Design and Optimization of Catalysts Based on Mechanistic Insights Derived from Quantum Chemical Reaction Modeling

Bifunctional iminophosphorane organocatalyst with additional hydrogen bonding: Calculations predict enhanced catalytic performance in a michael addition reaction

A new iminophosphorane-thiourea superbase was rationally designed and investigated as an organocatalyst for the enantioselective Michael addition reaction of nitromethane to 4-phenylbut-3-en-2-one. Starting from an iminophosphorane-thiourea organocatalyst structure already known, we have used theoretical calculations to determine the structures of transition states involved in the carbon-carbon bond formation step and carried out structural modifications to accelerate the reaction rate and to increase the enantioselectivity. The effective structural modification was adding a rigid hydroxyl group able to make an additional hydrogen bond to the transition state, producing a substantial decrease of the ΔG‡ by 7 kcal mol-1. The enantiomeric excess is predicted to be above of 97% using the reliable M06-2X and ωB97M - V functionals. The determination of the complete reaction mechanism and free energy profile was followed by a detailed microkinetic analysis. The present study points out a new direction for structural modifications on this kind of organocatalyst.

Differentiating the Yield of Chemical Reactions Using Parameters in First-Order Kinetic Equations to Identify Elementary Steps That Control the Reactivity from Complicated Reaction Path Networks

The yield of a chemical reaction is obtained by solving its rate equation. This study introduces an approach for differentiating yields by utilizing the parameters of the rate equation, which is expressed as a first-order linear differential equation. The yield derivative for a specific pair of reactants and products is derived by mathematically expressing the rate constant matrix contraction method, which is a simple kinetic analysis method. The parameters of the rate equation are the Gibbs energies of the intermediates and transition states in the reaction path network used to formulate the rate equation. Thus, our approach for differentiating the yield allows a numerical evaluation of the contribution of energy variation to the yield for each intermediate and transition state in the reaction path network. In other words, a comparison of these values automatically extracts the factors affecting the yield from a complicated reaction path network consisting of numerous reaction paths and intermediates. This study verifies the behavior of the proposed approach through numerical experiments on the reaction path networks of a model system and the Rh-catalyzed hydroformylation reaction. Moreover, the possibility of using this approach for designing ligands in organometallic catalysts is discussed.

Computationally Guided Ligand Discovery from Compound Libraries and Discovery of a New Class of Ligands for Ni-Catalyzed Cross-Electrophile Coupling of Challenging Quinoline Halides

Although screening technology has heavily impacted the fields of metal catalysis and drug discovery, its application to the discovery of new catalyst classes has been limited. The diversity of on- and off-cycle pathways, combined with incomplete mechanistic understanding, means that screens of potential new ligands have thus far been guided by intuitive analysis of the metal binding potential. This has resulted in the discovery of new classes of ligands, but the low hit rates have limited the use of this strategy because large screens require considerable cost and effort. Here, we demonstrate a method to identify promising screening directions via simple and scalable computational and linear regression tools that leads to a substantial improvement in hit rate, enabling the use of smaller screens to find new ligands. The application of this approach to a particular example of Ni-catalyzed cross-electrophile coupling of aryl halides with alkyl halides revealed a previously overlooked trend: reactions with more electron-poor amidine ligands result in a higher yield. Focused screens utilizing this trend were more successful than serendipity-based screening and led to the discovery of two new types of ligands, pyridyl oxadiazoles and pyridyl oximes. These ligands are especially effective for couplings of bromo- and chloroquinolines and isoquinolines, where they are now the state of the art. The simplicity of these models with parameters derived from metal-free ligand structures should make this approach scalable and widely accessible.

On Accelerating Substrate Optimization Using Computational Gibbs Energy Barriers: A Numerical Consideration Utilizing a Computational Data Set

Substrate optimization is a time- and resource-consuming step in organic synthesis. Recent advances in chemo- and materials-informatics provide systematic and efficient procedures utilizing tools such as Bayesian optimization (BO). This study explores the possibility of reducing the required experiments further by utilizing computational Gibbs energy barriers. To thoroughly validate the impact of using computational Gibbs energy barriers in BO-assisted substrate optimization, this study employs a computational Gibbs energy barrier data set in the literature and performs an extensive numerical investigation virtually regarding the Gibbs energy barriers as virtual experimental results and those with systematic and random noises as virtual computational results. The present numerical investigation shows that even the computational reactivity affected by noises of as much as 20 kJ/mol helps reduce the number of required experiments.

