Enantioselective hydrogenation of olefins by Rh-based chiral catalysts has been extensively studied for more than 50 years. Naively, one would expect that everything about this transformation is known and that selecting a catalyst that induces the desired reactivity or selectivity is a trivial task. Nonetheless, ligand engineering or selection for any new prochiral olefin remains an empirical trial-error exercise. In this study, we investigated whether machine learning techniques could be used to accelerate the identification of the most efficient chiral ligand. For this purpose, we used high throughput experimentation to build a large dataset consisting of results for Rh-catalyzed asymmetric olefin hydrogenation, specially designed for applications in machine learning. We showcased its alignment with existing literature while addressing observed discrepancies. Additionally, a computational framework for the automated and reproducible quantum-chemistry based featurization of catalyst structures was created. Together with less computationally demanding representations, these descriptors were fed into our machine learning pipeline for both out-of-domain and in-domain prediction tasks of selectivity and reactivity. For out-of-domain purposes, our models provided limited efficacy. It was found that even the most expensive descriptors do not impart significant meaning to the model predictions. The in-domain application, while partly successful for predictions of conversion, emphasizes the need for evaluating the cost-benefit ratio of computationally intensive descriptors and for tailored descriptor design. Challenges persist in predicting enantioselectivity, calling for caution in interpreting results from small datasets. Our insights underscore the importance of dataset diversity with broad substrate inclusion and suggest that mechanistic considerations could improve the accuracy of statistical models.

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The premise of most studies on the homogeneous electrocatalytic CO₂ reduction reaction (CO₂RR) is a good understanding of the reaction mechanisms. Yet, analyzing the reaction intermediates formed at the working electrode is challenging and not always attainable. Here, we present a new, general approach to studying the reaction intermediates applied for CO₂RR catalyzed by a series of cobalt complexes. The cobalt complexes were based on the TPA-ligands (TPA = tris(2-pyridylmethyl)amine) modified by amino groups in the secondary coordination sphere. By combining the electrochemical experiments, electrochemistry-coupled electrospray ionization mass spectrometry, with density functional theory (DFT) calculations, we identify and spectroscopically characterize the key reaction intermediates in the CO₂RR and the competing hydrogen-evolution reaction (HER). Additionally, the experiments revealed the rarely reported in situ changes in the secondary coordination sphere of the cobalt complexes by the CO₂-initiated transformation of the amino substituents to carbamates. This launched an even faster alternative HER pathway. The interplay of three catalytic cycles, as derived from the experiments and supported by the DFT calculations, explains the trends that cobalt complexes exhibit during the CO₂RR and HER. Additionally, this study demonstrates the need for a molecular perspective in the electrocatalytic activation of small molecules efficiently obtained by the EC-ESI-MS technique.

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Electrocatalytic reduction of organic halides and subsequent carboxylation are promising methods for the valorization of CO2 as a C1 source in synthetic organic chemistry. The reaction mechanism underlying the selectivity and reduction mechanism of benzyl halides is highly dependent on the nature of the electrode material as well as the processes, composition, and structure of the liquid phase at the electrode–solution interface. Herein, we present a computational study on the influence of the electric double layer (EDL) on the activation of benzyl halides at different applied potentials over the Au (111) cathode. Using a multiscale modeling approach, we demonstrate that, under realistic electrocatalytic conditions, the formation of a dense EDL over the cathode hampers the diffusion of benzyl halides toward the electrode surface. A combination of classical molecular dynamics simulations and density functional theory calculations reveals the most favorable benzyl halide electro-carboxylation pathway over the EDL that does not require direct substrate adsorption to the cathode surface. The dense EDL promotes the dissociative reduction of the benzyl halides via the outer-sphere electron transfer from the cathode surface to the electrolyte. Such a reduction mechanism results in a benzyl radical intermediate, which is then converted to benzyl anions in the EDL via an additional electron transfer step.@en

The current state-of-the-art electron-transfer modeling primarily focuses on the kinetics of charge transfer between an electroactive species and an inert electrode. Experimental studies have revealed that the existing Butler–Volmer model fails to satisfactorily replicate experimental voltammetry results for both solution-based and surface-bound redox couples. Consequently, experimentalists lack an accurate tool for predicting electron-transfer kinetics. In response to this challenge, we developed a density functional theory-based approach for accurately predicting current peak potentials by using the Marcus–Hush model. Through extensive cyclic voltammetry simulations, we conducted a thorough exploration that offers valuable insights for conducting well-informed studies in the field of electrochemistry.@en

