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.

Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation.

Methods to access chiral sulfur(VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization of unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products are formed in excellent yield and enantioselectivity with no observed bis-acylation. A data-science-driven approach to substrate scope evaluation was coupled to high throughput experimentation (HTE) to facilitate statistical modeling in order to inform mechanistic studies. Reaction kinetics, catalyst structural studies, and density functional theory (DFT) transition state analysis elucidated the turnover-limiting step to be the collapse of the tetrahedral intermediate and provided key insights into the catalyst-substrate structure-activity relationships responsible for the origin of the enantioselectivity. This study offers a reliable method for accessing enantioenriched sulfonimidamides to propel their application as pharmacophores and serves as an example of the mechanistic insight that can be gleaned from integrating data science and traditional physical organic techniques.

Electrocatalytic Asymmetric Nozaki-Hiyama-Kishi Decarboxylative Coupling: Scope, Applications, and Mechanism.

The first general enantioselective alkyl-Nozaki-Hiyama-Kishi (NHK) coupling reactions are disclosed herein by employing a Cr-electrocatalytic decarboxylative approach. Using easily accessible aliphatic carboxylic acids (via redox-active esters) as alkyl nucleophile synthons, in combination with aldehydes and enabling additives, chiral secondary alcohols are produced in a good yield with high enantioselectivity under mild reductive electrolysis. This reaction, which cannot be mimicked using stoichiometric metal or organic reductants, tolerates a broad range of functional groups and is successfully applied to dramatically simplify the synthesis of multiple medicinally relevant structures and natural products. Mechanistic studies revealed that this asymmetric alkyl e-NHK reaction was enabled by using catalytic tetrakis(dimethylamino)ethylene, which acts as a key reductive mediator to mediate the electroreduction of the CrIII/chiral ligand complex.

Computational Methods Enable the Prediction of Improved Catalysts for Nickel-Catalyzed Cross-Electrophile Coupling.

Cross-electrophile coupling has emerged as an attractive and efficient method for the synthesis of C(sp2)-C(sp3) bonds. These reactions are most often catalyzed by nickel complexes of nitrogenous ligands, especially 2,2'-bipyridines. Precise prediction, selection, and design of optimal ligands remains challenging, despite significant increases in reaction scope and mechanistic understanding. Molecular parameterization and statistical modeling provide a path to the development of improved bipyridine ligands that will enhance the selectivity of existing reactions and broaden the scope of electrophiles that can be coupled. Herein, we describe the generation of a computational ligand library, correlation of observed reaction outcomes with features of the ligands, and the in silico design of improved bipyridine ligands for Ni-catalyzed cross-electrophile coupling. The new nitrogen-substituted ligands display a 5-fold increase in selectivity for product formation versus homodimerization when compared to the current state of the art. This increase in selectivity and yield was general for several cross-electrophile couplings, including the challenging coupling of an aryl chloride with an N-alkylpyridinium salt.

14N to 15N Isotopic Exchange of Nitrogen Heteroaromatics through Skeletal Editing.

The selective modification of nitrogen heteroaromatics enables the development of new chemical tools and accelerates drug discovery. While methods that focus on expanding or contracting the skeletal structures of heteroaromatics are emerging, methods for the direct exchange of single core atoms remain limited. Here, we present a method for 14N → 15N isotopic exchange for several aromatic nitrogen heterocycles. This nitrogen isotope transmutation occurs through activation of the heteroaromatic substrate by triflylation of a nitrogen atom, followed by a ring-opening/ring-closure sequence mediated by 15N-aspartate to effect the isotopic exchange of the nitrogen atom. Key to the success of this transformation is the formation of an isolable 15N-succinyl intermediate, which undergoes elimination to give the isotopically labeled heterocycle. These transformations occur under mild conditions in high chemical and isotopic yields.

