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.

Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism's presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.

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.

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.

Dataset Design for Building Models of Chemical Reactivity

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.

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 β-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.

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.

Continuous flow synthesis of pyridinium salts accelerated by multi-objective Bayesian optimization with active learning

We report a human-in-the-loop implementation of the multi-objective experimental design via a Bayesian optimization platform (EDBO+) towards the optimization of butylpyridinium bromide synthesis under continuous flow conditions. The algorithm simultaneously optimized reaction yield and production rate (or space-time yield) and generated a well defined Pareto front. The versatility of EDBO+ was demonstrated by expanding the reaction space mid-campaign by increasing the upper temperature limit. Incorporation of continuous flow techniques enabled improved control over reaction parameters compared to common batch chemistry processes, while providing a route towards future automated syntheses and improved scalability. To that end, we applied the open-source Python module, nmrglue, for semi-automated nuclear magnetic resonance (NMR) spectroscopy analysis, and compared the acquired outputs against those obtained through manual processing methods from spectra collected on both low-field (60 MHz) and high-field (400 MHz) NMR spectrometers. The EDBO+ based model was retrained with these four different datasets and the resulting Pareto front predictions provided insight into the effect of data analysis on model predictions. Finally, quaternization of poly(4-vinylpyridine) with bromobutane illustrated the extension of continuous flow chemistry to synthesize functional materials.

Comparison of Monophosphine and Bisphosphine Precatalysts for Ni-Catalyzed Suzuki-Miyaura Cross-Coupling: Understanding the Role of the Ligation State in Catalysis

Practical advances in Ni-catalyzed Suzuki-Miyaura cross-coupling (SMC) have been limited by a lack of mechanistic understanding of phosphine ligand effects. While bisphosphines are commonly used in these methodologies, we have observed instances where monophosphines can provide comparable or higher levels of reactivity. Seeking to understand the role of ligation state in catalysis, we performed a head-to-head comparison study of C(sp2)-C(sp2) Ni SMCs catalyzed by mono and bisphosphine precatalysts using six distinct substrate pairings. Significant variation in optimal precatalyst was observed, with the monophosphine precatalyst tending to outperform the bisphosphines with electronically deactivated and sterically hindered substrates. Mechanistic experiments revealed a role for monoligated (P1Ni) species in accelerating the fundamental organometallic steps of the catalytic cycle, while highlighting the need for bisligated (P2Ni) species to avoid off-cycle reactivity and catalyst poisoning by heterocyclic motifs. These findings provide guidelines for ligand selection against challenging substrates and future ligand design tailored to the mechanistic demands of Ni-catalyzed SMCs.

On the use of real-world datasets for reaction yield prediction

The lack of publicly available, large, and unbiased datasets is a key bottleneck for the application of machine learning (ML) methods in synthetic chemistry. Data from electronic laboratory notebooks (ELNs) could provide less biased, large datasets, but no such datasets have been made publicly available. The first real-world dataset from the ELNs of a large pharmaceutical company is disclosed and its relationship to high-throughput experimentation (HTE) datasets is described. For chemical yield predictions, a key task in chemical synthesis, an attributed graph neural network (AGNN) performs as well as or better than the best previous models on two HTE datasets for the Suzuki-Miyaura and Buchwald-Hartwig reactions. However, training the AGNN on an ELN dataset does not lead to a predictive model. The implications of using ELN data for training ML-based models are discussed in the context of yield predictions.

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.

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.

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.

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.

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 β-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 β-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.

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.

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

ConspectusFor 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.

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)Ni^ICl]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.

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.

Auto-QChem: an automated workflow for the generation and storage of DFT calculations for organic molecules

This perspective describes Auto-QChem, an automatic, high-throughput and end-to-end DFT calculation workflow that computes chemical descriptors for organic molecules.