Palladium-Catalyzed Synthesis of Linked Bis-Heterocycles─Synthesis and Investigation of Photophysical Properties

A palladium-catalyzed domino C-N coupling/Cacchi reaction is reported. Design of photoluminescent bis-heterocycles, aided by density functional theory calculations, was performed with synthetic yields up to 98%. The photophysical properties of the products accessed via this strategy were part of a comprehensive study that led to broad emission spectra and quantum yields of up to 0.59. Mechanistic experiments confirmed bromoalkynes as competent intermediates, and a density functional theory investigation suggests a pathway involving initial oxidative addition into the cis C-Br bond of the gem-dihaloolefin.

Picking the lock of coordination cage catalysis

The design principles of metallo-organic assembly reactions have facilitated access to hundreds of coordination cages of varying size and shape. Many of these assemblies possess a well-defined cavity capable of hosting a guest, pictorially mimicking the action of a substrate binding to the active site of an enzyme. While there are now a growing collection of coordination cages that show highly proficient catalysis, exhibiting both excellent activity and efficient turnover, this number is still small compared to the vast library of metal-organic structures that are known. In this review, we will attempt to unpick and discuss the key features that make an effective coordination cage catalyst, linking structure to activity (and selectivity) using lessons learnt from both experimental and computational analysis of the most notable exemplars. We will also provide an outlook for this area, reasoning why coordination cages have the potential to become the gold-standard in (synthetic) non-covalent catalysis.

Biomolecule-Driven Two-Factor Authentication Strategy for Access Control of Molecular Devices

The rise of DNA nanotechnology is promoting the development of molecular security devices and marking an essential change in information security technology, to one that can resist the threats resulting from the increase in computing power, brute force attempts, and quantum computing. However, developing a secure and reliable access control strategy to guarantee the confidentiality of molecular security devices is still a challenge. Here, a biomolecule-driven two-factor authentication strategy for access control of molecular devices is developed. Importantly, the two-factor is realized by applying the specificity and nicking properties of the nicking enzyme and the programmable design of the DNA sequence, endowing it with the characteristic of a one-time password. To demonstrate the feasibility of this strategy, an access control module is designed and integrated to further construct a role-based molecular access control device. By constructing a command library composed of three commands (Ca, Cb, Ca and Cb), the authorized access of three roles in the molecular device is realized, in which the command Ca corresponds to the authorization of role A, Cb corresponds to the authorization of role B, and Ca and Cb corresponds to the authorization of role C. In this way, when users access the device, they not only need the correct factor but also need to apply for role authorization in advance to obtain secret information. This strategy provides a highly robust method for the research on access control of molecular devices and lays the foundation for research on the next generation of information security.

Synthesis of Hidden Subgroup Quantum Algorithms and Quantum Chemical Dynamics

We describe a general formalism for quantum dynamics and show how this formalism subsumes several quantum algorithms, including the Deutsch, Deutsch-Jozsa, Bernstein-Vazirani, Simon, and Shor algorithms as well as the conventional approach to quantum dynamics based on tensor networks. The common framework exposes similarities among quantum algorithms and natural quantum phenomena: we illustrate this connection by showing how the correlated behavior of protons in water wire systems that are common in many biological and materials systems parallels the structure of Shor's algorithm.

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling of synthetic transformation with the required extrapolative ability and chemical interpretability. To meet the gap between the rich domain knowledge of chemistry and the advanced molecular graph model, herein we report a knowledge-based graph model that embeds the digitalized steric and electronic information. In addition, a molecular interaction module is developed to enable the learning of the synergistic influence of reaction components. In this study, we demonstrate that this knowledge-based graph model achieves excellent predictions of reaction yield and stereoselectivity, whose extrapolative ability is corroborated by additional scaffold-based data splittings and experimental verifications with new catalysts. Because of the embedding of local environment, the model allows the atomic level of interpretation of the steric and electronic influence on the overall synthetic performance, which serves as a useful guide for the molecular engineering towards the target synthetic function. This model offers an extrapolative and interpretable approach for reaction performance prediction, pointing out the importance of chemical knowledge-constrained reaction modelling for synthetic purpose.

