Conspectus Machine-readable chemical structure representations are foundational in all
attempts to harness machine learning for the prediction of reactivities, selectivities, and …

Advances in density functional theory (DFT) mean that it is now possible to study catalytic
reactions with sufficient accuracy that the results compare favourably with experiment. These …

Catalyst discovery is increasingly relying on computational chemistry, and many of the
computational tools are currently being automated. The state of this automation and the …

Mathematical relationships that relate chemical structure with selectivity have provided
quantitative insights underlying catalyst design and informing mechanistic studies. However …

We describe an open-source computational toolkit (AARON: An Automated Reaction
Optimizer for New catalysts) that automates the quantum mechanical geometry optimization …

Copper-catalyzed atom transfer radical polymerization (Cu-ATRP) is one of the most widely
used controlled radical polymerization techniques. Notwithstanding the extensive …

The construction of N-heterocyclic eight-membered rings with good regio-, stereo-, and
enantioselective control remains a formidable challenge in asymmetric catalysis. Herein, we …

The chemical sciences are witnessing an influx of statistics into the catalysis literature.
These developments are propelled by modern technological advancements that are leading …

P‐stereogenic compounds are widely used as ligands in asymmetric catalysis and are
present in a myriad of bioactive compounds and pharmaceuticals. Yet, their stereocontrolled …

Access to enantiopure complex molecular structures is crucial for the development of new
drugs as well as agents used in crop-protection. In this regard, numerous asymmetric …