Project Area C
Solvation and Dispersion Interactions
In research area C, we will investigate such assembly and solvation effects in photoreactions and photocatalysis with the aim of developing a more general picture and rational use of dispersion interactions in photochemical synthesis.
C01: Taming Reactive Intermediates in Photocatalysis by Assembly Control
C02: Analysis of Charge Transfer Complexes, Radical Ion-Pairs and Photocatalytic Assemblies by NMR Spectroscopy
C03: Photocatalytic Csp2-Functionalizations in Confined Space
C05: Development and Application of Novel Quantum-Chemical Excited-State Methods for the Accurate Description of Photocatalytic Processes
C06: Design and Directed Evolution of Artificial Photoenzymes
C07: Machine Learning Insights into Catalyst-Substrate Assemblies for Rational Design
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.