Data-driven design of new chiral carboxylic acid for construction of indoles with C-central and C-N axial chirality via cobalt catalysis

Challenging enantio- and diastereoselective cobalt-catalyzed C-H alkylation has been realized by an innovative data-driven knowledge transfer strategy. Harnessing the statistics of a related transformation as the knowledge source, the designed machine learning (ML) model took advantage of delta learning and enabled accurate and extrapolative enantioselectivity predictions. Powered by the knowledge transfer model, the virtual screening of a broad scope of 360 chiral carboxylic acids led to the discovery of a new catalyst featuring an intriguing furyl moiety. Further experiments verified that the predicted chiral carboxylic acid can achieve excellent stereochemical control for the target C-H alkylation, which supported the expedient synthesis for a large library of substituted indoles with C-central and C-N axial chirality. The reported machine learning approach provides a powerful data engine to accelerate the discovery of molecular catalysis by harnessing the hidden value of the available structure-performance statistics.

Computational molecular refinement to enhance enantioselectivity by reinforcing hydrogen bonding interactions in major reaction pathway

Computational analyses have revealed that the distortion of a catalyst and the substrates and their interactions are key to determining the stability of the transition state. Hence, two strategies "distortion strategy" and "interaction strategy" can be proposed for improving enantiomeric excess in enantioselective reactions. The "distortion strategy" is used as a conventional approach that destabilizes the TS (transition state) of the minor pathway. On the other hand, the "interaction strategy" focuses on the stabilization of the TS of the major pathway in which an enhancement of the reaction rate is expected. To realize this strategy, we envisioned the TS stabilization of the major reaction pathway by reinforcing hydrogen bonding and adopted the chiral phosphoric acid-catalysed enantioselective Diels-Alder reaction of 2-vinylquinolines with dienylcarbamates. The intended "interaction strategy" led to remarkable improvements in the enantioselectivity and reaction rate.

A Computational Study on the Reaction Mechanism of Stereocontrolled Synthesis of β-Lactam within [2]Rotaxane

The macrocycle effect of [2]rotaxane on the highly trans-stereoselective cyclization reaction of N-benzylfumaramide was extensively investigated by various computational methods, including DFT and high-level DLPNO-CCSD(T) methods. Our computational results suggest that the most favorable mechanism of the CsOH-promoted cyclization of the fumaramide into trans-β-lactam within [2]rotaxane initiates with deprotonation of a N-benzyl group of the interlocked fumaramide substrate by CsOH, followed by the trans-selective C-C bond formation and protonation by one amide functional group of the macrocycle. Our distortion/interaction analysis further shows that the uncommon trans-stereoselective cyclization forming β-lactam within the rotaxane may be attributed to a higher distortion energy (mainly from the distortion of the twisted cis-fumaramide conformation enforced by the rotaxane). Our systematic study should give deeper mechanistic insight into the reaction mechanism influenced by a supramolecular host.

Electron-rich benzofulvenes as effective dipolarophiles in copper(i)-catalyzed asymmetric 1,3-dipolar cycloaddition of azomethine ylides

A series of benzofulvenes without any electron-withdrawing substituents were employed as 2π-type dipolarophiles for the first time to participate in Cu(i)-catalyzed asymmetric 1,3-dipolar cycloaddition (1,3-DC) reactions of azomethine ylides. An intrinsic non-benzenoid aromatic characteristic from benzofulvenes serves as a key driving force for activation of the electron-rich benzofulvenes. Utilizing the current methodology, a wide range of multi-substituted chiral spiro-pyrrolidine derivatives containing two contiguous all-carbon quaternary centers were formed in good yield with exclusive chemo-/regioselectivity and high to excellent stereoselectivity. Computational mechanistic studies elucidate the origin of the stereochemical outcome and the chemoselectivity, in which the thermostability of these cycloaddition products is the major factor.

