Computations and Machine Learning

While most of our research work relies on a combination of synthetic organometallic and analytical, spectroscopic and crystallographic methods, we also routinely make use of computational and machine learning tools to gain an improved understanding of reaction mechanisms as well as electronic structures and properties. This knowledge guides our experimental studies through establishing structure activity relationships and predicting improved molecular and catalyst structures.

Selected publications:

J. F. Goebel, J. Löffler, Z. Zeng, J. Handelmann, A. Hermann, I. Rodstein, T. Gensch, V. H. Gessner,* L. J. Gooßen*
Computer-Driven Development of Ylide functionalized Phosphines for Palladium-Catalyzed Hiyama Couplings 

Angew. Chem. Int. Ed. 2023, 62, e202216160 (https://doi.org/10.1002/anie.202216160).

J. Löffler, S. M. P. Vanden Broeck, C. S. J. Cazin, S. P. Nolan,* V. H. Gessner*
Correlation of Experimental and Calculated Reaction Enthalpies with Ligand Donor Strengths
Chem. Eur. J. 2023, 29, e202300151. (https://doi.org/10.1002/chem.202300151)

H. Steinert, J. Löffler, V. H. Gessner*
Single-Site and Cooperative Bond Activation Reactions with Ylide-functionalized Tetrylenes: A Computational Study
Eur. J. Inorg. Chem. 2021, 2021, 5004-5013. https://doi.org/10.1002/ejic.202100816.

L. T. Scharf, D. M. Andrada, G. Frenking, V. H. Gessner*
The Bonding Situation in Metalated Ylides
Chem. Eur. J. 2017, 23, 4422-4434. ( https://doi.org/10.1002/chem.201605997)