Deconvoluting Nonlinear Catalyst-Substrate Effects in the Intramolecular Dirhodium-Catalyzed C-H Insertion of Donor/Donor Carbenes Using Data Science Tools.

Interactions between catalysts and substrates can be highly complex and dynamic, often complicating the development of models to either predict or understand such processes. A dirhodium(II)-catalyzed C-H insertion of donor/donor carbenes into 2-alkoxybenzophenone substrates to form benzodihydrofurans was selected as a model system to explore nonlinear methods to achieve a mechanistic understanding. We found that the application of traditional methods of multivariate linear regression (MLR) correlating DFT-derived descriptors of catalysts and substrates leads to poorly performing models. This inspired the introduction of nonlinear descriptor relationships into modeling by applying the sure independence screening and sparsifying operator (SISSO) algorithm. Based on SISSO-generated descriptors, a high-performing MLR model was identified that predicts external validation points well. Mechanistic interpretation was aided by the deconstruction of feature relationships using chemical space maps, decision trees, and linear descriptors. Substrates were found to have a strong dependence on steric effects for determining their innate cyclization selectivity preferences. Catalyst reactive site features can then be matched to product features to tune or override the resultant diastereoselectivity within the substrate-dictated ranges. This case study presents a method for understanding complex interactions often encountered in catalysis by using nonlinear modeling methods and linear deconvolution by pattern recognition.

Predicting success in Cu-catalyzed C-N coupling reactions using data science.

Data science is assuming a pivotal role in guiding reaction optimization and streamlining experimental workloads in the evolving landscape of synthetic chemistry. A discipline-wide goal is the development of workflows that integrate computational chemistry and data science tools with high-throughput experimentation as it provides experimentalists the ability to maximize success in expensive synthetic campaigns. Here, we report an end-to-end data-driven process to effectively predict how structural features of coupling partners and ligands affect Cu-catalyzed C-N coupling reactions. The established workflow underscores the limitations posed by substrates and ligands while also providing a systematic ligand prediction tool that uses probability to assess when a ligand will be successful. This platform is strategically designed to confront the intrinsic unpredictability frequently encountered in synthetic reaction deployment.

Data science enabled discovery of a highly soluble 2,2'-bipyrimidine anolyte for application in a flow battery.

Development of non-aqueous redox flow batteries as a viable energy storage solution relies upon the identification of soluble charge carriers capable of storing large amounts of energy over extended time periods. A combination of metrics including number of electrons stored per molecule, redox potential, stability, and solubility of the charge carrier impact performance. In this context, we recently reported a 2,2'-bipyrimidine charge carrier that stores two electrons per molecule with reduction near -2.0 V vs. Fc/Fc+ and high stability. However, these first-generation derivatives showed a modest solubility of 0.17 M (0.34 M e-). Seeking to improve solubility without sacrificing stability, we harnessed the synthetic modularity of this scaffold to design a library of sixteen candidates. Using computed molecular descriptors and a single node decision tree, we found that minimization of the solvent accessible surface area (SASA) can be used to predict derivatives with enhanced solubility. This parameter was used in combination with a heatmap describing stability to de-risk a virtual screen that ultimately identified a 2,2'-bipyrimidine with significantly increased solubility and good stability metrics in the reduced states. This molecule was paired with a cyclopropenium catholyte in a prototype all-organic redox flow battery, achieving a cell potential up to 3 V.

An Asymmetric Aromatic Finkelstein Reaction: A Platform for Remote Diarylmethane Desymmetrization.

A first-of-its-kind enantioselective aromatic Finkelstein reaction is disclosed for the remote desymmetrization of diarylmethanes. The reaction operates through a copper-catalyzed C-I bond-forming event, and high levels of enantioselectivity are achieved through the deployment of a tailored guanidinylated peptide ligand. Strategic use of transition-metal-mediated reactions enables the chemoselective modification of the aryl iodide products; thus, the synthesis of a diverse set of otherwise difficult-to-access diarylmethanes with excellent levels of selectivity is realized from a common intermediate. A mixed experimental/computational analysis of steric parameters and substrate conformations identifies the importance of remote conformational effects as a key to achieving high enantioselectivity in this desymmetrization reaction.