Quantum Mechanical Prediction of Dissociation Constants for Thiazol-2-imine Derivatives

As weak acids or bases, in solution, drug molecules are in either their ionized or nonionized states. A high degree of ionization is essential for good water solubility of a drug molecule and is required for drug-receptor interactions, whereas the nonionized form improves a drug's lipophilicity, allowing the ligand to cross the cell membrane. The penetration of a drug ligand through cell membranes is mainly governed by the pKa of the drug molecule and the membrane environment. In this study, with the aim of predicting the acetonitrile pKa's (pKa(MeCN)) of eight drug-like thiazol-2-imine derivatives, we propose a very accurate and computationally affordable protocol by using several quantum mechanical approaches. Benchmark studies were conducted on a set of training molecules, which were selected from the literature with known pKa(water) and pKa(MeCN). Highly well-correlated pKa values were obtained when the calculations were performed with the isodesmic method at the M062X/6-31G** level of theory in conjunction with SMD solvation model for nitrogen-containing heterocycles. Finally, experimentally unknown pKa(MeCN) values of eight thiazol-2-imine structures, which were previously synthesized by some of us, are proposed.

Catalyst-Substrate Helical Character Matching Determines the Enantioselectivity in the Ishihara-Type Iodoarenes Catalyzed Asymmetric Kita-Dearomative Spirolactonization

Catalyst design has traditionally focused on rigid structural elements to prevent conformational flexibility. Ishihara's elegant design of conformationally flexible C₂-symmetric iodoarenes, a new class of privileged organocatalysts, for the catalytic asymmetric dearomatization (CADA) of naphthols is a notable exception. Despite the widespread use of the Ishihara catalysts for CADAs, the reaction mechanism remains the subject of debate, and the mode of asymmetric induction has not been well established. Here, we report an in-depth computational investigation of three possible mechanisms in the literature. Our results, however, reveal that this reaction is best rationalized by a fourth mechanism called "proton-transfer-coupled-dearomatization (PTCD)", which is predicted to be strongly favored over other competing pathways. The PTCD mechanism is consistent with a control experiment and further validated by applying it to rationalize the enantioselectivities. Oxidation of the flexible I(I) catalyst to catalytic active I(III) species induces a defined C₂-symmetric helical chiral environment with a delicate balance between flexibility and rigidity. A match/mismatch effect between the active catalyst and the substrate's helical shape in the dearomatization transition states was observed. The helical shape match allows the active catalyst to adapt its conformation to maximize attractive noncovalent interactions, including I(III)···O halogen bond, N-H···O hydrogen bond, and π···π stacking, to stabilize the favored transition state. A stereochemical model capable of rationalizing the effect of catalyst structural variation on the enantioselectivities is developed. The present study enriches our understanding of how flexible catalysts achieve high stereoinduction and may serve as an inspiration for the future exploration of conformational flexibility for new catalyst designs.

Transition metal oxide complexes as molecular catalysts for selective methane to methanol transformation: any prospects or time to retire?

Transition metal oxides have been extensively used in the literature for the conversion of methane to methanol. Despite the progress made over the past decades, no method with satisfactory performance or economic viability has been detected. The main bottleneck is that the produced methanol oxidizes further due to its weaker C-H bond than that of methane. Every improvement in the efficiency of a catalyst to activate methane leads to reduction of the selectivity towards methanol. Is it therefore prudent to keep studying (both theoretically and experimentally) metal oxides as catalysts for the quantitative conversion of methane to methanol? This perspective focuses on molecular metal oxide complexes and suggests strategies to bypass the current bottlenecks with higher weight on the computational chemistry side. We first discuss the electronic structure of metal oxides, followed by assessing the role of the ligands in the reactivity of the catalysts. For better selectivity, we propose that metal oxide anionic complexes should be explored further, while hydrophylic cavities in the vicinity of the metal oxide can perturb the transition-state structure for methanol increasing appreciably the activation barrier for methanol. We also emphasize that computational studies should target the activation reaction of methanol (and not only methane), the study of complete catalytic cycles (including the recombination and oxidation steps), and the use of molecular oxygen as an oxidant. The titled chemical conversion is an excellent challenge for theory and we believe that computational studies should lead the field in the future. It is finally shown that bottom-up approaches offer a systematic way for exploration of the chemical space and should still be applied in parallel with the recently popular machine learning techniques. To answer the question of the title, we believe that metal oxides should still be considered provided that we change our focus and perform more systematic investigations on the activation of methanol.

