Identifying general reaction conditions by bandit optimization.

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired, especially in the pharmaceutical and chemical industries1-6. Although many approaches are available to evaluate the general applicability of developed conditions, a universal approach to efficiently discover these conditions during optimizations is rare. Here we report the design, implementation and application of reinforcement learning bandit optimization models7-10 to identify generally applicable conditions by efficient condition sampling and evaluation of experimental feedback. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions, with up to 31% improvement over baselines that mimic state-of-the-art optimization approaches. A palladium-catalysed imidazole C-H arylation reaction, an aniline amide coupling reaction and a phenol alkylation reaction were investigated experimentally to evaluate use cases and functionalities of the bandit optimization model in practice. In all three cases, the reaction conditions that were most generally applicable yet not well studied for the respective reaction were identified after surveying less than 15% of the expert-designed reaction space.

Bayesian reaction optimization as a tool for chemical synthesis.

Reaction optimization is fundamental to synthetic chemistry, from optimizing the yield of industrial processes to selecting conditions for the preparation of medicinal candidates1. Likewise, parameter optimization is omnipresent in artificial intelligence, from tuning virtual personal assistants to training social media and product recommendation systems2. Owing to the high cost associated with carrying out experiments, scientists in both areas set numerous (hyper)parameter values by evaluating only a small subset of the possible configurations. Bayesian optimization, an iterative response surface-based global optimization algorithm, has demonstrated exceptional performance in the tuning of machine learning models3. Bayesian optimization has also been recently applied in chemistry4-9; however, its application and assessment for reaction optimization in synthetic chemistry has not been investigated. Here we report the development of a framework for Bayesian reaction optimization and an open-source software tool that allows chemists to easily integrate state-of-the-art optimization algorithms into their everyday laboratory practices. We collect a large benchmark dataset for a palladium-catalysed direct arylation reaction, perform a systematic study of Bayesian optimization compared to human decision-making in reaction optimization, and apply Bayesian optimization to two real-world optimization efforts (Mitsunobu and deoxyfluorination reactions). Benchmarking is accomplished via an online game that links the decisions made by expert chemists and engineers to real experiments run in the laboratory. Our findings demonstrate that Bayesian optimization outperforms human decisionmaking in both average optimization efficiency (number of experiments) and consistency (variance of outcome against initially available data). Overall, our studies suggest that adopting Bayesian optimization methods into everyday laboratory practices could facilitate more efficient synthesis of functional chemicals by enabling better-informed, data-driven decisions about which experiments to run.

Mechanism of Ni-Catalyzed Photochemical Halogen Atom-Mediated C(sp3)-H Arylation.

Within the context of Ni photoredox catalysis, halogen atom photoelimination from Ni has emerged as a fruitful strategy for enabling hydrogen atom transfer (HAT)-mediated C(sp3)-H functionalization. Despite the numerous synthetic transformations invoking this paradigm, a unified mechanistic hypothesis that is consistent with experimental findings on the catalytic systems and accounts for halogen radical formation and facile C(sp2)-C(sp3) bond formation remains elusive. We employ kinetic analysis, organometallic synthesis, and computational investigations to decipher the mechanism of a prototypical Ni-catalyzed photochemical C(sp3)-H arylation reaction. Our findings revise the previous mechanistic proposals, first by examining the relevance of SET and EnT processes from Ni intermediates relevant to the HAT-based arylation reaction. Our investigation highlights the ability for blue light to promote efficient Ni-C(sp2) bond homolysis from cationic NiIII and C(sp2)-C(sp3) reductive elimination from bipyridine NiII complexes. However interesting, the rates and selectivities of these processes do not account for the productive catalytic pathway. Instead, our studies support a mechanism that involves halogen atom evolution from in situ generated NiII dihalide intermediates, radical capture by a NiII(aryl)(halide) resting state, and key C-C bond formation from NiIII. Oxidative addition to NiI, as opposed to Ni0, and rapid NiIII/NiI comproportionation play key roles in this process. The findings presented herein offer fundamental insight into the reactivity of Ni in the broader context of catalysis.

