No abstract

A method for direct cross coupling between unactivated C(sp3)−H bonds and chloroformates has been accomplished via nickel and photoredox catalysis. A diverse range of feedstock chemicals, such as (a)cyclic alkanes and toluenes, along with late-stage intermediates, undergo intermolecular C−C bond formation to afford esters under mild conditions using only 3 equiv of the C−H partner. Site selectivity is predictable according to bond strength and polarity trends that are consistent with the intermediacy of a chlorine radical as the hydrogen atom-abstracting species.

Ni/photoredox catalysis has emerged as a powerful platform for C(sp 2 )-C(sp 3 ) bond formation. While many of these methods typically employ aryl bromides as the C(sp 2 ) coupling partner, a variety of aliphatic radical sources have been investigated. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because nonstandardized sets of aryl bromides are used in scope evaluation. Herein we report a Ni/photoredox-catalyzed (deutero)methylation and alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. Reaction development, mechanistic studies, and late-stage derivatization of a biologically-relevant aryl chloride, fenofibrate, are presented. Then, we describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing scope examples from published Ni/photoredox methods on this same chemical space, we identify areas of sparse coverage and high versus low average yields, enabling comparisons between prior art and this new method. Additionally, we demonstrate that the systematically-selected scope of aryl bromides can be used to quantify population-wide reactivity trends with supervised machine learning.

Technical analysis of the previously published random forest model and its ability to predict reactivity for unseen molecules.

Photocatalysts convert light into potent reactivity. Here, we report a biohybrid catalyst in which a photosynthetic protein performs broad-spectrum light absorption and subsequent energy transfer to a conjugated photocatalyst, leading to improved yields in test reactions. This strategy has the potential to be generalized for applications in industrial and biological catalysis.