The Challenge of Reproducing with Calculations Raw Experimental Kinetic Data for an Organic Reaction

Computational study on the mechanism for the synthesis of active pharmaceutical ingredients nitrofurantoin and dantrolene in both solution and mechanochemical conditions

A combination of density functional theory (DFT) calculations and microkinetic simulations is applied to the study of condensation between N-acyl-hydrazides and aldehydes in acidic media to produce the active pharmaceutical ingredients (API) nitrofurantoin and dantrolene. Previous experimental reports have shown that the use of ball milling conditions leads to a reduction in the reaction time, which is associated with a significant reduction of waste. This result is reproduced by the current calculations, which additionally provide a detailed mechanistic explanation for this behavior.

Revealing the Mechanistic Features of an Electrosynthetic Catalytic Reaction and the Role of Redox Mediators through DFT Calculations and Microkinetic Modeling

Organic electrosynthesis is an emerging field that provides original selectivity while adding features of atom economy, sustainability, and selectivity. Electrosynthesis is often enhanced by redox mediators or electroauxiliaries. The mechanistic understanding of organic electrosynthesis is however often limited by the low lifetime of intermediates and its difficult detection. In this work, we report a computational analysis of the mechanism of an appealing reaction previously reported by Mei and co-workers which is catalyzed by copper and employs iodide as redox mediator. Our scheme combines DFT calculations with microkinetic modeling and covers both the reaction in solution and the electrodic steps. A detailed mechanistic scheme is obtained which reproduces well experimental data and opens perspectives for the general treatment of these processes.

Combined DFT and Kinetic Monte Carlo Study of UiO-66 Catalysts for γ-Valerolactone Production

Zr-based metal-organic frameworks (MOFs) are excellent heterogeneous porous catalysts due to their thermal stability. Their tunability via node and linker modifications makes them amenable for theoretical studies on catalyst design. However, detailed benchmarks on MOF-based reaction mechanisms combined with kinetics analysis are still scarce. Thus, we here evaluate different computational models and density functional theory (DFT) methods followed by kinetic Monte Carlo studies for a case reaction relevant in biomass upgrading, i.e., the conversion of methyl levulinate to γ-valerolactone catalyzed by UiO-66. We show the impact of cluster versus periodic models, the importance of the DF of choice, and the direct comparison to experimental data via simulated kinetics data. Overall, we found that Perdew-Burke-Ernzerhof (PBE), a widely employed method in plane-wave periodic calculations, greatly overestimates reaction rates, while M06 with cluster models better fits the available experimental data and is recommended whenever possible.

Automated MUltiscale simulation environment

Multiscale techniques integrating detailed atomistic information on materials and reactions to predict the performance of heterogeneous catalytic full-scale reactors have been suggested but lack seamless implementation. The largest challenges in the multiscale modeling of reactors can be grouped into two main categories: catalytic complexity and the difference between time and length scales of chemical and transport phenomena. Here we introduce the Automated MUltiscale Simulation Environment AMUSE, a workflow that starts from Density Functional Theory (DFT) data, automates the analysis of the reaction networks through graph theory, prepares it for microkinetic modeling, and subsequently integrates the results into a standard open-source Computational Fluid Dynamics (CFD) code. We demonstrate the capabilities of AMUSE by applying it to the unimolecular iso-propanol dehydrogenation reaction and then, increasing the complexity, to the pre-commercial Pd/In2O3 catalyst employed for the CO2 hydrogenation to methanol. The results show that AMUSE allows the computational investigation of heterogeneous catalytic reactions in a comprehensive way, providing essential information for catalyst design from the atomistic to the reactor scale level.

DFT Study of Imine-Exchange Reactions in Iron(II)-Coordinated Pincers

The imine bond is among the most applied motifs in dynamic covalent chemistry. Although its uses are varied and often involve coordination to a transition metal for stability, mechanistic studies on imine exchange reactions so far have not included metal coordination. Herein, we investigated the condensation and transimination reactions of an Fe2+ -coordinated diimine pyridine pincer, employing wB97XD/6-311G(2d,2p) DFT calculations in acetonitrile. We first experimentally confirmed that Fe2+ is strongly coordinated by these pincers, and is thus a justified model ion. When considering a four-membered ring-shaped transition state for proton transfers, the required activation energies for condensation and transimination reaction exceeded the values expected for reactions known to be spontaneous at room temperature. The nature of the incoming and exiting amines and the substituents on the para-position of the pincer had no effect on this. Replacing Fe2+ with Zn2+ or removing it altogether did not reduce it either. However, the addition of two ethylamine molecules lowered the energy barriers to be compatible with experiment (19.4 and 23.2 kcal/mol for condensation and transimination, respectively). Lastly, the energy barrier of condensation of a non-coordinated pincer was significantly higher than found for Fe2+ -coordinating pincers, underlining the catalyzing effect of metal coordination on imine exchange reactions.