Density functional theory and CCSD(T) evaluation of ionization potentials, redox potentials, and bond energies related to zirconocene polymerization catalysts

Quantum-mechanical-based computational design of molecular catalysts requires accurate and fast electronic structure calculations to determine and predict properties of transition-metal complexes. For Zr-based molecular complexes related to polyethylene catalysis, previous evaluation of density functional theory (DFT) and wavefunction methods only examined oxides and halides or select reaction barrier heights. In this work, we evaluate the performance of DFT against experimental redox potentials and bond dissociation enthalpies (BDEs) for zirconocene complexes directly relevant to ethylene polymerization catalysis. We also examined the ability of DFT to compute the fourth atomic ionization potential of zirconium and the effect the basis set selection has on the ionization potential computed with CCSD(T). Generally, the atomic ionization potential and redox potentials are very well reproduced by DFT, but we discovered relatively large deviations of DFT-calculated BDEs compared to experiment. However, evaluation of BDEs with CCSD(T) suggests that experimental values should be revisited, and our CCSD(T) values should be taken as most accurate.

Overreact, an in silico lab: Automative quantum chemical microkinetic simulations for complex chemical reactions

Today's demand for precisely predicting chemical reactions from first principles requires research to go beyond Gibbs' free energy diagrams and consider other effects such as concentrations and quantum tunneling. The present work introduces overreact, a novel Python package for propagating chemical reactions over time using data from computational chemistry only. The overreact code infers all differential equations and parameters from a simple input that consists of a set of chemical equations and quantum chemistry package outputs for each chemical species. We evaluate some applications from the literature: gas-phase eclipsed-staggered isomerization of ethane, gas-phase umbrella inversion of ammonia, gas-phase degradation of methane by chlorine radical, and three solvation-phase reactions. Furthermore, we comment on a simple solvation-phase acid-base equilibrium. We show how it is possible to achieve reaction profiles and information matching experiments.

Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery

The field of predictive chemistry relates to the development of models able to describe how molecules interact and react. It encompasses the long-standing task of computer-aided retrosynthesis, but is far more reaching and ambitious in its goals. In this review, we summarize several areas where predictive chemistry models hold the potential to accelerate the deployment, development, and discovery of organic reactions and advance synthetic chemistry.

Oxidation and Reduction Pathways in the Knowles Hydroamination via a Photoredox-Catalyzed Radical Reaction

Systematic reaction path exploration revealed the entire mechanism of Knowles's light-promoted catalytic intramolecular hydroamination. Bond formation/cleavage competes with single electron transfer (SET) between the catalyst and substrate. These processes are described by adiabatic processes through transition states in an electronic state and non-radiative transitions through the seam of crossings (SX) between different electronic states. This study determined the energetically favorable SET path by introducing a practical computational model representing SET as non-adiabatic transitions via SXs between substrate's potential energy surfaces for different charge states adjusted based on the catalyst's redox potential. Calculations showed that the reduction and proton shuttle process proceeded concertedly. Also, the relative importance of SET paths (giving the product and leading back to the reactant) varies depending on the catalyst's redox potential, affecting the yield.

ReNeGate: A Reaction Network Graph-Theoretical Tool for Automated Mechanistic Studies in Computational Homogeneous Catalysis

Exploration of the chemical reaction space of chemical transformations in multicomponent mixtures is one of the main challenges in contemporary computational chemistry. To remove expert bias from mechanistic studies and to discover new chemistries, an automated graph-theoretical methodology is proposed, which puts forward a network formalism of homogeneous catalysis reactions and utilizes a network analysis tool for mechanistic studies. The method can be used for analyzing trajectories with single and multiple catalytic species and can provide unique conformers of catalysts including multinuclear catalyst clusters along with other catalytic mixture components. The presented three-step approach has the integrated ability to handle multicomponent catalytic systems of arbitrary complexity (mixtures of reactants, catalyst precursors, ligands, additives, and solvents). It is not limited to predefined chemical rules, does not require prealignment of reaction mixture components consistent with a reaction coordinate, and is not agnostic to the chemical nature of transformations. Conformer exploration, reactive event identification, and reaction network analysis are the main steps taken for identifying the pathways in catalytic systems given the starting precatalytic reaction mixture as the input. Such a methodology allows us to efficiently explore catalytic systems in realistic conditions for either previously observed or completely unknown reactive events in the context of a network representing different intermediates. Our workflow for the catalytic reaction space exploration exclusively focuses on the identification of thermodynamically feasible conversion channels, representative of the (secondary) catalyst deactivation or inhibition paths, which are usually most difficult to anticipate based solely on expert chemical knowledge. Thus, the expert bias is sought to be removed at all steps, and the chemical intuition is limited to the choice of the thermodynamic constraint imposed by the applicable experimental conditions in terms of threshold energy values for allowed transformations. The capabilities of the proposed methodology have been tested by exploring the reactivity of Mn complexes relevant for catalytic hydrogenation chemistry to verify previously postulated activation mechanisms and unravel unexpected reaction channels relevant to rare deactivation events.