Broad Survey of Selectivity in the Heterogeneous Hydrogenation of Heterocycles.

A broad survey of heterogeneous hydrogenation catalysts has been conducted for the reduction of heterocycles commonly found in pharmaceuticals. The comparative reactivity of these substrates is reported as a function of catalyst, temperature, and hydrogen pressure. This analysis provided several catalysts with complementary reactivity between substrates. We then explored a series of bisheterocyclic substrates that provided an intramolecular competition of heterocycle hydrogenation reactivity. In several cases, complete selectivity could be achieved for reduction of one heterocycle and isolated yields are reported. A general trend in reactivity is inferred in which quinoline is the most reactive, followed by pyrazine, then pyrrole and with pyridine being the least reactive.

Synthesis of Nickel(I)-Bromide Complexes via Oxidation and Ligand Displacement: Evaluation of Ligand Effects on Speciation and Reactivity.

Nickel's +1 oxidation state has received much interest due to its varied and often enigmatic behavior in increasingly popular catalytic methods. In part, the lack of understanding about NiI results from common synthetic strategies limiting the breadth of complexes that are accessible for mechanistic study and catalyst design. We report an oxidative approach using tribromide salts that allows for the generation of a well-defined precursor, [NiI(COD)Br]2, as well as several new NiI complexes. Included among them are complexes bearing bulky monophosphines, for which structure-speciation relationships are established and catalytic reactivity in a Suzuki-Miyaura coupling (SMC) is investigated. Notably, these routes also allow for the synthesis of well-defined monomeric t-Bubpy-bound NiI complexes, which has not previously been achieved. These complexes, which react with aryl halides, can enable previously challenging mechanistic investigations and present new opportunities for catalysis and synthesis.

A General Photocatalytic Strategy for Nucleophilic Amination of Primary and Secondary Benzylic C-H Bonds.

We report a visible-light photoredox-catalyzed method that enables nucleophilic amination of primary and secondary benzylic C(sp3)-H bonds. A novel amidyl radical precursor and organic photocatalyst operate in tandem to transform primary and secondary benzylic C(sp3)-H bonds into carbocations via sequential hydrogen atom transfer (HAT) and oxidative radical-polar crossover. The resulting carbocation can be intercepted by a variety of N-centered nucleophiles, including nitriles (Ritter reaction), amides, carbamates, sulfonamides, and azoles, for the construction of pharmaceutically relevant C(sp3)-N bonds under unified reaction conditions. Mechanistic studies indicate that HAT is amidyl radical-mediated and that the photocatalyst operates via a reductive quenching pathway. These findings establish a mild, metal-free, and modular protocol for the rapid diversification of C(sp3)-H bonds to a library of aminated products.

Branched-Selective Cross-Electrophile Coupling of 2-Alkyl Aziridines and (Hetero)aryl Iodides Using Ti/Ni Catalysis.

The arylation of 2-alkyl aziridines by nucleophilic ring-opening or transition-metal-catalyzed cross-coupling enables facile access to biologically relevant ß-phenethylamine derivatives. However, both approaches largely favor C-C bond formation at the less-substituted carbon of the aziridine, thus enabling access to only linear products. Consequently, despite the attractive bond disconnection that it poses, the synthesis of branched arylated products from 2-alkyl aziridines has remained inaccessible. Herein, we address this long-standing challenge and report the first branched-selective cross-coupling of 2-alkyl aziridines with aryl iodides. This unique selectivity is enabled by a Ti/Ni dual-catalytic system. We demonstrate the robustness of the method by a twofold approach: an additive screening campaign to probe functional group tolerance and a feature-driven substrate scope to study the effect of the local steric and electronic profile of each coupling partner on reactivity. Furthermore, the diversity of this feature-driven substrate scope enabled the generation of predictive reactivity models that guided mechanistic understanding. Mechanistic studies demonstrated that the branched selectivity arises from a TiIII-induced radical ring-opening of the aziridine.