The Open Reaction Database

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored in various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present the Open Reaction Database (ORD), an open-access schema and infrastructure for structuring and sharing organic reaction data, including a centralized data repository. The ORD schema supports conventional and emerging technologies, from benchtop reactions to automated high-throughput experiments and flow chemistry. The data, schema, supporting code, and web-based user interfaces are all publicly available on GitHub. Our vision is that a consistent data representation and infrastructure to support data sharing will enable downstream applications that will greatly improve the state of the art with respect to computer-aided synthesis planning, reaction prediction, and other predictive chemistry tasks.

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 β-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.

The Evolution of Data-Driven Modeling in Organic Chemistry

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.

Univariate classification of phosphine ligation state and reactivity in cross-coupling catalysis

[Figure: see text].

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.

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.

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 β-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.

Regioselective Cross-Electrophile Coupling of Epoxides and (Hetero)aryl Iodides via Ni/Ti/Photoredox Catalysis

A cross-electrophile coupling reaction of epoxides and (hetero)aryl iodides that operates via the merger of three catalytic cycles involving a Ni-, Ti-, and organic photoredox catalyst has been developed. Three distinct classes of epoxides, styrene oxides, cyclic epoxides, and terminal aliphatic epoxides, all undergo coupling in moderate to good yield and high regioselectivity with the use of three different nitrogen-based ligands for Ni under otherwise identical reaction conditions. The mild reaction conditions accommodate a broad scope of abundant and complex coupling partners. Mechanistic studies suggest that when styrene oxides are employed radical intermediates are involved via Ti-radical ring-opening of the epoxide. Conversely, for terminal aliphatic epoxides, involvement of an iodohydrin intermediate enables the formation of the unexpected linear product.

Role of Electron-Deficient Olefin Ligands in a Ni-Catalyzed Aziridine Cross-Coupling To Generate Quaternary Carbons

We previously reported the development of an electron-deficient olefin (EDO) ligand, Fro-DO, that promotes the generation of quaternary carbon centers via Ni-catalyzed Csp³-Csp³ cross-coupling with aziridines. By contrast, electronically and structurally similar EDO ligands such as dimethyl fumarate and electron-deficient styrenes afford primarily β-hydride elimination side reactivity. Only a few catalyst systems have been identified that promote the formation of quaternary carbons via Ni-catalyzed Csp³-Csp³ cross-coupling. Although Fro-DO represents a promising ligand in this regard, the basis for its superior performance is not well understood. Here we describe a detailed mechanistic study of the aziridine cross-coupling reaction and the role of EDO ligands in facilitating Csp³-Csp³ bond formation. This analysis reveals that cross-coupling proceeds by a Ni^0/II cycle with a Ni^II azametallacyclobutane catalyst resting state. Turnover-limiting C-C reductive elimination occurs from a spectroscopically observable Ni^II-dialkyl intermediate bound to the EDO. Computational analysis shows that Fro-DO accelerates turnover limiting reductive elimination via LUMO lowering. However, it is no more effective than dimethyl fumarate at reducing the barrier to Csp³-Csp³ reductive elimination. Instead, Fro-DO's unique reactivity arises from its ability to associate favorably to Ni^II intermediates. Natural bond order second-order perturbation theory analysis of the catalytically relevant Ni^II intermediate indicates that Fro-DO binds to Ni^II through an additional stabilizing donor-acceptor interaction between its sulfonyl group and Ni^II. Design of new ligands to evaluate this proposal supports this model and has led to the development of a new and tunable ligand framework.

Nickel/Photoredox-Catalyzed Methylation of (Hetero)aryl Chlorides Using Trimethyl Orthoformate as a Methyl Radical Source

Methylation of organohalides represents a valuable transformation, but typically requires harsh reaction conditions or reagents. We report a radical approach for the methylation of (hetero)aryl chlorides using nickel/photoredox catalysis wherein trimethyl orthoformate, a common laboratory solvent, serves as a methyl source. This method permits methylation of (hetero)aryl chlorides and acyl chlorides at an early and late stage with broad functional group compatibility. Mechanistic investigations indicate that trimethyl orthoformate serves as a source of methyl radical via β-scission from a tertiary radical generated upon chlorine-mediated hydrogen atom transfer.

