Polymer Property

Predicting and designing polymers with desired properties is a major challenge due to their structural complexity and vast chemical space. Current research focuses on developing machine learning models, including graph neural networks, variational autoencoders, and transformer-based architectures, to efficiently predict polymer properties from various representations (e.g., molecular graphs, sequences, and images) and even design new polymers with targeted characteristics. These advancements leverage both labeled and unlabeled data, often incorporating physics-based models to address data scarcity and improve accuracy. The ultimate goal is to accelerate materials discovery and development, enabling the creation of novel polymers for diverse applications.