Data Science-Enabled Palladium-Catalyzed Enantioselective Aryl-Carbonylation of Sulfonimidamides.

New methods for the general asymmetric synthesis of sulfonimidamides are of great interest due to their applications in medicinal chemistry, agrochemical discovery, and academic research. We report a palladium-catalyzed cross-coupling method for the enantioselective aryl-carbonylation of sulfonimidamides. Using data science techniques, a virtual library of calculated bisphosphine ligand descriptors was used to guide reaction optimization by effectively sampling the catalyst chemical space. The optimized conditions identified using this approach provided the desired product in excellent yield and enantioselectivity. As the next step, a data science-driven strategy was also used to explore a diverse set of aryl and heteroaryl iodides, providing key information about the scope and limitations of the method. Furthermore, we tested a range of racemic sulfonimidamides for compatibility of this coupling partner. The developed method offers a general and efficient strategy for accessing enantioenriched sulfonimidamides, which should facilitate their application in industrial and academic settings.

Using Data Science for Mechanistic Insights and Selectivity Predictions in a Non-Natural Biocatalytic Reaction.

The study of non-natural biocatalytic transformations relies heavily on empirical methods, such as directed evolution, for identifying improved variants. Although exceptionally effective, this approach provides limited insight into the molecular mechanisms behind the transformations and necessitates multiple protein engineering campaigns for new reactants. To address this limitation, we disclose a strategy to explore the biocatalytic reaction space and garner insight into the molecular mechanisms driving enzymatic transformations. Specifically, we explored the selectivity of an "ene"-reductase, GluER-T36A, to create a data-driven toolset that explores reaction space and rationalizes the observed and predicted selectivities of substrate/mutant combinations. The resultant statistical models related structural features of the enzyme and substrate to selectivity and were used to effectively predict selectivity in reactions with out-of-sample substrates and mutants. Our approach provided a deeper understanding of enantioinduction by GluER-T36A and holds the potential to enhance the virtual screening of enzyme mutants.

Data Science Enables the Development of a New Class of Chiral Phosphoric Acid Catalysts.

The widespread success of BINOL-chiral phosphoric acids (CPAs) has led to the development of several high molecular weight, sterically encumbered variants. Herein, we disclose an alternative, minimalistic chiral phosphoric acid backbone incorporating only a single instance of point chirality. Data science techniques were used to select a diverse training set of catalysts, which were benchmarked against the transfer hydrogenation of an 8-aminoquinoline. Using a univariate classification algorithm and multivariate linear regression, key catalyst features necessary for high levels of selectivity were deconvoluted, revealing a simple catalyst model capable of predicting selectivity for out-of-set catalysts. This workflow enabled extrapolation to a catalyst providing higher selectivity than both reported peptide-type and BINOL-type catalysts (up to 95:5 er). These techniques were then successfully applied towards two additional transforms. Taken together, these examples illustrate the power of combining rational design with data science (ab initio) to efficiently explore reactivity during catalyst development.

Catalyst-Controlled Enantioselective and Regiodivergent Addition of Aryl Boron Nucleophiles to N-Alkyl Nicotinate Salts.

Dihydropyridines are versatile building blocks for the synthesis of pyridines, tetrahydropyridines, and piperidines. Addition of nucleophiles to activated pyridinium salts allows synthesis of 1,2-, 1,4-, or 1,6-dihydropyridines; however, this process often leads to a mixture of constitutional isomers. Catalyst-controlled regioselective addition of nucleophiles to pyridiniums has the potential to solve this problem. Herein, we report that the regioselective addition of boron-based nucleophiles to pyridinium salts can be accomplished by the choice of a Rh catalyst.

