Open Polymer Challenge: Leveraging Machine Learning for Polymer Informatics

Machine learning (ML) holds immense potential for discovering sustainable polymer materials, yet progress is hindered by the lack of high-quality open data. We provide an open-sourced dataset that is ten times larger than existing ones, along with competitive ML baselines and evaluation pipelines. This challenge targets multi-task polymer property prediction, which is crucial for virtual screening of polymers.Participants are asked to develop accurate prediction models, with a focus on material properties. A variety of ML techniques such as data augmentation and imbalanced learning, sophisticated learning paradigms like transfer learning and self-supervised learning, and novel model architectures with a good inductive bias on polymers can be leveraged. The competition results will directly accelerate the discovery of novel polymers for sustainable and energy-saving materials.