Synthesis of β-Phenethylamines via Ni/Photoredox Cross-Electrophile Coupling of Aliphatic Aziridines and Aryl Iodides

A photoassisted Ni-catalyzed reductive cross-coupling between tosyl-protected alkyl aziridines and commercially available (hetero)aryl iodides is reported. This mild and modular method proceeds in the absence of stoichiometric heterogeneous reductants and uses an inexpensive organic photocatalyst to access medicinally valuable β-phenethylamine derivatives. Unprecedented reactivity was achieved with the activation of cyclic aziridines. Mechanistic studies suggest that the regioselectivity and reactivity observed under these conditions are a result of nucleophilic iodide ring opening of the aziridine to generate an iodoamine as the active electrophile. This strategy also enables cross-coupling with Boc-protected aziridines.

3d-d Excited States of Ni(II) Complexes Relevant to Photoredox Catalysis: Spectroscopic Identification and Mechanistic Implications

Synthetic organic chemistry has seen major advances due to the merger of nickel and photoredox catalysis. A growing number of Ni-photoredox reactions are proposed to involve generation of excited nickel species, sometimes even in the absence of a photoredox catalyst. To gain insights about these excited states, two of our groups previously studied the photophysics of Ni(^t-Bubpy)(o-Tol)Cl, which is representative of proposed intermediates in many Ni-photoredox reactions. This complex was found to have a long-lived excited state (τ = 4 ns), which was computationally assigned as a metal-to-ligand charge transfer (MLCT) state with an energy of 1.6 eV (38 kcal/mol). This work evaluates the computational assignment experimentally using a series of related complexes. Ultrafast UV-Vis and mid-IR transient absorption data suggest that a MLCT state is generated initially upon excitation but decays to a long-lived state that is ³d-d rather than ³MLCT in character. Dynamic cis,trans-isomerization of the square planar complexes was observed in the dark using ¹H NMR techniques, supporting that this ³d-d state is tetrahedral and accessible at ambient temperature. Through a combination of transient absorption and NMR studies, the ³d-d state was determined to lie ∼0.5 eV (12 kcal/mol) above the ground state. Because the ³d-d state features a weak Ni-aryl bond, the excited Ni(II) complexes can undergo Ni homolysis to generate aryl radicals and Ni(I), both of which are supported experimentally. Thus, photoinduced Ni-aryl homolysis offers a novel mechanism of initiating catalysis by Ni(I).

Direct Use of Carboxylic Acids in the Photocatalytic Hydroacylation of Styrenes To Generate Dialkyl Ketones

A general protocol for the hydroacylation of styrenes from aliphatic carboxylic acids is reported. These reactions proceed via β-scission of a phosphoranyl radical that is accessed by photoredox catalysis, followed by addition of the resulting acyl radical to the styrenyl olefin. We show that phosphine tunability is critical for efficient intermolecular coupling due to competitive quenching of the photocatalyst by the olefin. Primary, secondary, and structurally rigid tertiary carboxylic acids all generate valuable unsymmetrical dialkyl ketones.

Generation of Phosphoranyl Radicals via Photoredox Catalysis Enables Voltage-Independent Activation of Strong C-O Bonds

Despite the prevalence of alcohols and carboxylic acids as functional groups in organic molecules and the potential to serve as radical precursors, C-O bonds remain difficult to activate. We report a synthetic strategy for direct access to both alkyl and acyl radicals from these ubiquitous functional groups via photoredox catalysis. This method exploits the unique reactivity of phosphoranyl radicals, generated from a polar/SET crossover between a phosphine radical cation and an oxygen-centered nucleophile. We show the desired reactivity in the reduction of benzylic alcohols to the corresponding benzyl radicals with terminal H atom trapping to afford the deoxygenated products. Using the same method, we demonstrate access to synthetically versatile acyl radicals, which enables the reduction of aromatic and aliphatic carboxylic acids to the corresponding aldehydes with exceptional chemoselectivity. This protocol also transforms carboxylic acids to heterocycles and cyclic ketones via intramolecular acyl radical cyclizations to forge C-O, C-N, and C-C bonds in a single step.

