Stable fluorophosphines: predicted and realized ligands for catalysis

Monofluorophos-Metal Complexes: Ripe for Future Discoveries in Homogeneous Catalysis

The discovery that cyclic (ArO)2PF can support Rh-catalysts for hydroformylation with significant advantages in tuning regioselectivity transformed the study of metal complexes of monofluorophos ligands from one of primarily academic interest to one with potentially important applications in catalysis. In this review, the syntheses of monofluorophosphites, (RO)2PF, and monofluorophosphines, R2PF, are discussed and the factors that control the kinetic stability of these ligands to hydrolysis and disproportionation are set out. A survey of the coordination chemistry of these two classes of monofluorophos ligands with d-block metals is presented, emphasising the bonding of the fluorophos to d-block metals, predominantly in low oxidation states. The application of monofluorophos ligands in homogeneous catalysis (especially hydroformylation and hydrocyanation) is discussed, and it is argued that there is great potential for monofluorophos complexes in future catalytic applications.

Activation of fluoride anion as nucleophile in water with data-guided surfactant selection

A principal component surfactant_map was developed for 91 commonly accessible surfactants for use in surfactant-enabled organic reactions in water, an important approach for sustainable chemical processes. This map was built using 22 experimental and theoretical descriptors relevant to the physicochemical nature of these surfactant-enabled reactions, and advanced principal component analysis algorithms. It is comprised of all classes of surfactants, i.e. cationic, anionic, zwitterionic and neutral surfactants, including designer surfactants. The value of this surfactant_map was demonstrated in activating simple inorganic fluoride salts as effective nucleophiles in water, with the right surfactant. This led to the rapid development (screening 13-15 surfactants) of two fluorination reactions for β-bromosulfides and sulfonyl chlorides in water. The latter was demonstrated in generating a sulfonyl fluoride with sufficient purity for direct use in labelling of chymotrypsin, under physiological conditions.

BF3-Promoted Ring Expansion of Iminylphosphiranes and Acylphosphiranes for Divergent Access to 1,2-Azaphospholidines and 1,2-Dihydrophosphetes

Ring expansion of strained small rings provides an efficient method for the synthesis of various high-value carbocycles and heterocycles. Here we report BF3·Et2O as both an activating reagent and fluorine source, enabling ring expansion of phosphirane and P-F bond formation. Treatment of 1-iminylphosphirane complexes with BF3·Et2O resulted in 1,2-azaphospholidines, while the reaction of 1-acylphosphirane complexes with BF3·Et2O afforded 1,2-dihydrophosphetes. The reaction path was tuned by the nucleophilicity of the N and O atoms toward the intermediate phosphenium cation.

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.

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Computers have become closely involved with most aspects of modern life, and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers, and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry-it is now entirely possible to complete research projects where computation and synthesis are cooperative and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimization, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that are affected by the combination of input variables. Mechanistic deviation is most likely when challenging multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalyst libraries until a new "best" match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward a "big data" view of organometallic homogeneous catalysis with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarize here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists so that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.

Iterative Supervised Principal Component Analysis Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

The rational design of catalysts remains a challenging endeavor within the broader chemical community owing to the myriad variables that can affect key bond-forming events. Designing selective catalysts for any reaction requires an efficient strategy for discovering predictive structure-activity relationships. Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H-pyrrole (C) via a Ti-catalyzed formal [2 + 2 +1] cycloaddition of phenylpropyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space and k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (>90% C) by incorporating 2,6-dimethyl-4-(pyrrolidin-1-yl)pyridine as a ligand. The origin of catalyst selectivity was probed by examining ISPCA variable loadings in combination with DFT modeling, revealing that ligand lability plays an important role in selectivity. A parallel catalyst search using multivariate linear regression (MLR), a popular approach in catalysis informatics, was also conducted in order to compare these strategies in a hypothetical catalyst scouting campaign. ISPCA appears to be more robust and predictive than MLR when sparse training sets are used that are representative of the data available during the early search for an optimal catalyst. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.

Mapping the properties of bidentate ligands with calculated descriptors (LKB-bid)

Ligand space for bidentates has been mapped, computationally, varying donors, substituents and backbones, to give a new database, LKB-bid.

Transient Phosphenium and Arsenium Ions versus Stable Stibenium and Bismuthenium Ions

Fluoride abstraction from bis-m-terphenylelement fluorides (2,6-Mes2 C6 H3 )2 EF (E=P, As) generated the highly reactive phosphenium ion [(2,6-Mes2 C6 H3 )2 P]+ and the arsenium ion [(2,6-Mes2 C6 H3 )2 As]+ , which immediately underwent intramolecular electrophilic substitution and formation of an 1,2,4-trimethyl-6-mesityl-5-m-terphenyl-benzo[b]phospholium ion and an 1,2,4-trimethyl-6-mesityl-5-m-terphenyl-benzo[b]arsolium ion, respectively. The formation of the latter involved a methyl group migration from the ortho-position of a flanking mesityl group to the meta-position. This reactivity of [(2,6-Mes2 C6 H3 )2 E]+ (E=P, As) is in sharp contrast to the related stibenium ion [(2,6-Mes2 C6 H3 )2 Sb]+ and bismuthenium ion [(2,6-Mes2 C6 H3 )2 Bi]+ , which have been recently isolated and fully characterized (Angew. Chem. Int. Ed. 2018, 57, 10080-10084). On the basis of DFT calculations, a mechanism for the rearrangement of the phosphenium and arsenium ions into the phospholium and arsolium ions is proposed, which is not feasible for the stibenium and bismuthenium ions.