Multi-Time-Scale Simulation of Complex Reactive Mixtures: How Do Polyoxometalates Form?

Understanding the dynamics of reactive mixtures still challenges both experiments and theory. A relevant example can be found in the chemistry of molecular metal-oxide nanoclusters, also known as polyoxometalates. The high number of species potentially involved, the interconnectivity of the reaction network, and the precise control of the pH and concentrations needed in the synthesis of such species make the theoretical/computational treatment of such processes cumbersome. This work addresses this issue relying on a unique combination of recently developed computational methods that tackle the construction, kinetic simulation, and analysis of complex chemical reaction networks. By using the Bell-Evans-Polanyi approximation for estimating activation energies, and an accurate and robust linear scaling for correcting the computed pKa values, we report herein multi-time-scale kinetic simulations for the self-assembly processes of polyoxotungstates that comprise 22 orders of magnitude, from tens of femtoseconds to months of reaction time. This very large time span was required to reproduce very fast processes such as the acid/base equilibria (at 10-12 s), relatively slow reactions such as the formation of key clusters such as the metatungstate (at 103 s), and the very slow assembly of the decatungstate (at 106 s). Analysis of the kinetic data and of the reaction network topology shed light onto the details of the main reaction mechanisms, which explains the origin of kinetic and thermodynamic control followed by the reaction. Simulations at alkaline pH fully reproduce experimental evidence since clusters do not form under those conditions.

Fluorine-based Zn salan complexes

We synthesised and characterised the racemic and chiral versions of two Zn salan fluorine-based complexes from commercially available materials. The complexes are susceptible to absorbing H₂O from the atmosphere. In solution (DMSO-H₂O) and at the millimolar level, experimental and theoretical studies identify that these complexes exist in a dimeric-monomeric equilibrium. We also investigated their ability to sense amines via¹⁹F NMR. In CDCl₃ or d⁶-DMSO, strongly coordinating molecules (H₂O or DMSO) are the limiting factor in using these easy-to-make complexes as chemosensory platforms since their exchange with analytes requires an extreme excess of the latter.

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.

Mechanism and selectivity of photocatalyzed CO2 reduction by a function-integrated Ru catalyst

The phosphine-substituted Ru(II) polypyridyl complex, [Ru^II-(tpy)(pqn)(MeCN)]²⁺ (RuP), was disclosed to be an efficient photocatalyst for the reduction of CO₂ to CO with excellent selectivity. In this work, density functional calculations were performed to elucidate the reaction mechanism and understand the origin of selectivity. The calculations showed that RuP was first excited to the singlet excited state, followed by intersystem crossing to produce a triplet species (3RuIII(L˙-)-S), which was then reduced by the sacrificial electron donor BIH to generate a Ru^II(L˙^-) intermediate. The ligand of Ru^II(L˙^-) was further reduced to produce a Ru^II(L^2-) intermediate. The redox non-innocent nature of the tpy and pqn ligands endows the Ru center with an oxidation state of +2 after two one-electron reductions. Ru^II(L^2-) nucleophilically attacks CO₂, in which two electrons are delivered from the ligands to CO₂, affording a Ru^II-COOH species after protonation. This is followed by the protonation of the hydroxyl moiety of Ru^II-COOH, coupled with the C-O bond cleavage, resulting in the formation of Ru^II-CO. Ultimately, CO is dissociated after two one-electron reductions. Protonation of Ru^II(L^2-) to generate a Ru^II-hydride, a critical intermediate for the production of formate and H₂, turns out to be kinetically less favorable, even though it is thermodynamically more favorable. This fact is due to the presence of a Ru²⁺ ion in the reduced catalyst, which disfavors its protonation.

