Machine Learning Insights into Catalyst-Substrate Assemblies for Rational Design (C07*)

• Subject Area: Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
• Term: since 2026
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 444632635

The project 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.

• DFG Programme: CRC/Transregios
• Subproject of: TRR 325: Assembly Controlled Chemical Photocatalysis
• Applicant Institution: Universität Regensburg
• Project Head: Professorin Dr. Julia Westermayr