Enhancing Copolymer Property Prediction through the Weighted-Chained-SMILES Machine Learning Framework

Accurately predicting copolymer properties plays a pivotal role in the field of polymer informatics. This endeavor necessitates a comprehensive understanding of polymer structures, adept feature engineering, and proficient application of machine learning algorithms. In traditional methodologies, features for each monomer structure were generated independently, thus, segregating features from individual monomers. This approach results in a less informative representation, with limited applicability. To address these challenges, we introduce an innovative machine learning framework, named weighted-chained-SMILES. By constructing a representative SMILES notation, more intricate information can be encapsulated within the generated features. Our experimental results to predict the thermal properties demonstrate that our approach not only delivers competitive predictive performance but also exhibits enhanced adaptability across a diverse range of molecular representations. The versatility showcased by our model suggests promising potential for tackling more complex copolymer systems and extending its predictive capabilities to various other polymer properties.