Data science-based material research

Adoption of methods and concepts of artificial intelligence and machine learning for polymer research and for generation of new findings from big data sets

Methods of artificial intelligence together with combinatorial high-throughput experimental techniques provide new opportunities in the search for future materials with tailored properties. Algorithms of machine learning have the potential to detect structure-property relationships in big data sets.
However, to fully exploit these methods, concepts of central and reusable data storage must be implemented.
Together with analytical insights obtained from experiments and theoretical models, such methods can enormously enhance the potential for prediction of material properties.
New approaches and breakthroughs can be expected for simulation methods and theoretical polymer physics, for research on biomaterials, nano-optical components, process and materials engineering, as well as for catalysis.

Prof. Dr. Jens-Uwe Sommer,
Prof. Dr. Carsten Werner, Prof. Dr. Andreas Fery, Prof. Dr. Markus Stommel