The oxidative addition of aryl halides to bipyridine- or phenanthroline-ligated nickel­(I) is a commonly proposed step in nickel catalysis. However, there is a scarcity of complexes of this type that both are well-defined and undergo oxidative addition with aryl halides, hampering organometallic studies of this process. We report the synthesis of a well-defined Ni­(I) complex, [(CO2Etbpy)­NiICl]4 (1). Its solution-phase speciation is characterized by a significant population of monomer and a redox equilibrium that can be perturbed by π-acceptors and σ-donors. 1 reacts readily with aryl bromides, and mechanistic studies are consistent with a pathway proceeding through an initial Ni­(I) → Ni­(III) oxidative addition to form a Ni­(III) aryl species. Such a process was demonstrated stoichiometrically for the first time, affording a structurally characterized Ni­(III) aryl complex.

Organic chemistry is replete with complex relationships: for example, how a reactant’s structure relates to the resulting product formed; how reaction conditions relate to yield; how a catalyst’s structure relates to enantioselectivity. Questions like these are at the foundation of understanding reactivity and developing novel and improved reactions. An approach to probing these questions that is both longstanding and contemporary is data-driven modeling. Here, we provide a synopsis of the history of data-driven modeling in organic chemistry and the terms used to describe these endeavors. We include a timeline of the steps that led to its current state. The case studies included highlight how, as a community, we have advanced physical organic chemistry tools with the aid of computers and data to augment the intuition of expert chemists and to facilitate the prediction of structure–activity and structure–property relationships.

While the oxidative addition of Ni­(I) to aryl iodides has been commonly proposed in catalytic methods, an in-depth mechanistic understanding of this fundamental process is still lacking. Herein, we describe a detailed mechanistic study of the oxidative addition process using electroanalytical and statistical modeling techniques. Electroanalytical techniques allowed rapid measurement of the oxidative addition rates for a diverse set of aryl iodide substrates and four classes of catalytically relevant complexes (Ni­(MeBPy), Ni­(MePhen), Ni­(Terpy), and Ni­(BPP)). With >200 experimental rate measurements, we were able to identify essential electronic and steric factors impacting the rate of oxidative addition through multivariate linear regression models. This has led to a classification of oxidative addition mechanisms, either through a three-center concerted or halogen-atom abstraction pathway based on the ligand type. A global heat map of predicted oxidative addition rates was created and shown applicable to a better understanding of the reaction outcome in a case study of a Ni-catalyzed coupling reaction.