Study of the structure and reactivity of catalytic systems of Pd-photoredox reactions assisted by machine learning techniques

Abstract

Transition metal-photoredox reactions have become a growing research area in the past few years. In particular, for Pd-photoredox reactions, the dual function of the catalyst plays an important role in the development of this methodology. The palladium complex acts as a photoredox catalyst maintaining its classical versatility in subsequent reactions, allowing the formation of new bonds, what makes possible for transformations inaccessible to classical palladium ground state chemistry to be carried out from the excited state of the catalyst.Since this new palladium chemistry is still at an early stage of investigation, several aspects of mechanism, reactivity and reaction scope still need to be explored in order to fully apply this strategy that already demonstrates great synthetic potential. Therefore, in this research project, we intend to combine machine learning techniques with the development of methodology and study of reaction mechanism in order to systematically evaluate catalytic systems with desirable electronic and steric properties to access new chemical transformations using Pd-photoredox strategies in high selectivity levels, including in their asymmetric versions.