Engineering Frustrated Lewis Pair Active Sites in Porous Organic Scaffolds for Catalytic CO2 Hydrogenation.

Frustrated Lewis pairs (FLPs), featuring reactive combinations of Lewis acids and Lewis bases, have been utilized for myriad metal-free homogeneous catalytic processes. Immobilizing the active Lewis sites to a solid support, especially to porous scaffolds, has shown great potential to ameliorate FLP catalysis by circumventing some of its inherent drawbacks, such as poor product separation and catalyst recyclability. Nevertheless, designing immobilized Lewis pair active sites (LPASs) is challenging due to the requirement of placing the donor and acceptor centers in appropriate geometric arrangements while maintaining the necessary chemical environment to perform catalysis, and clear design rules have not yet been established. In this work, we formulate simple guidelines to build highly active LPASs for direct catalytic hydrogenation of CO2 through a large-scale screening of a diverse library of 25,000 immobilized FLPs. The library is built by introducing boron-containing acidic sites in the vicinity of the existing basic nitrogen sites of the organic linkers of metal-organic frameworks collected in a "top-down" fashion from the CoRE MOF 2019 database. The chemical and geometrical appropriateness of these LPASs for CO2 hydrogenation is determined by evaluating a series of simple descriptors representing the intrinsic strength (acidity and basicity) of the components and their spatial arrangement in the active sites. Analysis of the leading candidates enables the formulation of pragmatic and experimentally relevant design principles which constitute the starting point for further exploration of FLP-based catalysts for the reduction of CO2.

3DReact: Geometric Deep Learning for Chemical Reactions.

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

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.

The fundamental relation between electrohelicity and molecular optical activity.

Electrohelicity arises in molecules such as allene and spiropentadiene when their symmetry is reduced and helical frontier molecular orbitals (MOs) appear. Such molecules are optically active and electrohelicity has been suggested as a possible design principle for increasing the chiroptical response. Here we examine the fundamental link between electrohelicity and optical activity by studying the origin of the electric and magnetic transition dipole moments of the π-π* transitions. We show that the helical character of the MOs drives the optical activity in allene, and we use this knowledge to design allenic molecules with increased chiroptical response. We further examine longer carbyne-like molecules. While the MO helicity also contributes to the optical activity in non-planar butatriene, the simplest cumulene, we show there is no relation between the chiroptical response and the helical π-MOs of tolane, a simple polyyne. Finally, we demonstrate that the optical activity of spiropentadiene is inherently linked to mixing of its two π-systems rather than the helical shape of its occupied π-MOs. We thus find that the fundamental connection between electrohelicity and optical activity is very molecule dependent. Although electrohelicity is not the underlying principle, we show that the chiroptical response can be enhanced through insight into the helical nature of electronic transitions.

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.

Genetic Algorithms for the Discovery of Homogeneous Catalysts.

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts.

Symmetry-Induced Singlet-Triplet Inversions in Non-Alternant Hydrocarbons.

Molecules with inversion of the singlet and triplet excited-state energies are highly promising for the development of organic light-emitting diodes (OLEDs). To date, azaphenalenes are the only class of molecules where these inversions have been identified. Here, we screen a curated database of organic crystal structures to identify existing compounds for violations of Hund's rule in the lowest excited states. We identify two further classes with this behavior. The first, a class of zwitterions, has limited relevance to molecular emitters as the singlet-triplet inversions occur in the third excited singlet state. The second class consists of two D2h -symmetry non-alternant hydrocarbons, a fused azulene dimer and a bicalicene, whose lowest excited singlet states violate Hund's rule. Due to the connectivity of the polycyclic structure, they achieve this symmetry through aromatic stabilization. These hydrocarbons show promise as the next generation of building blocks for OLED emitters.

Modular Synthesis of Benzocyclobutenes via Pd(II)-Catalyzed Oxidative [2+2] Annulation of Arylboronic Acids with Alkenes.

Benzocyclobutenes (BCBs) are highly valuable compounds in organic synthesis, medicinal chemistry, and materials science. However, catalytic modular synthesis of functionalized BCBs from easily accessible starting materials remains limited. We report herein an efficient synthesis of diversely functionalized BCBs by a Pd(II)-catalyzed formal [2+2] annulation between arylboronic acids and alkenes in the presence of N-fluorobenzenesulfonimide (NFSI). An intermolecular carbopalladation followed by palladium oxidation, intramolecular C(sp2)-H activation by a transient C(sp3)-Pd(IV) species, and selective carbon-carbon (C-C) bond-forming reductive elimination from a high-valent five-membered palladacycle is proposed to account for the reaction outcome. Kinetically competent oxidation of alkylPd(II) to alkylPd(IV) species is important to avoid the formation of a Heck adduct. The reaction forges two C-C bonds of the cyclobutene core and is compatible with a wide range of functional groups. No chelating bidentate directing group in the alkene part is needed for this transformation.