A Machine Learning Approach to Model Interaction Effects: Development and Application to Alcohol Deoxyfluorination.

The application of machine learning (ML) techniques to model high-throughput experimentation (HTE) datasets has seen a recent rise in popularity. Nevertheless, the ability to model the interplay between reaction components, known as interaction effects, with ML remains an outstanding challenge. Using a simulated HTE dataset, we find that the presence of irrelevant features poses a strong obstacle to learning interaction effects with common ML algorithms. To address this problem, we propose a two-part statistical modeling approach for HTE datasets: classical analysis of variance of the experiment to identify systematic effects that impact reaction yield across the experiment followed by regression of individual effects using chemistry-informed features. To illustrate this methodology, we use our previously published alcohol deoxyfluorination dataset comprising 740 reactions to build a compact, interpretable generalized additive model that accounts for each significant effect observed in the dataset. We achieve a sizeable performance boost compared to our previously published random forest model, reducing mean absolute error from 18 to 13% and root-mean-squared error from 22 to 17% on a newly generated validation set. Finally, we demonstrate that this approach can facilitate the generation of new mechanistic hypotheses, which, when probed experimentally, can lead to a deeper understanding of chemical reactivity.

Interrogating the Mechanistic Features of Ni(I)-Mediated Aryl Iodide Oxidative Addition Using Electroanalytical and Statistical Modeling Techniques.

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.

Strategies for Nucleophilic C(sp3)-(Radio)Fluorination.

This Perspective surveys the progress and current limitations of nucleophilic fluorination methodologies. Despite the long and rich history of C(sp3)-F bond construction in chemical research, the inherent challenges associated with this transformation have largely constrained nucleophilic fluorination to a privileged reaction platform. In recent years, the Doyle group─along with many others─has pursued the study and development of this transformation with the intent of generating deeper mechanistic understanding, developing user-friendly fluorination reagents, and contributing to the invention of synthetic methods capable of enabling radiofluorination. Studies from our laboratory are discussed along with recent developments from others in this field. Fluoride reagent development and the mechanistic implications of reagent identity are highlighted. We also outline the chemical space inaccessible by current synthetic technologies and a series of future directions in the field that can potentially fill the existing dark spaces.

Bioinspired Supercharging of Photoredox Catalysis for Applications in Energy and Chemical Manufacturing.

For more than a decade, photoredox catalysis has been demonstrating that when photoactive catalysts are irradiated with visible light, reactions occur under milder, cheaper, and environmentally friendlier conditions. Furthermore, this methodology allows for the activation of abundant chemicals into valuable products through novel mechanisms that are otherwise inaccessible. The photoredox approach, however, has been primarily used for pharmaceutical applications, where its implementation has been highly effective, but typically with a more rudimentary understanding of the mechanisms involved in these transformations. From a global perspective, the manufacture of everyday chemicals by the chemical industry as a whole currently accounts for 10% of total global energy consumption and generates 7% of the world's greenhouse gases annually. In this context, the Bio-Inspired Light-Escalated Chemistry (BioLEC) Energy Frontier Research Center (EFRC) was founded to supercharge the photoredox approach for applications in chemical manufacturing aimed at reducing its energy consumption and emissions burden, by using bioinspired schemes to harvest multiple electrons to drive endothermically uphill chemical reactions. The Center comprises a diverse group of researchers with expertise that includes synthetic chemistry, biophysics, physical chemistry, and engineering. The team works together to gain a deeper understanding of the mechanistic details of photoredox reactions while amplifying the applications of these light-driven methodologies.In this Account, we review some of the major advances in understanding, approach, and applicability made possible by this collaborative Center. Combining sophisticated spectroscopic tools and photophysics tactics with enhanced photoredox reactions has led to the development of novel techniques and reactivities that greatly expand the field and its capabilities. The Account is intended to highlight how the interplay between disciplines can have a major impact and facilitate the advance of the field. For example, techniques such as time-resolved dielectric loss (TRDL) and pulse radiolysis are providing mechanistic insights not previously available. Hypothesis-driven photocatalyst design thus led to broadening of the scope of several existing transformations. Moreover, bioconjugation approaches and the implementation of triplet-triplet annihilation mechanisms created new avenues for the exploration of reactivities. Lastly, our multidisciplinary approach to tackling real-world problems has inspired the development of efficient methods for the depolymerization of lignin and artificial polymers.

