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Gemeinsames Kolloquium der Physik und Chemie
Am Dienstag, dem 1. November 2022, spricht um 17:15 Uhr im Faraday-Hörsaal,
Dr. Tobias Gensch Department of Chemistry, TU Berlin
“Data-driven workflows to guide experiments and understand ligand effects in catalysis“
Traditionally, discovery in catalysis is an empirical process that is mostly reliant on human knowledge and intuition and often involves extensive, iterative trial-and-error screening rather than quantitative predictions. This is a consequence of the very large and highly multidimensional search space where even minor changes in the catalyst structures can have dramatic effects on the activity, selectivity and stability of catalysts. Thus, there is a high potential for improvements by transitioning to data-driven workflows where all relevant data is utilized and where machine learning tools can guide, predict and explain experiments.
We have demonstrated this potential in several applications using chemical space representations based on high-level physicochemical descriptors, mostly in the context of cross-coupling with phosphorus-based ligands. Even in the absence of experimental data, this can be used to design ligand sets that maximize information on catalyst effects in the least number of reactions. Regression modelling with interpretable descriptors can offer insight into reaction mechanisms and the precise ligand effects in catalysis. Combined with virtual libraries, such regression models can provide specific predictions for improved ligand structures. We demonstrated this by suggesting ligands for enantiospecific alkyl-Suzuki coupling from a virtual library of 300 000 phosphorus ligands. Even very simple classification tools proved useful in this context, for example to reveal surprisingly general ligand effects on catalyst speciation in cross-coupling.