Polymer Informatics Accelerates and Improves Polymer Science
The FAIRmat Seminar series continues with a talk by Prof. Dr. Christopher Künneth, an assistant professor at the University of Bayreuth (UBT).
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Abstract:
Polymer informatics has become an increasingly important area in materials science and chemistry, offering systematic pathways for the discovery and design of polymers tailored to specific applications. While traditional computational methods encounter limitations when navigating the vast chemical space of polymer structures, data-driven approaches provide a scalable and efficient alternative.
In this seminar, Prof. Künneth 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.