Response to Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"

We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.

Direct C-C Bond Formation from Alkanes Using Ni-Photoredox Catalysis

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.

Long-Lived Charge-Transfer States of Nickel(II) Aryl Halide Complexes Facilitate Bimolecular Photoinduced Electron Transfer

Here we investigate the photophysics and photochemistry of Ni(II) aryl halide complexes common to cross-coupling and Ni/photoredox reactions. Computational and ultrafast spectroscopic studies reveal that these complexes feature long-lived ³MLCT excited states, implicating Ni as an underexplored alternative to precious metal photocatalysts. Moreover, we show that ³MLCT Ni(II) engages in bimolecular electron transfer with ground-state Ni(II), which enables access to Ni(III) in the absence of external oxidants or photoredox catalysts. As such, it is possible to facilitate Ni-catalyzed C-O bond formation solely by visible light irradiation, thus representing an alternative strategy for catalyst activation in Ni cross-coupling reactions.

Ni-Catalyzed Carbon-Carbon Bond-Forming Reductive Amination

This report describes a three-component, Ni-catalyzed reductive coupling that enables the convergent synthesis of tertiary benzhydryl amines, which are challenging to access by traditional reductive amination methodologies. The reaction makes use of iminium ions generated in situ from the condensation of secondary N-trimethylsilyl amines with benzaldehydes, and these species undergo reaction with several distinct classes of organic electrophiles. The synthetic value of this process is demonstrated by a single-step synthesis of antimigraine drug flunarizine (Sibelium) and high yielding derivatization of paroxetine (Paxil) and metoprolol (Lopressor). Mechanistic investigations support a sequential oxidative addition mechanism rather than a pathway proceeding via α-amino radical formation. Accordingly, application of catalytic conditions to an intramolecular reductive coupling is demonstrated for the synthesis of endo- and exocyclic benzhydryl amines.

Mild, Redox-Neutral Formylation of Aryl Chlorides through the Photocatalytic Generation of Chlorine Radicals

We report a redox-neutral formylation of aryl chlorides that proceeds through selective 2-functionalization of 1,3-dioxolane through nickel and photoredox catalysis. This scalable benchtop approach provides a distinct advantage over traditional reductive carbonylation in that no carbon monoxide, pressurized gas, or stoichiometric reductant is employed. The mild conditions give unprecedented scope from abundant and complex aryl chloride starting materials.

Nickel-Catalyzed Enantioselective Reductive Cross-Coupling of Styrenyl Aziridines

A Ni-catalyzed reductive cross-coupling of styrenyl aziridines with aryl iodides is reported. This reaction proceeds by a stereoconvergent mechanism and is thus amenable to asymmetric catalysis using a chiral bioxazoline ligand for Ni. The process allows facile access to highly enantioenriched 2-arylphenethylamines from racemic aziridines. Multivariate analysis revealed that ligand polarizability, among other features, influences the observed enantioselectivity, shedding light on the success of this emerging ligand class for enantioselective Ni catalysis.

Parameterization of phosphine ligands demonstrates enhancement of nickel catalysis via remote steric effects

The field of Ni-catalysed cross-coupling has seen rapid recent growth because of the low cost of Ni, its earth abundance, and its ability to promote unique cross-coupling reactions. Whereas advances in the related field of Pd-catalysed cross-coupling have been driven by ligand design, the development of ligands specifically for Ni has received minimal attention. Here, we disclose a class of phosphines that enable the Ni-catalysed Csp³ Suzuki coupling of acetals with boronic acids to generate benzylic ethers, a reaction that failed with known ligands for Ni and designer phosphines for Pd. Using parameters to quantify phosphine steric and electronic properties together with regression statistical analysis, we identify a model for ligand success. The study suggests that effective phosphines feature remote steric hindrance, a concept that could guide future ligand design tailored to Ni. Our analysis also reveals that two classic descriptors for ligand steric environment-cone angle and % buried volume-are not equivalent, despite their treatment in the literature.