Directed Message Passing Neural Networks for Accurate Prediction of Polymer-Solvent Interaction Parameters

Accurate prediction of polymer-solvent interactions is essential for applications such as polymer processing, drug delivery, and membrane separations. The Flory-Huggins interaction parameter (χ parameter) serves as a key descriptor for polymer-solvent compatibility; however, its experimental determination is often costly and time-consuming. In this study, we develop a machine learning framework based on Directed Message Passing Neural Networks (D-MPNNs) to predict χ parameters directly from molecular structures, temperature, and volume fraction. Our approach systematically evaluates different feature representations, pooling methods, and empirical equation integrations to optimize prediction accuracy. Among the tested models, D-MPNN-TC, which incorporates both temperature and volume fraction, achieves the highest predictive performance (MAE = 0.092, RMSE = 0.162, R² = 0.926), outperforming descriptor-based models that rely on precomputed molecular fingerprints and handcrafted chemical features. Additionally, integrating the Flory-Huggins equation into the classification framework enables highly accurate miscibility predictions, with F1 scores of 0.915. Further analysis using t-SNE visualization reveals that D-MPNNs effectively capture key structural features, such as aromaticity and cyclic structures, that influence polymer-solvent interactions. Our findings highlight the advantages of graph-based molecular representations over traditional fingerprinting methods and underscore the importance of volume fraction information in predicting polymer-solvent compatibility. This study provides a scalable and interpretable framework for leveraging machine learning in polymer science, facilitating data-driven solvent selection and polymer design.