Rapid one-step 18F-radiolabeling of biomolecules in aqueous media by organophosphine fluoride acceptors

Currently, only a few ¹⁸F-radiolabeling methods were conducted in aqueous media, with non-macroelement fluoride acceptors and stringent conditions required. Herein, we describe a one-step non-solvent-biased, room-temperature-driven ¹⁸F-radiolabeling methodology based on organophosphine fluoride acceptors. The high water tolerance for this isotope-exchange-based ¹⁸F-labeling method is attributed to the kinetic and thermodynamic preference of F/F over the OH/F substitution based on computational calculations and experimental validation. Compact [^18/19F]di-tert-butyl-organofluorophosphine and its derivatives used as ¹⁸F-labeling synthons exhibit excellent stability in vivo. The synthons are further conjugated to several biomolecular ligands such as c(RGDyk) and human serum albumin. The one-step labeled biomolecular tracers demonstrate intrinsic target imaging ability and negligible defluorination in vivo. The current method thus offers a facile and efficient ¹⁸F-radiolabeling pathway, enabling further widespread application of ¹⁸F.

The synthesis of ¹⁸F-labeled positron emission tomography (PET) tracers is difficult and typically requires anhydrous conditions. Here, the authors developed organophosphine precursors that allow for quick, high-yield synthesis of ¹⁸F-labeled probes in either organic solvents or aqueous media.

Computational Ligand Descriptors for Catalyst Design

Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimization, and design of catalysts. In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,P-donor ligands, and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.

Carbeniophosphines versus Phosphoniocarbenes: The Role of the Positive Charge

The chemistry of carbeniophosphines and phosphoniocarbenes, which have general structures derived formally from the three-component "carbene/phosphine/positive charge" association, is presented. These two complementary classes of carbon-phosphorus-based ligands, defined by the presence of an inverted cationic coordinating structure (C+ ∼P: vs. P+ ∼C:) have the common purpose of positioning a positive charge in the vicinity of the metal center. Through selected examples, the synthetic methods, coordination properties, and general reactivity of both cationic species is described. Particular emphasis is placed on the influence of the positive charge on the respective chemical behavior of the two classes of compound.

Designing bifunctional alkene isomerization catalysts using predictive modelling

Optimised isomerisation catalysts are found using an iterative approach combining experimental studies and descriptor modelling.

Aminophobanes: hydrolytic stability, tautomerism and application in Cr-catalysed ethene oligomerisation

9-Amino-9-phosphabicyclo[3.3.1]nonanes, (PhobPNHR'; R = Me or (i)Pr) are readily prepared by aminolysis of PhobPCl and are significantly less susceptible to hydrolysis than the acyclic analogues Cy2PNHR'. Treatment of Cy2PNHMe with Cy2PCl readily gave Cy2PNMePCy2. By contrast, treatment of PhobPCl with PhobPNHMe in the presence of Et3N does not afford PhobPNMePPhob but instead the salt [PhobP(= NMeH)PPhob]Cl is formed which, upon addition of [PtCl2(NC(t)Bu)2] gives the zwitterionic complex [PtCl3(PhobP(= NMeH)PPhob)]. The neutral PhobP(= NMe)PPhob is accessible from PhobNMeLi and is converted to the chelate [PdCl2(PhobPNMePPhob)] by addition of [PdCl2(cod)]. The anomalous preference of the PhobP group for the formation of PPN products is discussed. The unsymmetrical diphos ligands PhobPNMePAr2 (Ar = Ph, o-Tol) are prepared, converted to [Cr(CO)4(PhobPNMePAr2)] and shown to form Cr-catalysts for ethene oligomerisation, producing a pattern of higher alkenes that corresponds to a Schulz-Flory distribution overlaid on selective tri/tetramerisation.

A straightforward access to ruthenium-coordinated fluorophosphines from phosphorous oxyacids

For the first time, it has been shown the deoxofluorination of metal-coordinated phosphorous oxyacids is feasible using a safe and environmentally friendly “F” source and affords fluorophosphines in a mild and selective way.

Lost in chemical space? Maps to support organometallic catalysis

Descriptors calculated from molecular structures have been used to map different areas of chemical space. A number of applications for such maps can be identified, ranging from the fine-tuning and optimisation of catalytic activity and compound properties to virtual screening of novel compounds, as well as the exhaustive exploration of large areas of chemical space by automated combinatorial building and evaluation. This review focuses on organometallic catalysis, but also touches on other areas where similar approaches have been used, with a view to assessing the extent to which chemical space has been explored.

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Bond and small-molecule activation with low-valent nickel complexes

The use of nickel compounds in low oxidation states allowed a variety of useful transformations of interest for academia, industry and in the solution of environmental issues.

A simple route to azaborinylphosphines: isoelectronic B–N analogues of arylphosphine ligands

Azaborinylphosphines are readily prepared by the reaction of silylphosphines with a chloroborane under mild conditions; they are shown to contain P–B bonds that are sufficiently robust to allow these ligands to be used in homogeneous catalysis.

Expansion of the Ligand Knowledge Base for Chelating P,P-Donor Ligands (LKB-PP)

We have expanded the ligand knowledge base for bidentate P,P- and P,N-donor ligands (LKB-PP, Organometallics2008, 31, 1372-1383) by 208 ligands and introduced an additional steric descriptor (nHe8). This expanded knowledge base now captures information on 334 bidentate ligands and has been processed with principal component analysis (PCA) of the descriptors to produce a detailed map of bidentate ligand space, which better captures ligand variation and has been used for the analysis of ligand properties.