Migrative Carbofluorination of Saturated Amides Enabled by Pd-Based Dyotropic Rearrangement.

Directly editing an all-carbon quaternary carbon itself of nonstrained acyclic molecules remains underexploited despite the recent advances in the fields of both C-H and C-C bond activation. Herein, we report a palladium-catalyzed migrative carbofluorination of saturated amides enabled by the activation of both the C(sp3)-H and the Cquaternary-Cσ bonds. In this transformation, the α-quaternary carbon of Weinreb amides is converted to α-tertiary fluoride with concurrent migration of an aryl or an amido group from the α- to ß-carbon. DFT calculations indicate that the dyotropic rearrangement proceeds through an unusual anti-selective [2.1.0] bicyclic transition state. The reaction, compatible with a broad range of functional groups, is stereospecific and is applicable to the synthesis of enantioenriched products.

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.

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.

How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts' conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realization of a reversible steric shielding for the photocontrol of Brønsted basicity of piperidine photoswitchable organocatalysts.

Helical versus linear Jahn-Teller distortions in allene and spiropentadiene radical cations.

The allene radical cation can be stabilized both by Jahn-Teller distortion of the bond lengths and by torsion of the end-groups. However, only the latter happens and the allene radical cation relaxes into a twisted D2 symmetry structure with equal double-bond lengths. Here we revisit the Jahn-Teller distortion of allene and spiropentadiene by assessing the possible implications of their helical π-systems in the radical cations. We describe a general relation between the structure and the number of π-electrons in spiroconjugated and linearly conjugated systems. Through constrained optimizations we compare the stabilization achieved by bond-length alternation and axial torsion in the radical cations, which we explain with a simple frontier molecular orbital (MO) picture. While structurally different, allene and spiropentadiene have similar helical frontier MOs. Both cations relax through torsion because the stabilization of their helical frontier MOs is bigger than that which can be achieved by linear π-conjugation. Electrohelicity thus manifests in molecular systems with partial occupation as a helical π-conjugation effect, which evidently provides more stabilization than its linear counterpart in terms of the Jahn-Teller distortion. This mechanism may be a driving factor for the relaxation in a range of spiroconjugated and linearly conjugated cationic systems.

Assessing the persistence of chalcogen bonds in solution with neural network potentials.

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what the most prevalent non-covalent interactions occurring in a solute-Cl--THF mixture are. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.

Mapping Active Site Geometry to Activity in Immobilized Frustrated Lewis Pair Catalysts.

The immobilization of molecular catalysts imposes spatial constraints on their active site. We reveal that in bifunctional catalysis such constraints can also be utilized as an appealing handle to boost intrinsic activity through judicious control of the active site geometry. To demonstrate this, we develop a pragmatic approach, based on nonlinear scaling relationships, to map the spatial arrangements of the acid-base components of frustrated Lewis pairs (FLPs) to their performance in the catalytic hydrogenation of CO2 . The resulting activity map shows that fixing the donor-acceptor centers at specific distances and locking them into appropriate orientations leads to an unforeseen many-fold increase in the catalytic activity of FLPs compared to their unconstrained counterparts.

Design of Frustrated Lewis Pair Catalysts for Direct Hydrogenation of CO2.

Despite recent progress in the chemistry of frustrated Lewis pairs (FLPs), direct FLP-catalyzed hydrogenation of CO2 remains elusive. From a near-infinite array of plausible Lewis pairs, it is challenging to identify individual combinations that are appropriate for catalyzing this reaction. To this end, we propose a mapping of the chemical composition of FLPs to their activity towards direct catalytic hydrogenation of CO2 into formate. The maps, built upon linear scaling relationships, pinpoint specific FLP combinations with the proper complementary acidity and basicity to optimally balance the energetics of the catalytic cycle. One such combination was experimentally validated to achieve hitherto unreported catalytic turnover for this transformation.

Tuning the Thermal Stability and Photoisomerization of Azoheteroarenes through Macrocycle Strain*.

