Integrating stoichiometry and chain architecture for accurate copolymer property prediction

Copolymers' diverse properties are influenced by monomer type, stoichiometry, and chain architecture, making prediction challenging. Traditional machine learning models struggle to capture these complexities. This study introduces Co-MPNN, a novel graph-based model integrating stoichiometric ratios and chain architectures into a message-passing neural network (MPNN). Using a copolymer dataset from DFT calculations, Co-MPNN outperforms baseline models like MPNN and Random Forest in predicting electron affinity (EA) and ionization potential (IP). Sensitivity analysis confirms Co-MPNN's robustness with small training sets, while latent space visualization highlights its capacity to prioritize key features, enhancing interpretability. These results demonstrate Co-MPNN's potential for accurate and interpretable copolymer property prediction, advancing copolymer design and optimization.