Transition-metal complexes are attractive targets for the design of catalysts and functional
materials. The behavior of the metal–organic bond, while very tunable for achieving target …

Discovering new reactions, optimizing their performance, and extending the synthetically
accessible chemical space are critical drivers for major technological advances and more …

The field of predictive chemistry relates to the development of models able to describe how
molecules interact and react. It encompasses the long-standing task of computer-aided …

Owing to the unknown correlation of a metal's ligand and its resulting preferred speciation in
terms of oxidation state, geometry, and nuclearity, a rational design of multinuclear catalysts …

Conspectus For the past two decades, linear free energy scaling relationships and volcano
plots have seen frequent use as computational tools that aid in understanding and …

The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …

Hundreds of catalytic methods are developed each year to meet the demand for high-purity
chiral compounds. The computational design of enantioselective organocatalysts remains a …

Non-heme iron halogenases and hydroxylases activate inert C–H bonds to selectively
catalyze the functionalization of diverse biological products under physiological conditions …

We report the transition metal quantum mechanics (tmQM) data set, which contains the
geometries and properties of a large transition metal–organic compound space. tmQM …

Predicting electronic energies, densities, and related chemical properties can facilitate the
discovery of novel catalysts, medicines, and battery materials. However, existing machine …