Ni/Photoredox-Catalyzed C(sp3)-C(sp3) Coupling between Aziridines and Acetals as Alcohol-Derived Alkyl Radical Precursors.

Aziridines are readily available C(sp3) precursors that afford valuable ß-functionalized amines upon ring opening. In this article, we report a Ni/photoredox methodology for C(sp3)-C(sp3) cross-coupling between aziridines and methyl/1°/2° aliphatic alcohols activated as benzaldehyde dialkyl acetals. Orthogonal activation modes of each alkyl coupling partner facilitate cross-selectivity in the C(sp3)-C(sp3) bond-forming reaction: the benzaldehyde dialkyl acetal is activated via hydrogen atom abstraction and ß-scission via a bromine radical (generated in situ from single-electron oxidation of bromide), whereas the aziridine is activated at the Ni center via reduction. We demonstrate that an Ni(II) azametallacycle, conventionally proposed in aziridine cross-coupling, is not an intermediate in the productive cross-coupling. Rather, stoichiometric organometallic and linear free energy relationship studies indicate that aziridine activation proceeds via Ni(I) oxidative addition, a previously unexplored elementary step.

Oxidative Addition of Aryl Halides to a Ni(I)-Bipyridine Complex.

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.

Structure-Reactivity Relationships of Buchwald-Type Phosphines in Nickel-Catalyzed Cross-Couplings.

The dialkyl-ortho-biaryl class of phosphines, commonly known as Buchwald-type ligands, are among the most important phosphines in Pd-catalyzed cross-coupling. These ligands have also been successfully applied to several synthetically valuable Ni-catalyzed cross-coupling methodologies and, as demonstrated in this work, are top performing ligands in Ni-catalyzed Suzuki Miyaura Coupling (SMC) and C-N coupling reactions, even outperforming commonly employed bisphosphines like dppf in many circumstances. However, little is known about their structure-reactivity relationships (SRRs) with Ni, and limited examples of well-defined, catalytically relevant Ni complexes with Buchwald-type ligands exist. In this work, we report the analysis of Buchwald-type phosphine SRRs in four representative Ni-catalyzed cross-coupling reactions. Our study was guided by data-driven classification analysis, which together with mechanistic organometallic studies of structurally characterized Ni(0), Ni(I), and Ni(II) complexes allowed us to rationalize reactivity patterns in catalysis. Overall, we expect that this study will serve as a platform for further exploration of this ligand class in organonickel chemistry as well as in the development of new Ni-catalyzed cross-coupling methodologies.

Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources.

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)-C(sp3) bond formation. While many of these methods typically employ aryl bromides as the C(sp2) 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 and reveal sources of possible functional group incompatibility with supervised machine learning.

A Multi-Objective Active Learning Platform and Web App for Reaction Optimization.

We report the development of an open-source experimental design via Bayesian optimization platform for multi-objective reaction optimization. Using high-throughput experimentation (HTE) and virtual screening data sets containing high-dimensional continuous and discrete variables, we optimized the performance of the platform by fine-tuning the algorithm components such as reaction encodings, surrogate model parameters, and initialization techniques. Having established the framework, we applied the optimizer to real-world test scenarios for the simultaneous optimization of the reaction yield and enantioselectivity in a Ni/photoredox-catalyzed enantioselective cross-electrophile coupling of styrene oxide with two different aryl iodide substrates. Starting with no previous experimental data, the Bayesian optimizer identified reaction conditions that surpassed the previously human-driven optimization campaigns within 15 and 24 experiments, for each substrate, among 1728 possible configurations available in each optimization. To make the platform more accessible to nonexperts, we developed a graphical user interface (GUI) that can be accessed online through a web-based application and incorporated features such as condition modification on the fly and data visualization. This web application does not require software installation, removing any programming barrier to use the platform, which enables chemists to integrate Bayesian optimization routines into their everyday laboratory practices.

