“Recent Progress on Machine Learning and Data-Based Science in Asymmetric Catalysis”
M. Sc. Lucca Pfitzer; Prof. René Peters (project B06, Cooperative asymmetric dual / multiple activation catalysis under confinement)
Asymmetric catalysis is a cornerstone of modern synthetic chemistry, yet the rational prediction of enantioselectivity remains one of its central challenges. Traditionally, catalyst development has relied heavily on empirical optimization and mechanistic intuition, often requiring extensive experimental (or computational) screening. In recent years, data-driven approaches in combination with new machine learning algorithms have emerged as powerful tools to complement classical strategies by uncovering quantitative structure–selectivity relationships and to accelerate catalyst optimization.
Date: 07/05/2026
Time: 9:00 am – 10:00 am
Place: Lecture Hall 55.12, Pfaffenwaldring 55
