With great advancements in digital assets around us, chemistry is also not left behind to benefit from chemical data and data-driven approaches with machine learning (ML) algorithms. In our research group, we coalesce several ML with experiments and quantum-chemistry tools to accelerate optimization and mechanistic understanding of catalytic reactions and their various components.

Selected publications:

A) S. Dana,' N. K. Pandit,' P. Boos,' T. von Münchow, S. E. Peters, S. Trienes, L. Haberstock, R. Herbst-Irmer, D. Stalke, L. Ackermann, "Parametrization of κ2-N,O-Oxazoline Preligands for Enantioselective Cobaltaelectro-Catalyzed C–H Activations", ACS Catal. 2025, in press. 'These authors contributed equally Abstract

B) X. Hou, S. Li, J. Frey, X. Hong, L. Ackermann, "Machine learning-guided yield optimization for palladaelectro-catalyzed annulation reaction", Chem 2024, 10, 2283-2294. Abstract

C) Z-J. Zhang, M. M. Simon, S. Yu, S-W. Li, X. Chen, S. Cattani, X. Hong, L. Ackermann, "Nickel-Catalyzed Atroposelective C–H Alkylation Enabled by Bimetallic Catalysis with Air-Stable Heteroatom-Substituted Secondary Phosphine Oxide Preligands", J. Am. Chem. Soc. 2024, 146, 13, 9172–9180. Abstract

D) Z. Lin,' U. Dhawa,' X. Hou,' M. Surke, B. Yuan, S.-W. Li, Y.-C. Liou, M. J. Johansson, L.-C. Xu, C.-H. Chao, X. Hong, L. Ackermann, Nat. Commun 2023, 14, 4224. Abstract

E) Z-J. Zhang, S.-W. L, J. C. A. Oliveira, Y. Li, X. Chen, S.-Q. Zhang, L.-C. Xu, T. Rogge, X. Hong, L. Ackermann, "Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C–N axial chirality via cobalt catalysis" Nat. Commun 2023, 14, 3149. Abstract