Machine Learning for developing and understanding novel, asymmetric 3d metal-catalyzed C–H activations

• Applicant: Professor Dr. Lutz Ackermann
• Subject Area: Organic Molecular Chemistry - Synthesis and Characterisation
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 497259673

The project employs methods of machine learning to identify novel catalysis concepts for asymmetric C-H activations with broadly available 3d-transition metals. Based on orienting studies we will thereby significantly improve the understanding of the corresponding mechanism.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2363: Utilization and Development of Machine Learning for Molecular Applications – Molecular Machine Learning