Encapsulation Enhances the Catalytic Activity of C-N Coupling: Reaction Mechanism of a Cu(I)/Calix[8]arene Supramolecular Catalyst

Development of C-N coupling methodologies based on Earth-abundant metals is a promising strategy in homogeneous catalysis for sustainable processes. However, such systems suffer from deactivation and low catalytic activity. We here report that encapsulation of Cu(I) within the phenanthroyl-containing calix[8]arene derivative 1,5-(2,9-dimethyl-1,10-phenanthroyl)-2,3,4,6,7,8-hexamethyl-p-tert-butylcalix[8]arene (C8PhenMe6 ) significantly enhances C-N coupling activity up to 92 % yield in the reaction of aryl halides and aryl amines, with low catalyst loadings (2.5 % mol). A tailored multiscale computational protocol based on Molecular Dynamics simulations and DFT investigations revealed an oxidative addition/reductive elimination process of the supramolecular catalyst [Cu(C8PhenMe6)I]. The computational investigations uncovered the origins of the enhanced catalytic activity over its molecular analogues: Catalyst deactivation through dimerization is prevented, and product release facilitated. Capturing the dynamic profile of the macrocycle and the impact of non-covalent interactions on reactivity allows for the rationalization of the behavior of the flexible supramolecular catalysts employed.

Monitoring Reaction Intermediates to Predict Enantioselectivity Using Mass Spectrometry**

Enantioselective reactions are at the core of chemical synthesis. Their development mostly relies on prior knowledge, laborious product analysis and post‐rationalization by theoretical methods. Here, we introduce a simple and fast method to determine enantioselectivities based on mass spectrometry. The method is based on ion mobility separation of diastereomeric intermediates, formed from a chiral catalyst and prochiral reactants, and delayed reactant labeling experiments to link the mass spectra with the reaction kinetics in solution. The data provide rate constants along the reaction paths for the individual diastereomeric intermediates, revealing the origins of enantioselectivity. Using the derived kinetics, the enantioselectivity of the overall reaction can be predicted. Hence, this method can offer a rapid discovery and optimization of enantioselective reactions in the future. We illustrate the method for the addition of cyclopentadiene (CP) to an α,β‐unsaturated aldehyde catalyzed by a diarylprolinol silyl ether.

Quantum Chemical Calculations to Trace Back Reaction Paths for the Prediction of Reactants

The long-due development of a computational method for the ab initio prediction of chemical reactants that provide a target compound has been hampered by the combinatorial explosion that occurs when reactions consist of multiple elementary reaction processes. To address this challenge, we have developed a quantum chemical calculation method that can enumerate the reactant candidates from a given target compound by combining an exhaustive automated reaction path search method with a kinetics method for narrowing down the possibilities. Two conventional name reactions were then assessed by tracing back the reaction paths using this new method to determine whether the known reactants could be identified. Our method is expected to be a powerful tool for the prediction of reactants and the discovery of new reactions.

New Insights and Predictions into Complex Homogeneous Reactions Enabled by Computational Chemistry in Synergy with Experiments: Isotopes and Mechanisms

