Paving the road towards automated homogeneous catalyst design.

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

Photoreactions with a Twist: Atropisomerism-Driven Divergent Reactivity of Enones with UV and Visible Light.

Light-induced transformation of atropisomeric and achiral enones displays divergent reactivity. Photocyclization leading to 3,4-dihydroquinolin-2-one was observed with achiral enone carboxamide, whereas the atropisomeric enone carboxamides underwent hydrogen abstraction leading to spiro-ß-lactams. Detailed photochemical, photophysical, and theoretical investigations have provided insight into this divergent reactivity and selectivity.