The molecular electrostatic potential (MESP) V (r) data derived from a reliable quantum
chemical method has been widely used for the interpretation and prediction of various …

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 …

The design of molecular catalysts typically involves reconciling multiple conflicting property
requirements, largely relying on human intuition and local structural searches. However, the …

Although machine learning bears enormous potential to accelerate developments in
homogeneous catalysis, the frequent need for extensive experimental data can be a …

Ligands, especially phosphines and carbenes, can play a key role in modifying and
controlling homogeneous organometallic catalysts, and they often provide a convenient …

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 …

Atomically precise gold clusters are highly desirable due to their well‐defined structure
which allows the study of structure–property relationships. In addition, they have potential in …

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 …

The mechanistic foundation behind the identity of a phosphine ligand that best promotes a
desired reaction outcome is often non-intuitive, and thus has been addressed in numerous …

The application of computational methods in enantioselective catalysis has evolved from the
rationalization of the observed stereochemical outcome to their prediction and application to …