Driving to a Better Understanding of Acyl Glucuronide Transformations Using NMR and Molecular Modeling

Acyl glucuronide (AG) metabolites of carboxylic acid-containing drugs and products of their transformations have long been implicated in drug-induced liver injury (DILI). To inform on the DILI risk arising from AG reactive intermediates, a comprehensive mechanistic study of enzyme-independent AG rearrangements using nuclear magnetic resonance (NMR) and density functional theory (DFT) was undertaken. NMR spectroscopy was utilized for structure elucidation and kinetics measurements of nine rearrangement and hydrolysis products of 1β-O-acyl glucuronide of ibufenac. To extract rate constants of rearrangement, mutarotation, and hydrolysis from kinetic data, 11 different kinetic models were examined. Model selection and estimated rate constant verification were supported by measurements of H/D kinetic isotope effects. DFT calculations of ground and transition states supported the proposed kinetic mechanisms and helped to explain the unusually fast intramolecular transacylation rates found for some of the intermediates. The findings of the current study reinforce the notion that the short half-life of parent AG and slow hydrolysis rates of AG rearrangement products are the two key factors that can influence the in vivo toxicity of AGs.

Seeking the Optimal Descriptor for SN2 Reactions through Statistical Analysis of Density Functional Theory Results

Bimolecular nucleophilic substitution is one of the fundamental reactions in organic chemistry, yet there is still knowledge to be gained on the role of the nucleophile and the substrate. A statistical treatment of over 600 density functional theory (DFT)-computed barriers for bimolecular nucleophilic substitution at methyl derivatives (S_N2@C) leads to the identification of numerical descriptors that best represent the entering and leaving ability of 26 different nucleophiles. The treatment is based on singular value decomposition (SVD) of a matrix of computed energy barriers. The current work represents the extension to a problem of reactivity of the hidden descriptor methodology that we had previously developed for the thermodynamic problem of bond dissociation energies in transition-metal complexes. The analysis of the results shows that a single descriptor is sufficient. This hidden descriptor has different values for nucleophilic and leaving abilities and, contrary to expectation, does not correlate especially well with either frontier molecular orbital descriptors or solvation descriptors. In contrast, it correlates with other thermodynamic and geometric parameters. This statistical procedure can be in principle extended to additional chemical fragments and other reactions.

Understanding Ball Milling Mechanochemical Processes with DFT Calculations and Microkinetic Modeling

Mechanochemistry is an emerging field with many potential applications in sustainable chemistry. But despite the growing interest in the field, its underlying mechanistic foundations are not fully understood yet. This work presents the application of computational tools, such as DFT calculations in continuum and microkinetic modeling, to the analysis of mechanically activated procedures. Two reactions reported in previous experimental publications were studied: (i) a series of Diels-Alder reactions and (ii) the synthesis of sulfonylguanidines. Calculations succeed in reproducing experimentally reported reaction times. The procedures were mostly standard, coupled with some sensitive choices in terms of starting concentrations and dielectric constant. This means that these particular reactions accelerated by ball milling followed the same mechanism as the equivalent reactions in solution. The implications of this result on the general picture of mechanochemical processes are discussed.

Chalcogen vs Halogen Bonding Catalysis in a Water-Bridge-Cocatalyzed Nitro-Michael Reaction

Recently, a tellurium-based chalcogen-bond-catalyzed nitro-Michael reaction was reported ( Angew. Chem. Int. Ed. 2019, 58, 16923), taking advantage of the strong Lewis acidity of the catalyst. This species was found to be more effective than an analogous iodine-based halogen bond organocatalyst. Herein, we present a detailed mechanistic and kinetic analysis of these catalytic cycles including the influence of the solvent (and the performance of different intrinsic solvation models). While the chalcogen bonding interaction is fundamental to activate the C-C bond formation, we found that the presence of a two-water molecular bridge is critical to allow the following, otherwise high-energy proton transfer step. Even though the iodine-based halogen bonding interaction is stronger than the tellurium-based chalcogen bonding one, which makes the former a stronger Lewis acid and hence in principle a more efficient catalyst, solvation effects explain the smaller energy span of the latter.

The key role of the latent N-H group in Milstein's catalyst for ester hydrogenation

We previously demonstrated that Milstein's seminal diethylamino-substituted PNN-pincer-ruthenium catalyst for ester hydrogenation is activated by dehydroalkylation of the pincer ligand, releasing ethane and eventually forming an NHEt-substituted derivative that we proposed is the active catalyst. In this paper, we present a computational and experimental mechanistic study supporting this hypothesis. Our DFT analysis shows that the minimum-energy pathways for hydrogen activation, ester hydrogenolysis, and aldehyde hydrogenation rely on the key involvement of the nascent N-H group. We have isolated and crystallographically characterized two catalytic intermediates, a ruthenium dihydride and a ruthenium hydridoalkoxide, the latter of which is the catalyst resting state. A detailed kinetic study shows that catalytic ester hydrogenation is first-order in ruthenium and hydrogen, shows saturation behavior in ester, and is inhibited by the product alcohol. A global fit of the kinetic data to a simplified model incorporating the hydridoalkoxide and dihydride intermediates and three kinetically relevant transition states showed excellent agreement with the results from DFT.