Azobenzene and its derivatives are one of the most widespread molecular scaffolds used in a range of modern applications, as well as in fundamental research. After photoexcitation, azo-based photoswitches revert back to the most stable isomer on a timescale ( t 1 / 2 ) that determines the range of potential applications. Attempts to bring t 1 / 2 to extreme values prompted the development of azobenzene and azoheteroarene derivatives that either rebalance the E- and Z-isomer stabilities, or exploit unconventional thermal isomerization mechanisms. In the former case, one successful strategy has been the creation of macrocycle strain, which tends to impact the E/Z stability asymmetrically, and thus significantly modify t 1 / 2 . On the bright side, bridged derivatives have shown an improved optical switching owing to the higher quantum yields and absence of degradation. However, in most (if not all) cases, bridged derivatives display a reversed thermal stability (more stable Z-isomer), and smaller t 1 / 2 than the acyclic counterparts, which restricts their potential interest to applications requiring a fast forward and backwards switch. In this paper, the impact of alkyl bridges on the thermal stability of phenyl-azoheteroarenes is investigated by using computational methods, and it is revealed that it is indeed possible to combine such improved photoswitching characteristics while preserving the regular thermal stability (more stable E-isomer), and increased t 1 / 2 values under the appropriate connectivity and bridge length.

Structure-Property Relationships in Bithiophenes with Hydrogen-Bonded Substituents.

The use of crystal engineering to control the supramolecular arrangement of π-conjugated molecules in the solid-state is of considerable interest for the development of novel organic electronic materials. In this study, we investigated the effect of combining of two types of supramolecular interaction with different geometric requirements, amide hydrogen bonding and π-interactions, on the π-overlap between calamitic π-conjugated cores. To this end, we prepared two series of bithiophene diesters and diamides with methylene, ethylene, or propylene spacers between the bithiophene core and the functional groups in their terminal substituents. The hydrogen-bonded bithiophene diamides showed significantly denser packing of the bithiophene cores than the diesters and other known α,ω-disubstituted bithiophenes. The bithiophene packing density reach a maximum in the bithiophene diamide with an ethylene spacer, which had the smallest longitudinal bithiophene displacement and infinite 1D arrays of electronically conjugated, parallel, and almost linear N-H⋅⋅⋅O=C hydrogen bonds. The synergistic hydrogen bonding and π-interactions were attributed to the favorable conformation mechanics of the ethylene spacer and resulted in H-type spectroscopic aggregates in solid-state absorption spectroscopy. These results demonstrate that the optoelectronic properties of π-conjugated materials in the solid-state may be tailored systematically by side-chain engineering, and hence that this approach has significant potential for the design of organic and polymer semiconductors.

Tuning the π-Accepting Properties of Mesoionic Carbenes: A Combined Computational and Experimental Study.

Mesoionic imidazolylidenes are recognized as excellent electron-donating ligands in organometallic and main group chemistry. However, these carbene ligands typically show poor π-accepting properties. A computational analysis of 71 mesoionic imidazolylidenes that bear different aryl or heteroaryl substituents in C2 position was performed. The study has revealed that a diphenyltriazinyl (Dpt) substituent renders the corresponding carbene particularly π-acidic. The computational results could be corroborated experimentally. A mesoionic imidazolylidene with a Dpt substituent was found to be a better σ-donor and a better π-acceptor compared to an Arduengo-type N-heterocyclic carbene. To demonstrate the utility of the new carbene, the ligand was used to stabilize a low-valent paramagnetic tin compound.

Impact of quantum-chemical metrics on the machine learning prediction of electron density.

Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics of the target properties. One of the most appealing quantum-chemical properties for regression models is the electron density, and some of us recently proposed a transferable and scalable model based on the decomposition of the density onto an atom-centered basis set. The decomposition, as well as the training of the model, is at its core a minimization of some loss function, which can be arbitrarily chosen and may lead to results of different quality. Well-studied in the context of density fitting (DF), the impact of the metric on the performance of ML models has not been analyzed yet. In this work, we compare predictions obtained using the overlap and the Coulomb-repulsion metrics for both decomposition and training. As expected, the Coulomb metric used as both the DF and ML loss functions leads to the best results for the electrostatic potential and dipole moments. The origin of this difference lies in the fact that the model is not constrained to predict densities that integrate to the exact number of electrons N. Since an a posteriori correction for the number of electrons decreases the errors, we proposed a modification of the model, where N is included directly into the kernel function, which allowed lowering of the errors on the test and out-of-sample sets.