Boodsarin Sawatlon

Computational Design of Transition Metal Catalysts for Hydrodefluorination of Trifluoromethylarene using Hydrosilane Expanding upon the initial use of nickel complexes to cleave aliphatic C-F bond in the hydrodefluorination of trifluoromethylarene, we employed linear free energy scaling relationships and molecular volcano plots to elucidate the impact of N-heterocyclic carbene and phosphine ligands, as well as metals and additives, on the energy span related to catalytic activity.

Abstract The hydrogenation of carbon dioxide to formate is an intriguing reaction from both an environmental and an energy perspective, primarily due to the prospective uses of the product as a platform chemical in numerous applications such as an organic hydrogen carrier. Although several transition-metal-based catalysts have been shown to facilitate this chemical transformation, few guidelines exist on how best to tune the catalysts in order to achieve maximum activity.

The application of modern machine learning to challenges in atomistic simulation is gaining attraction. We present new machine learning models that can predict the energy of the oxidative addition process between a transition metal complex and a substrate for C-C cross-coupling reaction. In turn, this quantity can be used as a descriptor to estimate the activity of homogeneous catalysts using molecular volcano plots.