Synthetic and Mechanistic Implications of Chlorine Photoelimination in Nickel/Photoredox C(sp3)-H Cross-Coupling.

In recent years, the development of light-driven reactions has contributed numerous advances in synthetic organic chemistry. A particularly active research area combines photoredox catalysis with nickel catalysis to accomplish otherwise inaccessible cross-coupling reactions. In these reactions, the photoredox catalyst absorbs light to generate an electronically excited charge-transfer state that can engage in electron or energy transfer with a substrate and the nickel catalyst. Our group questioned whether photoinduced activation of the nickel catalyst itself could also contribute new approaches to cross-coupling. Over the past 5 years, we have sought to advance this hypothesis for the development of a suite of mild and site-selective C(sp3)-H cross-coupling reactions with chloride-containing coupling partners via photoelimination of a Ni-Cl bond.On the basis of a report from the Nocera laboratory, we reasoned that photolysis of a Ni(III) aryl chloride species, generated by single-electron oxidation of a typical Ni(II) intermediate in cross-coupling, might allow for the catalytic generation of chlorine atoms. Combining this with the ability of Ni(II) to accept alkyl radicals, we hypothesized that photocatalytically generated chlorine atoms could mediate hydrogen atom transfer (HAT) with C(sp3)-H bonds to generate a substrate-derived alkyl radical that is captured by the Ni center in cross-coupling. A photoredox catalyst was envisioned to promote the necessary single-electron oxidation and reduction of the Ni catalyst to facilitate an overall redox-neutral process. Overall, this strategy would offer a visible-light-driven mechanism for chlorine radical formation enabled by the sequential capture of two photons.As an initial demonstration, we developed a Ni/photoredox-catalyzed α-oxy C(sp3)-H arylation of cyclic and acyclic ethers. This method was extended to a mild formylation of abundant and complex aryl chlorides through selective 2-functionalization of 1,3-dioxolane. Seeking to develop a suite of reactions that introduce carbon at all different oxidation states, we explored C(sp3)-H cross-coupling with trimethyl orthoformate, a common laboratory solvent. We found that trimethyl orthoformate serves as a source of methyl radical for a methylation reaction via ß-scission from a tertiary radical generated upon chlorine-mediated HAT. Since chlorine radical is capable of abstracting unactivated C(sp3)-H bonds, our efforts have also been directed at cross-coupling with a range of feedstock chemicals, such as alkanes and toluenes, along with late-stage intermediates, using chloroformates as coupling partners. Overall, this platform enables access to valuable synthetic transformations with (hetero)aryl chlorides, which despite being the most ubiquitous and inexpensive aryl halide coupling partners, are rarely reactive in Ni/photoredox catalysis.Little is known about the photophysics and photochemistry of organometallic Ni complexes relevant to cross-coupling. We have conducted mechanistic investigations, including computational, spectroscopic, emission quenching, and stoichiometric oxidation studies, of Ni(II) aryl halide complexes common to Ni/photoredox reactions. These studies indicate that chlorine radical generation from excited Ni(III) is operative in the described C(sp3)-H functionalization methods. More generally, the studies illustrate that the photochemistry of cross-coupling catalysts cannot be ignored in metallaphotoredox reactions. We anticipate that further mechanistic understanding should facilitate new catalyst design and lead to the development of new synthetic methods.

Predicting Reaction Yields via Supervised Learning.

