From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials.

Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

Catalytic enantioselective reductive Eschenmoser-Claisen rearrangements.

An important challenge in enantioselective catalysis is developing strategies for the precise synthesis of neighboring congested all-carbon quaternary stereocenters. The well-defined transition states of [3,3]-sigmatropic rearrangements and their underlying stereospecificity render them powerful tools for the synthesis of such arrays. However, this type of pericyclic reaction remains notoriously difficult to catalyze, especially in an enantioselective fashion. Herein, we describe an enantioselective reductive Eschenmoser-Claisen rearrangement catalyzed by chiral 1,3,2-diazaphospholene-hydrides. This developed transformation enables full control of the two newly formed acyclic stereogenic centers, leading to amides with vicinal all-carbon quaternary-tertiary or quaternary-quaternary carbon atoms.

Toward in silico Catalyst Optimization.

In this minireview, we overview a computational pipeline developed within the framework of NCCR Catalysis that can be used to successfully reproduce the enantiomeric ratios of homogeneous catalytic reactions. At the core of this pipeline is the SCINE Molassembler module, a graph-based software that provides algorithms for molecular construction of all periodic table elements. With this pipeline, we are able to simultaneously functionalizenand generate ensembles of transition state conformers, which permits facile exploration of the influencenof various substituents on the overall enantiomeric ratio. This allows preconceived back-of-the-envelope designnmodels to be tested and subsequently refined by providing quick and reliable access to energetically low-lyingntransition states, which represents a key step in undertaking in silico catalyst optimization.

Nickel-catalyzed direct stereoselective α-allylation of ketones with non-conjugated dienes.

The development of efficient and sustainable methods for the construction of carbon-carbon bonds with the simultaneous stereoselective generation of vicinal stereogenic centers is a longstanding goal in organic chemistry. Low-valent nickel(0) complexes which promote α-functionalization of carbonyls leveraging its pro-nucleophilic character in conjunction with suitable olefin acceptors are scarce. We report a Ni(0)NHC catalyst which selectively converts ketones and non-conjugated dienes to synthetically highly valuable α-allylated products. The catalyst directly activates the α-hydrogen atom of the carbonyl substrate transferring it to the olefin acceptor. The transformation creates adjacent quaternary and tertiary stereogenic centers in a highly diastereoselective and enantioselective manner. Computational studies indicate the ability of the Ni(0)NHC catalyst to trigger a ligand-to-ligand hydrogen transfer process from the ketone α-hydrogen atom to the olefin substrate, setting the selectivity of the process. The shown selective functionalization of the α-C-H bond of carbonyl groups by the Ni(0)NHC catalyst opens up new opportunities to exploit sustainable 3d-metal catalysis for a stereoselective access to valuable chiral building blocks.

Photocatalyzed [2σ + 2σ] and [2σ + 2π] Cycloadditions for the Synthesis of Bicyclo[3.1.1]heptanes and 5- or 6-Membered Carbocycles.

We report the use of photocatalysis for the homolytic ring-opening of carbonyl cyclopropanes. In contrast to previous studies, our approach does not require a metal cocatalyst or a strong reductant. The carbonyl cyclopropanes can be employed for both [2σ + 2σ] and [2σ + 2π] annulation with either alkenes/alkynes or bicyclo[1.1.0]butanes, yielding cyclopent-anes/-enes and bicyclo[3.1.1]heptanes (BCHs), respectively. BCHs are promising bioisosteres for 1,2,4,5 tetra-substituted aromatic rings. Mechanistic studies, including density functional theory computation and a trapping experiment with DMPO, support a 1,3-biradical generated from cyclopropane as a key intermediate for these transformations.

Donor-Acceptor Aminocyclobutane Monoesters: Synthesis and Silylium-Catalyzed (4+2) Annulation with Indoles.

