Computational chemistry is an increasingly powerful tool to understand chemical reactions, especially organometallic catalysis. Two critical aspects must be considered for a successful analysis of such systems: the electronic structure calculations of energies, geometries and reactions to produce a suitable mechanism of the reaction, and a kinetic model that can extract that information and predict the rate of reaction. The ability of computational chemistry to elucidate reaction energetics, to use this information to improve catalytic cycles and to develop new catalytic schemes will ultimately head to the experimental realization of faster, more robust and economically efficient new catalysts.

Heavy atom quantum mechanical tunneling has turned in the 21st century from a chemical curiosity to an established mini-discipline. The large mass of second period atoms (from Li to Ne) was thought to be a deterrent for a tunneling process, but they can indeed react provided the energy barriers are low and narrow. Reactions involving carbenes, antiaromatic systems, Jahn-Teller distortions and even fluorine jumping close to the absolute zero can be explained and predicted through computational chemistry. The detection of these systems can produce a plethora of reactions with novel chemical, physical and spectroscopical features.

The “Energy Span Model”: Analysis of catalytic efficiency and the understanding of the kinetic roots of catalytic cycles
With the development of computational tools the calculation of reaction pathways for catalytic systems has become a routine job. But still, a missing link between the calculated reaction profile and the kinetics of a catalytic cycle makes it impossible to resolve the basic question: What makes for a good catalytic cycle? The “Energy Span Model”, is a kinetic methodology that permits the calculation of the TOF (turn-over frequency) and TON (turn-over number) of several catalytic cycles from the energy level of the intermediates and transition states. In other words, we provide a mathematical tool that enables:
I) To choose the “right” pathway among alternative proposed cycles.
II) To understand the fundamental factors that shape the kinetics of the cycles.
III) To suggest improvements for a catalyst.

Halogen bonding is the new player in the world of non-covalent interactions. In the 21st century similar systems are continuously being discovered, including chalcogen, pnictogen, tetrel, beryllium, or aerogen bonds, that can come as σ-holes, π-holes, or even δ-holes. The bonding pattern includes an electrostatic hole (a positive region in the electrostatic potential) and the possibility of bonding by charge transfer. We are developing a model that explains the reasons why these two types of interactions always come hand-in-hand both in directionality and strength, and elucidates on the agonistic effect between the different interactions.