polymers, particularly polyimides, this study delves into the glass transition temperature (T g) prediction, aiming to fully exploit the potential within both the global and local structures of molecules. To achieve this, a graph reinforcement learning framework termed Molecular Structural Regularized Graph Convolutional Network with Reinforcement Learning (MSRGCN-RL) is proposed. Experimental results highlight the crucial role of both global and local structural regularization in precise T g prediction. Concurrently, optimization of MSRGCN training through RL proves essential. This research leads the way in integrating Graph Neural Networks (GNNs) with reinforcement learning methodologies for the property prediction of polymers.