A convenient one-step synthesis of ß-aminocyclobutane monoesters starting from commercially available reagents is reported. The obtained strained rings undergo (4+2) dearomative annulation with indole partners using silylium catalysis. This organocatalyzed annulation provided tricyclic indolines with four new stereocenters in up to quantitative yield and >95 : 5 diastereoselectivity and can proceed both intra- and intermolecularly. When performed intramolecularly, the tetracyclic structure of either akuamma or malagasy alkaloids was obtained selectively depending on the temperature of the reaction. This divergent outcome could be rationalized based on DFT calculations.

Theory-guided development of homogeneous catalysts for the reduction of CO2 to formate, formaldehyde, and methanol derivatives.

The stepwise catalytic reduction of carbon dioxide (CO2) to formic acid, formaldehyde, and methanol opens non-fossil pathways to important platform chemicals. The present article aims at identifying molecular control parameters to steer the selectivity to the three distinct reduction levels using organometallic catalysts of earth-abundant first-row metals. A linear scaling relationship was developed to map the intrinsic reactivity of 3d transition metal pincer complexes to their activity and selectivity in CO2 hydrosilylation. The hydride affinity of the catalysts was used as a descriptor to predict activity/selectivity trends in a composite volcano picture, and the outstanding properties of cobalt complexes bearing bis(phosphino)triazine PNP-type pincer ligands to reach the three reduction levels selectively under different reaction conditions could thus be rationalized. The implications of the composite volcano picture were successfully experimentally validated with selected catalysts, and the challenging intermediate level of formaldehyde could be accessed in over 80% yield with the cobalt complex 6. The results underpin the potential of tandem computational-experimental approaches to propel catalyst design for CO2-based chemical transformations.

Substrate-controlled C-H or C-C alkynylation of cyclopropanes: generation of aryl radical cations by direct light activation of hypervalent iodine reagents.

We report the first oxidative C-H alkynylation of arylcyclopropanes. Irradiation of ethynylbenziodoxolone (EBX) reagents with visible light at 440 nm promoted the reaction. By the choice of the aryl group on the cyclopropane, it was possible to completely switch the outcome of the reaction from the alkynylation of the C-H bond to the oxyalkynylation of the C-C bond, which proceeded without the need for a catalyst, in contrast to previous works. The oxyalkynylation could also be extended to aminocyclopropanes as well as styrenes. Computations indicated that the C-H activation became a favoured nearly barrierless process in the presence of two ortho methyl groups on the benzene ring.

Constructing and interpreting volcano plots and activity maps to navigate homogeneous catalyst landscapes.

Volcano plots and activity maps are powerful tools for studying homogeneous catalysis. Once constructed, they can be used to estimate and predict the performance of a catalyst from one or more descriptor variables. The relevance and utility of these tools has been demonstrated in several areas of catalysis, with recent applications to homogeneous catalysts having been pioneered by our research group. Both volcano plots and activity maps are built from linear free energy scaling relationships that connect the value of a descriptor variable(s) with the relative energies of other catalytic cycle intermediates/transition states. These relationships must be both constructed and postprocessed appropriately to obtain the resulting plots/maps; this process requires careful execution to obtain meaningful results. In this protocol, we provide a step-by-step guide to building volcano plots and activity maps using curated reaction profile data. The reaction profile data are obtained using density functional theory computations to model the catalytic cycle. In addition, we provide volcanic, a Python code that automates the steps of the process following data acquisition. Unlike the computation of individual reaction energy profiles, our tools lead to a holistic view of homogeneous catalyst performance that can be broadly applied for both explanatory and screening purposes.

The (not so) simple prediction of enantioselectivity - a pipeline for high-fidelity computations.

