Das Fakultätskolloquium findet in der Regel jeden zweiten Montag im Monat während des Semesters statt (vier Termine). In- und ausländische WissenschafterInnen wie auch WissenschafterInnen unserer Fakultät geben in 45-minütigen Vorträgen Einblick in ihre Forschung.

Vor den Kolloquien findet ein Fakultätskaffee statt, im Rahmen dessen Fakultätsangehörige und Student*innen herzlich eingeladen sind, sich auf einen Austausch bei Kaffee und Kuchen zu treffen.

Programm-Koordination: Univ.-Prof. Dr. Nuno Maulide

Monday, 12th October 2026, 16:00 / Joseph Loschmidt lecture hall (LH 2) of the Faculty of Chemistry:

Prof. Anat Milo, Department of Chemistry, Ben-Gurion University of the Negev, Israel

"Can we use machine learning for mechanistic studies?"

Understanding reaction mechanisms remains one of the central challenges in catalysis, yet the increasing availability of data-driven approaches raises an important question: can machine learning be used not only to optimize reactions, but also to uncover their underlying mechanisms? Our group has explored the design, in situ modification, and mechanistic investigation of secondary-sphere interactions in organocatalysis. As an initial demonstration, the secondary sphere of N-heterocyclic carbenes (NHCs) was modified under reaction conditions using boronic acids, enabling modulation of the reactivity and selectivity of the benzoin reaction while providing insight into the underlying mechanism.^1–3 Interception of reactive intermediates in these systems further enabled the development of a mild and chemoselective aldehyde deuteration protocol,⁴ and the generality of the approach was later extended to secondary-sphere modification of amino catalysts in proline-catalyzed aldol reactions.^5,6 Beyond providing access to diverse, highly selective, and modular catalyst derivatives with minimal synthetic effort, these studies also highlight a broader challenge in homogeneous catalysis: mechanistically rich datasets are often confined to narrow structural domains and limited data-size regimes. In this talk, I will discuss how such ubiquitous, yet sparse, datasets can provide a valuable foundation for developing machine-learning strategies not only for reaction optimization, but also for mechanistic interrogation.^7–11

1.       Zak (Eshel), I. L.; Gadekar, S. C.; Milo, A. Synlett, 32, 329 (2021).
2.       Alassad, Z.; Nandi, A.; Kozuch, S.; Milo, A. J. Am. Chem. Soc. 145, 89 (2023).
3.       Gadekar, S. C.; Dhayalan, V.; Nandi, A.; Zak, I. L.; Mizrachi, M. S.; Kozuch, S.; Milo, A. ACS Catal. 11, 14561 (2021).
4.       Domb, I.; Lustosa, D. M.; Milo, A. Chem. Commun. (Camb) 58, 1950 (2022).
5.       Lustosa, D. M.; Barkai, S.; Domb, I.; Milo, A. J. Org. Chem. 87, 1850 (2022).
6.       Lustosa, D. M.; Milo, A. ACS Catal. 12, 7886 (2022).
7.       Shalit Peleg, H.; Milo, A. Angew. Chem. Int. Ed. 62, e202219070 (2023).
8.       Harnik, Y.; Shalit Peleg H, Bermano A, Milo, A. Chem. Sci., 10833 (2025).
9.       Eshel, I. L.; Barkai, S.; Barranco, S.; Pérez-Temprano, M.; Milo, A. ChemRxiv doi:10.26434/chemrxiv-2025-z0djf (2025).
10.     Barranco, S.; Eshel, I. L.; Zhang, J.; Di Matteo, M.; Milo, A. and Pérez-Temprano, M. H. Nat. Cata.l 1306 (2025).

Monday, 19th April 2027, 16:00 / Joseph Loschmidt lecture hall (LH 2) of the Faculty of Chemistry:

Prof. Dr. Christian Hertweck; Head of Biomolecular Chemistry, Leibniz Institute for Natural Product Research and Infection Biology Hans Knöll Institute