Two Copper-Carbenes from One Diazo Compound

Many transition-metal complexes ML_n decompose diazo compounds N₂═CR¹R² generating metal-carbenes L_nM═CR¹R² which transfer the carbene group to other substrates, constituting an important tool in organic synthesis. All previous reports have shown that the CR¹R² fragment at the metal-carbene remains intact from the parent diazo compound. Herein we report the detection and isolation of a monosubstituted copper carbene where the CR¹R² ligand has undergone a modification from the initial diazo reagent. When Tp^MsCu(THF) (Tp^Ms = hydrotris(3-mesityl)pyrazolylborate ligand) was reacted with N,N-diethyl diazoacetamide [N₂═C(H)(CONEt₂)], the stable copper carbene Tp^MsCu═C(H)(NEt₂) was isolated, resulting from a decarbonylation process, with carbon monoxide being trapped as Tp^MsCu(CO). The simultaneous observation of products derived from the intramolecular carbene insertion reaction into C-H bonds demonstrates that the expected Tp^MsCu═C(H)(CONEt₂) complex is also formed. Experimental data, DFT calculations, and microkinetic models allow us to propose that the latter undergoes CO loss en route to the former.

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

Accurate prediction of chemical reactions in solution is challenging for current state-of-the-art approaches based on transition state modelling with density functional theory. Models based on machine learning have emerged as a promising alternative to address these problems, but these models currently lack the precision to give crucial information on the magnitude of barrier heights, influence of solvents and catalysts and extent of regio- and chemoselectivity. Here, we construct hybrid models which combine the traditional transition state modelling and machine learning to accurately predict reaction barriers. We train a Gaussian Process Regression model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic substitution reaction and use it to predict barriers with a mean absolute error of 0.77 kcal mol-1 for an external test set. The model was further validated on regio- and chemoselectivity prediction on patent reaction data and achieved a competitive top-1 accuracy of 86%, despite not being trained explicitly for this task. Importantly, the model gives error bars for its predictions that can be used for risk assessment by the end user. Hybrid models emerge as the preferred alternative for accurate reaction prediction in the very common low-data situation where only 100-150 rate constants are available for a reaction class. With recent advances in deep learning for quickly predicting barriers and transition state geometries from density functional theory, we envision that hybrid models will soon become a standard alternative to complement current machine learning approaches based on ground-state physical organic descriptors or structural information such as molecular graphs or fingerprints.

Mechanism of an Organocatalytic Cope Rearrangement Involving Iminium Intermediates: Elucidating the Role of Catalyst Ring Size

The mechanism of the organocatalytic Cope rearrangement is elucidated through a combined computational and experimental approach. As reported previously, hydrazides catalyze the Cope rearrangement of 1,5-hexadiene-2-carboxaldehydes via iminium ion formation, and seven- and eight-membered ring catalysts are more active than smaller ring sizes. In the present work, quantum mechanical computations and kinetic isotope effect experiments demonstrate that the Cope rearrangement step, rather than iminium formation, is rate-limiting. The computations further explain how the hydrazide catalyst lowers the free-energy barrier of the Cope rearrangement via an associative transition state that is stabilized by enehydrazine character. The computations also explain the catalyst ring size effect, as larger hydrazide rings are able to accommodate optimal transition-state geometries that minimize the unfavorable lone-pair repulsion between neighboring nitrogen atoms and maximize the favorable hyperconjugative donation from each nitrogen atom into neighboring electron-poor sigma bonds, with the seven-membered catalyst achieving a nearly ideal transition-state geometry that is comparable to that of an unconstrained acyclic catalyst. Experimental kinetics studies support the computations, showing that the seven-membered and acyclic hydrazide catalysts react 10 times faster than the six-membered catalyst. Unraveling the mechanism of this reaction is an important step in understanding other reactions catalyzed by hydrazides, and explaining the ring size effect is critical because cyclic catalysts provide a constrained scaffold, enabling the development of asymmetric variants of these reactions.