The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful means to refine catalyst design strategies. Accurately establishing the selectivity of reactions facilitated by molecular catalysts, however, remains a challenging task for computational chemistry. The small free energy differences that lead to large variations in the enantiomeric ratio (er) represent particularly tricky quantities to predict with sufficient accuracy to be helpful for prioritizing experiments. Further complicating this problem is the fact that standard approaches typically consider only one or a handful of conformers identified through human intuition as pars pro toto of the conformational space. Obviously, this assumption can potentially lead to dramatic failures should key energetic low-lying structures be missed. Here, we introduce a multi-level computational pipeline leveraging the graph-based Molassembler library to construct an ensemble of molecular catalysts. The manipulation and interpretation of molecules as graphs provides a powerful and direct route to tailored functionalization and conformer generation that facilitates high-throughput mechanistic investigations of chemical reactions. The capabilities of this approach are validated by examining a Rh(iii) catalyzed asymmetric C-H activation reaction and assessing the limitations associated with the underlying ligand design model. Specifically, the presence of remarkably flexible chiral Cp ligands, which induce the experimentally observed high level of selectivity, present a rich configurational landscape where multiple unexpected conformations contribute to the reported enantiomeric ratios (er). Using Molassembler, we show that considering about 20 transition state conformations per catalysts, which are generated with little human intervention and are not tied to "back-of-the-envelope" models, accurately reproduces experimental er values with limited computational expense.

Mapping Catalyst-Solvent Interplay in Competing Carboamination/Cyclopropanation Reactions.

Group 9 metals, in particular RhIII complexes with cyclopentadienyl ligands, are competent C-H activation catalysts. Recently, a Cp*RhIII -catalyzed reaction of alkenes with N-enoxyphthalimides showed divergent outcome based on the solvent, with carboamination favored in methanol and cyclopropanation in 2,2,2-trifluoroethanol (TFE). Here, we create selectivity and activity maps capable of unravelling the catalyst-solvent interplay on the outcome of these competing reactions by analyzing 42 cyclopentadienyl metal catalysts, CpX MIII (M=Co, Rh, Ir). These maps not only can be used to rationalize previously reported experimental results, but also capably predict the behavior of untested catalyst/solvent combinations as well as aid in identifying experimental protocols that simultaneously optimize both catalytic activity and selectivity (solutions in the Pareto front). In this regard, we demonstrate how and why the experimentally employed Cp*RhIII catalyst represents an ideal choice to invoke a solvent-induced change in reactivity. Additionally, the maps reveal the degree to which even perceived minor changes in the solvent (e. g., replacing methanol with ethanol) influence the ratio of carboamination and cyclopropanation products. Overall, the selectivity and activity maps presented here provide a generalizable tool to create global pictures of anticipated reaction outcome that can be used to develop new experimental protocols spanning metal, ligand, and solvent space.

Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts.

Hundreds of catalytic methods are developed each year to meet the demand for high-purity chiral compounds. The computational design of enantioselective organocatalysts remains a significant challenge, as catalysts are typically discovered through experimental screening. Recent advances in combining quantum chemical computations and machine learning (ML) hold great potential to propel the next leap forward in asymmetric catalysis. Within the context of quantum chemical machine learning (QML, or atomistic ML), the ML representations used to encode the three-dimensional structure of molecules and evaluate their similarity cannot easily capture the subtle energy differences that govern enantioselectivity. Here, we present a general strategy for improving molecular representations within an atomistic machine learning model to predict the DFT-computed enantiomeric excess of asymmetric propargylation organocatalysts solely from the structure of catalytic cycle intermediates. Mean absolute errors as low as 0.25 kcal mol-1 were achieved in predictions of the activation energy with respect to DFT computations. By virtue of its design, this strategy is generalisable to other ML models, to experimental data and to any catalytic asymmetric reaction, enabling the rapid screening of structurally diverse organocatalysts from available structural information.

Structure and Reactivity of N-Heterocyclic Alkynyl Hypervalent Iodine Reagents.

