(Generative) Polymer Informatics Accelerates and Improves Polymer Science

On May 7, 2026, Christopher Künneth gave the talk "Polymer Informatics Accelerates and Improves Polymer Science" in the FAIRmat Seminar Series in Berlin. In this seminar, he presents how machine learning models are directly applied to the prediction of polymer properties and the design of bioplastics and organic battery materials. The presentation further showcases the role of chemical language models in this process and how it streamlines polymer discovery. The relevance of this research extends across multiple sectors. By accelerating the polymer discovery and design process, these computational pipelines can support continued development in areas ranging from sustainable materials to energy storage. Furthermore, the adaptability of this approach suggests its potential use for other complex material classes.