C03 studies supramolecular H-bonding networks and their impact on photochemistry to establish them as tools to control and steer reactivity and selectivity in photocatalysis. The first funding period focused on creating supramolecular photocatalytic environments for polyene cyclizations, thereby uncovering an unprecedented mechanism for eosin Y-catalyzed hydrofunctionalizations. In the second funding period we will continue these investigations and expand the reaction scope to unactivated alkene and aryl functionalizations. The project will also explore the properties of photoacids in F-alcohol environments and their suitability in transformations usually requiring strong acids. 

Organic Chemistry, Synthesis
Prof. Dr. Tanja Gulder

Project C07 aims to enhance our understanding of light-driven chemical reactions within the CRC by applying advanced machine learning methods. On one side, computationally efficient machine learning-driven photodynamics simulations on longer time and length scales will be used to study long-lived excited states of ring-contracted flavins and reactions at interfaces, respectively. In addition, machine learning will be used to guide rational catalyst and substrate design and will be integrated into experimental workflows to optimize reaction conditions and experimental planning.