Ethynylbenziodoxol(on)e (EBX) cyclic hypervalent iodine reagents have become popular reagents for the alkynylation of radicals and nucleophiles, but only offer limited possibilities for further structure and reactivity fine-tuning. Herein, the synthesis of new N-heterocyclic hypervalent iodine reagents with increased structural flexibility based on amide, amidine and sulfoximine scaffolds is reported. Solid-state structures of the reagents are reported and the analysis of the I-Calkyne bond lengths allowed assessing the trans-effect of the different substituents. Molecular electrostatic potential (MEP) maps of the reagents, derived from DFT computations, revealed less pronounced σ-hole regions for sulfonamide-based compounds. Most reagents reacted well in the alkynylation of ß-ketoesters. The alkynylation of thiols afforded more variable yields, with compounds with a stronger σ-hole reacting better. In metal-mediated transformations, the N-heterocyclic hypervalent iodine reagents gave inferior results when compared to the O-based EBX reagents.

Diversifying Metal-Ligand Cooperative Catalysis in Semi-Synthetic [Mn]-Hydrogenases.

The reconstitution of [Mn]-hydrogenases using a series of MnI complexes is described. These complexes are designed to have an internal base or pro-base that may participate in metal-ligand cooperative catalysis or have no internal base or pro-base. Only MnI complexes with an internal base or pro-base are active for H2 activation; only [Mn]-hydrogenases incorporating such complexes are active for hydrogenase reactions. These results confirm the essential role of metal-ligand cooperation for H2 activation by the MnI complexes alone and by [Mn]-hydrogenases. Owing to the nature and position of the internal base or pro-base, the mode of metal-ligand cooperation in two active [Mn]-hydrogenases is different from that of the native [Fe]-hydrogenase. One [Mn]-hydrogenase has the highest specific activity of semi-synthetic [Mn]- and [Fe]-hydrogenases. This work demonstrates reconstitution of active artificial hydrogenases using synthetic complexes differing greatly from the native active site.

The Genesis of Molecular Volcano Plots.

ConspectusFor the past two decades, linear free energy scaling relationships and volcano plots have seen frequent use as computational tools that aid in understanding and predicting the catalytic behavior of heterogeneous and electrocatalysts. Based on Sabatier's principle, which states that a catalyst should bind a substrate neither too strongly nor too weakly, volcano plots provide an estimate of catalytic performance (e.g., overpotential, catalytic cycle thermodynamics/kinetics, etc.) through knowledge of a descriptor variable. By the use of linear free energy scaling relationships, the value of this descriptor is employed to estimate the relative energies of other catalytic cycle intermediates/transition states. Postprocessing of these relationships leads to a volcano curve that reveals the anticipated performance of each catalyst, with the best species appearing on or near the peak or plateau. While the origin of volcanoes is undoubtedly rooted in examining heterogeneously catalyzed reactions, only recently has this concept been transferred to the realm of homogeneous catalysis. This Account summarizes the work done by our group in implementing and refining "molecular volcano plots" for use in analyzing and predicting the behavior of homogeneous catalysts.We begin by taking the reader through the initial proof-of-principle study that transferred the model from heterogeneous to homogeneous catalysis by examining thermodynamic aspects of a Suzuki-Miyaura cross-coupling reaction. By establishing linear free energy scaling relationships and reproducing the volcano shape, we definitively showed that volcano plots are also valid for homogeneous systems. On the basis of this key finding, we further illustrate how unified pictures of C-C cross-coupling thermodynamics were created using three-dimensional molecular volcanoes.The second section highlights an important transformation from "thermodynamic" to "kinetic" volcanoes by using the descriptor variable to directly estimate transition state barriers. Taking this idea further, we demonstrate how volcanoes can be used to directly predict an experimental observable, the turnover frequency. Discussion is also provided on how different flavors of molecular volcanoes can be used to analyze aspects of homogeneous catalysis of interest to experimentalists, such as determining the product selectivity and probing the substrate scope.The third section focuses on incorporating machine learning approaches into molecular volcanoes and invoking big-data-type approaches in the analysis of catalytic behavior. Specifically, we illustrate how machine learning can be used to predict the value of the descriptor variable, which facilitates nearly instantaneous screening of thousands of catalysts. With the large amount of data created from the machine learning/volcano plot tandem, we show how the resulting database can be mined to garner an enhanced understanding of catalytic processes. Emphasis is also placed on the latest generation of augmented volcano plots, which differ fundamentally from earlier volcanoes by elimination of the use of linear free energy scaling relationships and by assessment of the similarity of the complete catalytic cycle energy profile to that for an ideal reference species that is used to discriminate catalytic performance.We conclude by examining a handful of applications of molecular volcano plots to interesting problems in homogeneous catalysis and offering thoughts on the future prospects and uses of this new set of tools.