Homogeneous catalysis and biocatalysis have been widely applied in synthetic, medicinal, and energy chemistry as well as synthetic biology. Driven by developments of new computational chemistry methods and better computer hardware, computational chemistry has become an essentially indispensable mechanistic "instrument" to help understand structures and decipher reaction mechanisms in catalysis. In addition, synergy between computational and experimental chemistry deepens our mechanistic understanding, which further promotes the rational design of new catalysts. In this Account, we summarize new or deeper mechanistic insights (including isotope, dispersion, and dynamical effects) into several complex homogeneous reactions from our systematic computational studies along with subsequent experimental studies by different groups. Apart from uncovering new mechanisms in some reactions, a few computational predictions (such as excited-state heavy-atom tunneling, steric-controlled enantioswitching, and a new geminal addition mechanism) based on our mechanistic insights were further verified by ensuing experiments.The Zimmerman group developed a photoinduced triplet di-π-methane rearrangement to form cyclopropane derivatives. Recently, our computational study predicted the first excited-state heavy-atom (carbon) quantum tunneling in one triplet di-π-methane rearrangement, in which the reaction rates and ¹²C/¹³C kinetic isotope effects (KIEs) can be enhanced by quantum tunneling at low temperatures. This unprecedented excited-state heavy-atom tunneling in a photoinduced reaction has recently been verified by an experimental ¹²C/¹³C KIE study by the Singleton group. Such combined computational and experimental studies should open up opportunities to discover more rare excited-state heavy-atom tunneling in other photoinduced reactions. In addition, we found unexpectedly large secondary KIE values in the five-coordinate Fe(III)-catalyzed hetero-Diels-Alder pathway, even with substantial C-C bond formation, due to the non-negligible equilibrium isotope effect (EIE) derived from altered metal coordination. Therefore, these KIE values cannot reliably reflect transition-state structures for the five-coordinate metal pathway. Furthermore, our density functional theory (DFT) quasi-classical molecular dynamics (MD) simulations demonstrated that the coordination mode and/or spin state of the iron metal as well as an electric field can affect the dynamics of this reaction (e.g., the dynamically stepwise process, the entrance/exit reaction channels).Moreover, we unveiled a new reaction mechanism to account for the uncommon Ru(II)-catalyzed geminal-addition semihydrogenation and hydroboration of silyl alkynes. Our proposed key gem-Ru(II)-carbene intermediates derived from double migrations on the same alkyne carbon were verified by crossover experiments. Additionally, our DFT MD simulations suggested that the first hydrogen migration transition-state structures may directly and quickly form the key gem-Ru-carbene structures, thereby "bypassing" the second migration step. Furthermore, our extensive study revealed the origin of the enantioselectivity of the Cu(I)-catalyzed 1,3-dipolar cycloaddition of azomethine ylides with β-substituted alkenyl bicyclic heteroarenes enabled by dual coordination of both substrates. Such mechanistic insights promoted our computational predictions of the enantioselectivity reversal for the corresponding monocyclic heteroarene substrates and the regiospecific addition to the less reactive internal C═C bond of one diene substrate. These predictions were proven by our experimental collaborators. Finally, our mechanistic insights into a few other reactions are also presented. Overall, we hope that these interactive computational and experimental studies enrich our mechanistic understanding and aid in reaction development.

Efficient synthesis of cyclic carbonates from CO2 under ambient conditions over Zn(betaine)2Br2: experimental and theoretical studies

It is very interesting to synthesize high value-added chemicals from CO₂ under mild conditions with low energy consumption. Here, we report that a novel catalyst, Zn(betaine)₂Br₂, can efficiently promote the cycloaddition of CO₂ with epoxides to synthesize cyclic carbonates under ambient conditions (30 °C, 1 atm). DFT calculations provide important insights into the mechanism, particularly the unusual synergistic catalytic action of Zn²⁺, Br^- and NR⁴⁺, which is the critical factor for the outstanding performance of Zn(betaine)₂Br₂. The unique features of the catalyst are that it is cheap, green and very easy to prepare.

Computational discoveries of reaction mechanisms: recent highlights and emerging challenges

This review examines some of the notable advances and trends that have shaped the field of computational elucidation of organic reaction mechanisms over the last 10-15 years. It highlights the types of mechanistic problems that have recently become possible to study and summarizes the methodological developments that have permitted these new advances. Case studies are taken from three representative areas of organic chemistry-asymmetric catalysis, glycosylation reactions, and single electron transfer reactions-which illustrate themes common to the broader field. These include the trend towards modelling systems that are increasingly complex (both structurally and mechanistically), the growing appreciation of the mechanistic roles of non-covalent interactions, and the increasing ability to explore dynamical features of reaction mechanisms. Some interesting new challenges that have emerged in the field are identified.