Numerous disciplines, such as image recognition and language translation, have been revolutionized by using machine learning (ML) to leverage big data. In organic synthesis, providing accurate chemical reactivity predictions with supervised ML could assist chemists with reaction prediction, optimization, and mechanistic interrogation.To apply supervised ML to chemical reactions, one needs to define the object of prediction (e.g., yield, enantioselectivity, solubility, or a recommendation) and represent reactions with descriptive data. Our group's effort has focused on representing chemical reactions using DFT-derived physical features of the reacting molecules and conditions, which serve as features for building supervised ML models.In this Account, we present a review and perspective on three studies conducted by our group where ML models have been employed to predict reaction yield. First, we focus on a small reaction data set where 16 phosphine ligands were evaluated in a single Ni-catalyzed Suzuki-Miyaura cross-coupling reaction, and the reaction yield was modeled with linear regression. In this setting, where the regression complexity is strongly limited by the amount of available data, we emphasize the importance of identifying single features that are directly relevant to reactivity. Next, we focus on models trained on two larger data sets obtained with high-throughput experimentation (HTE). With hundreds to thousands of reactions available, more complex models can be explored, for example, models that algorithmically perform feature selection from a broad set of candidate features. We examine how a variety of ML algorithms model these data sets and how well these models generalize to out-of-sample substrates. Specifically, we compare the ML models that use DFT-based featurization to a baseline model that is obtained with features that carry no physical information, that is, random features, and to a naive non-ML model that averages yields of reactions that share the same conditions and substrate combinations. We find that for only one of the two data sets, DFT-based featurization leads to a significant, although moderate, out-of-sample prediction improvement. The source of this improvement was further isolated to specific features which allowed us to formulate a testable mechanistic hypothesis that was validated experimentally. Finally, we offer remarks on supervised ML model building on HTE data sets focusing on algorithmic improvements in model training.Statistical methods in chemistry have a rich history, but only recently has ML gained widespread attention in reaction development. As the untapped potential of ML is explored, novel tools are likely to arise from future research. Our studies suggest that supervised ML can lead to improved predictions of reaction yield over simpler modeling methods and facilitate mechanistic understanding of reaction dynamics. However, further research and development is required to establish ML as an indispensable tool in reactivity modeling.

Phosphine/Photoredox Catalyzed Anti-Markovnikov Hydroamination of Olefins with Primary Sulfonamides via α-Scission from Phosphoranyl Radicals.

New strategies to access radicals from common feedstock chemicals hold the potential to broadly impact synthetic chemistry. We report a dual phosphine and photoredox catalytic system that enables direct formation of sulfonamidyl radicals from primary sulfonamides. Mechanistic investigations support that the N-centered radical is generated via α-scission of the P-N bond of a phosphoranyl radical intermediate, formed by sulfonamide nucleophilic addition to a phosphine radical cation. As compared to the recently well-explored ß-scission chemistry of phosphoranyl radicals, this strategy is applicable to activation of N-based nucleophiles and is catalytic in phosphine. We highlight application of this activation strategy to an intermolecular anti-Markovnikov hydroamination of unactivated olefins with primary sulfonamides. A range of structurally diverse secondary sulfonamides can be prepared in good to excellent yields under mild conditions.

Ni/Photoredox-Catalyzed Enantioselective Cross-Electrophile Coupling of Styrene Oxides with Aryl Iodides.

A Ni/photoredox-catalyzed enantioselective reductive coupling of styrene oxides and aryl iodides is reported. This reaction affords access to enantioenriched 2,2-diarylalcohols from racemic epoxides via a stereoconvergent mechanism. Multivariate linear regression (MVLR) analysis with 29 bioxazoline (BiOx) and biimidazoline (BiIm) ligands revealed that enantioselectivity correlates with electronic properties of the ligands, with more electron-donating ligands affording higher ee's. Experimental and computational mechanistic studies were conducted, lending support to the hypothesis that reductive elimination is enantiodetermining and the electronic character of the ligands influences the enantioselectivity by altering the position of the transition state structure along the reaction coordinate. This study demonstrates the benefits of utilizing statistical modeling as a platform for mechanistic understanding and provides new insight into an emerging class of chiral ligands for stereoconvergent Ni and Ni/photoredox cross-coupling.