Low-Temperature Intramolecular [4+2] Cycloaddition of Allenes with Arenes for the Synthesis of Diene Ligands.

The intramolecular [4+2] cycloaddition between arenes and allenes first reported by Himbert gives rapid access to rigid polycyclic scaffolds. Herein, we report a one-pot oxyalkynylation/cycloaddition reaction proceeding under mild conditions (23-90 °C) and providing complex polycyclic architectures with high efficiency, and atom and step economy. The bicyclo[2.2.2]octadiene products were obtained with a wide variety of useful functional groups and were successfully applied as chiral ligands for metal catalysis. Computational studies gave a first rationalization of the low activation energy for the cycloaddition based on counter-intuitive favorable dispersive interactions in the transition state.

Probing Substrate Scope with Molecular Volcanoes.

Molecular volcano plots, a tool generally used to identify ideal catalysts for a given chemical transformation, are employed in an alternative fashion to examine the substrate scope by revealing the range of substrates that can be accommodated by a single catalyst. Here the power to rationalize and better understand the substrate scope is demonstrated through the evaluation of the energetics of various electrophilic substrates for a Suzuki cross-coupling reaction. The plots reproduce experimentally known trends while quantifying the magnitude to which the overall activity depends on a substrate's intrinsic electronic and steric properties and how these energetics are altered by the addition of substituent groups or changes to the core structure. Overall, the information revealed by these substrate volcanoes can be used in tandem with conventional molecular volcano plots to identify general catalyst design principles for a series of substrates.

Data-powered augmented volcano plots for homogeneous catalysis.

Given the computational resources available today, data-driven approaches can propel the next leap forward in catalyst design. Using a data-driven inspired workflow consisting of data generation, statistical analysis, and dimensionality reduction algorithms we explore trends surrounding the thermodynamics of a model hydroformylation reaction catalyzed by group 9 metals bearing phosphine ligands. Specifically, we introduce "augmented volcano plots" as a means to easily visualize the similarity of each catalyst's complete catalytic cycle energy profile to that of a hypothetical ideal reference profile without relying upon linear scaling relationships. In addition to quickly identifying catalysts that most closely match the ideal thermodynamic catalytic cycle energy profile, these maps also enable a more refined comparison of closely lying species in standard volcano plots. For the reaction studied here, they inherently uncover the presence of multiple sets of scaling relationships differentiated by metal type, where iridium catalysts follow distinct relationships from cobalt/rhodium catalysts and have profiles that more closely match the ideal thermodynamic profile. Reconstituted molecular volcano plots confirm the findings of the augmented volcanoes by showing that hydroformylation thermodynamics are governed by two distinct volcano shapes, one for iridium catalysts and a second for cobalt/rhodium species.

Functional Models of the Nickel Pincer Nucleotide Cofactor of Lactate Racemase.

A novel nickel pincer cofactor was recently discovered in lactate racemase. Reported here are three synthetic nickel pincer complexes that are both structural and functional models of the pincer cofactor in lactate racemase. DFT computations suggest the ipso-carbon atom of the pyridinium pincer ligands act as a hydride acceptor for lactate isomerization, whereas an organometallic pathway involving nickel-mediated ß-hydride elimination is less favored.