Mechanism and Stereochemistry of Rhodium-Catalyzed [5 + 2 + 1] Cycloaddition of Ene-Vinylcyclopropanes and Carbon Monoxide Revealed by Visual Kinetic Analysis and Quantum Chemical Calculations

Previously, we developed a rhodium-catalyzed [5 + 2 + 1] cycloaddition of ene-vinylcyclopropanes (ene-VCPs) and carbon monoxide to synthesize eight-membered carbocycles. The efficiency of this reaction can be appreciated from its application in the synthesis of several natural products. Herein we report the results of a 15-year investigation into the mechanism of the [5 + 2 + 1] cycloaddition by applying visual kinetic analysis and high-level quantum chemical calculations at the DLPNO-CCSD(T)//BMK level. According to the kinetic measurements, the resting state of the catalyst possesses a dimeric structure (with two rhodium centers) whereas the active catalytic species is monomeric (with one rhodium center). The catalytic cycle consists of cyclopropane cleavage (the turnover-limiting step), alkene insertion, CO insertion, reductive elimination, and catalyst transfer steps. Other reaction pathways have also been considered but then have been ruled out. The steric origin of the diastereoselectivity (cis versus trans) was revealed by comparing the alkene insertion transition states. In addition, how the double-bond configuration of the VCPs (Z versus E) affects the substrate reactivity and the origins of chemoselectivity ([5 + 2 + 1] versus [5 + 2]) were also investigated. The present study will provide assistance in understanding other carbonylative annulations catalyzed by transition metals.

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.

The computation of reaction selectivity represents an appealing complementary route to experimental studies and a powerful mean to refine catalyst design strategies.

Determining the Three-Dimensional Structures of Silica-Supported Metal Complexes from the Ground Up

The immobilization of molecularly precise metal complexes to substrates, such as silica, provides an attractive platform for the design of active sites in heterogeneous catalysts. Specific steric and electronic variations of the ligand environment enable the development of structure-activity relationships and the knowledge-driven design of catalysts. At present, however, the three-dimensional environment of the precatalyst, much less the active site, is generally not known for heterogeneous single-site catalysts. We explored the degree to which NMR-based surface-to-complex interatomic distances could be used to solve the three-dimensional structures of three silica-supported metal complexes. The structure solution revealed unexpected features related to the environment around the metal that would be difficult to discern otherwise. This approach appears to be highly robust and, due to its simplicity, is readily applied to most single-site catalysts with little extra effort.

Deprotometalation-Iodolysis and Direct Iodination of 1-Arylated 7-Azaindoles: Reactivity Studies and Molecule Properties

Five protocols were first compared for the copper-catalyzed C-N bond formation between 7-azaindole and aryl/heteroaryl iodides/bromides. The 1-arylated 7-azaindoles thus obtained were subjected to deprotometalation-iodolysis sequences using lithium 2,2,6,6-tetramethylpiperidide as the base and the corresponding zinc diamide as an in situ trap. The reactivity of the substrate was discussed in light of the calculated atomic charges and the pK_a values. The behavior of the 1-arylated 7-azaindoles in direct iodination was then studied, and the results explained by considering the HOMO orbital coefficients and the atomic charges. Finally, some of the iodides generated, generally original, were involved in the N-arylation of indole. While crystallographic data were collected for fifteen of the synthesized compounds, biological properties (antimicrobial, antifungal and antioxidant activity) were evaluated for others.

A Dataset of Computational Reaction Barriers for the Claisen Rearrangement: Chemical and Numerical Analysis

Theoretical reaction screening based on Gibbs energy barriers would be promising to accelerate chemical reactions mining. The number of quantum chemical calculations can be reduced by using an optimization algorithm such as genetic algorithm (GA) and Bayesian optimization (BO). The focus of this study is to generate a dataset of reaction barriers of size ∼100000. Such a dataset would be useful to quickly evaluate various implementations of an optimization algorithm such as GA and BO. The dataset includes Gibbs energy barriers of the Claisen rearrangement for ∼100000 molecules computed on the basis of a semiempirical theory PM7. After evaluating its chemical and numerical features, it is found that the dataset well reflects chemical trends of various substitutions and is useful in testing various implementations of an optimization algorithm. The dataset is available in the supplementary material of this paper.