Generality-oriented optimization of enantioselective aminoxyl radical catalysis.

Catalytic enantioselective methods that are generally applicable to a broad range of substrates are rare. We report a strategy for the oxidative desymmetrization of meso-diols predicated on a nontraditional catalyst optimization protocol by using a panel of screening substrates rather than a singular model substrate. Critical to this approach was rational modulation of a peptide sequence in the catalyst incorporating a distinct aminoxyl-based active residue. A general catalyst emerged, providing high selectivity in the delivery of enantioenriched lactones across a broad range of diols, while also achieving up to ~100,000 turnovers.

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 Physical Organic Approach towards Statistical Modeling of Tetrazole and Azide Decomposition.

Nitrogen atom-rich heterocycles and organic azides have found extensive use in many sectors of modern chemistry from drug discovery to energetic materials. The prediction and understanding of their energetic properties are thus key to the safe and effective application of these compounds. In this work, we disclose the use of multivariate linear regression modeling for the prediction of the decomposition temperature and impact sensitivity of structurally diverse tetrazoles and organic azides. We report a data-driven approach for property prediction featuring a collection of quantum mechanical parameters and computational workflows. The statistical models reported herein carry predictive accuracy as well as chemical interpretability. Model validation was successfully accomplished via tetrazole test sets with parameters generated exclusively in silico. Mechanistic analysis of the statistical models indicated distinct divergent pathways of thermal and impact-initiated decomposition.

Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands.

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient. To accomplish this, a density functional theory-derived database of >550 bisphosphine ligands was constructed, and a designer chemical space mapping technique was established. The protocol used classification methods to identify active catalysts, followed by linear regression to model reaction selectivity. This led to the prediction and validation of significantly improved ligands for all reaction outputs, suggesting a general strategy that can be readily implemented for reaction optimizations where performance is controlled by bisphosphine ligands.

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.

Identifying structure-function relationships to modulate crossover in nonaqueous redox flow batteries.

Nonaqueous redox flow batteries (NARFBs) offer a promising solution for large-scale storage of renewable energy. However, crossover of redox active molecules between the two sides of the cell is a major factor limiting their development, as most selective separators are designed for deployment in water, rather than organic solvents. This report describes a systematic investigation of the crossover rates of redox active organic molecules through an anion exchange separator under RFB-relevant non-aqueous conditions (in acetonitrile/KPF6) using a combination of experimental and computational methods. A structurally diverse set of neutral and cationic molecules was selected, and their rates of crossover were determined experimentally with the organic solvent-compatible anion exchange separator Fumasep FAP-375-PP. The resulting data were then fit to various descriptors of molecular size, charge, and hydrophobicity (overall charge, solution diffusion coefficient, globularity, dynamic volume, dynamic surface area, clogP). This analysis resulted in multiple statistical models of crossover rates for this separator. These models were then used to predict tether groups that dramatically slow the crossover of small organic molecules in this system.

Catalytic asymmetric C-H insertion reactions of vinyl carbocations.

From the preparation of pharmaceuticals to enzymatic construction of natural products, carbocations are central to molecular synthesis. Although these reactive intermediates are engaged in stereoselective processes in nature, exerting enantiocontrol over carbocations with synthetic catalysts remains challenging. Many resonance-stabilized tricoordinated carbocations, such as iminium and oxocarbenium ions, have been applied in catalytic enantioselective reactions. However, their dicoordinated counterparts (aryl and vinyl carbocations) have not, despite their emerging utility in chemical synthesis. We report the discovery of a highly enantioselective vinyl carbocation carbon-hydrogen (C-H) insertion reaction enabled by imidodiphosphorimidate organocatalysts. Active site confinement featured in this catalyst class not only enables effective enantiocontrol but also expands the scope of vinyl cation C-H insertion chemistry, which broadens the utility of this transition metal-free C(sp3)-H functionalization platform.