Many catalytic reactions suffer from product inhibition, which especially hard to control in homogeneous hydrogenation due to the scaling relation between the inhibited and active states of the catalyst. We recently reported one such pathway in Mn(I) hydrogenation and demonstrated that addition of alkoxide bases could affect the thermodynamic favorability of this reaction and selectively suppress the product inhibition. Since external reaction promotors are formally not involved in reaction thermodynamics, we set to investigate the explicit molecular interactions behind these apparently environmental effects. Herein, we reveal that the thermodynamic landscape of the inhibitory process exhibits a non-monotonic dependence on the base concentration. This study related this phenomenon to the presence of two dominant mechanisms operating at different base concentrations. Specifically, the base additives can enhance the ionic strength and lower the free energy of the inhibited state at low promotor concentration. At high base concentrations this study suggested the formation of highly labile alcohol-alkoxide clusters which stabilize the free alcohol and make its addition to the catalyst unfavourable, thereby suppressing the inhibition. While relatively weak, such noncovalent interactions between reactants and reaction environment can cause substantial perturbations to the free energy of catalytic process, ultimately deciding its fate.@en

In the past decade, computational tools have become integral to catalyst design. They continue to offer significant support to experimental organic synthesis and catalysis researchers aiming for optimal reaction outcomes. More recently, data-driven approaches utilizing machine learning have garnered considerable attention for their expansive capabilities. This Perspective provides an overview of diverse initiatives in the realm of computational catalyst design and introduces our automated tools tailored for high-throughput in silico exploration of the chemical space. While valuable insights are gained through methods for high-throughput in silico exploration and analysis of chemical space, their degree of automation and modularity are key. We argue that the integration of data-driven, automated and modular workflows is key to enhancing homogeneous catalyst design on an unprecedented scale, contributing to the advancement of catalysis research.

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Computational chemistry pipelines typically commence with geometry generation, well-established for organic compounds but presenting a considerable challenge for transition metal complexes. This paper introduces MACE, an automated computational workflow for converting chemist SMILES/MOL representations of the ligands and the metal center to 3D coordinates for all feasible stereochemical configurations for mononuclear octahedral and square planar complexes directly suitable for quantum chemical computations and implementation in high-throughput computational chemistry workflows. The workflow is validated through a structural screening of a data set of transition metal complexes extracted from the Cambridge Structural Database. To further illustrate the power and capabilities of MACE, we present the results of a model DFT study on the hemilability of pincer ligands in Ru, Fe, and Mn complexes, which highlights the utility of the workflow for both focused mechanistic studies and larger-scale high-throughput pipelines.

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Data-driven catalyst design is a promising approach for addressing the challenges in identifying suitable catalysts for synthetic transformations. Models with descriptor calculations relying solely on the precatalyst structure are potentially generalizable but may overlook catalyst-substrate interactions. This study explores substrate-specific interactions in the context of Rh-catalyzed asymmetric hydrogenation to elucidate the impact of substrate inclusion on the catalyst structure and on the descriptors derived from it. We compare a catalyst-substrate complex with methyl 2-acetamidoacrylate as a model substrate with the generic precatalyst structure involving a placeholder substrate, norbornadiene, across 11 Rh-based catalysts with bidentate bisphosphine ligands. For these systems, a full conformer ensemble analysis reveals an intriguing finding: the rigid substrate induces conformational freedom in the ligand. This flexibility gives rise to a more diverse conformer landscape, showing a previously overlooked aspect of catalyst-substrate dynamics. Electronic descriptor variations particularly highlight differences between substrate-specific and precatalyst structures. This study suggests that generic precatalyst-like models may lack crucial insights into the conformational freedom of the catalyst. We speculate that such conformational freedom may be a more general phenomenon that can influence the development of generalizable predictive models of computational TM-based catalysis.

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Organic electrochemistry is currently experiencing an era of renaissance, which is closely related to the possibility of carrying out organic transformations under mild conditions, with high selectivity, high yields, and without the use of toxic solvents. Combination of organic electrochemistry with alternative approaches, such as photo-chemistry was found to have great potential due to induced synergy effects. In this work, we propose for the first time utilization of plasmon triggering of enhanced and regio-controlled organic chemical transformation performed in photoelectrochemical regime. The advantages of the proposed route is demonstrated in the model amination reaction with formation of C−N bond between pyrazole and substituted benzene derivatives. Amination was performed in photo-electrochemical mode on the surface of plasmon active Au@Pt electrode with attention focused on the impact of plasmon triggering on the reaction efficiency and regio-selectivity. The ability to enhance the reaction rate significantly and to tune products regio-selectivity is demonstrated. We also performed density functional theory calculations to inquire about the reaction mechanism and potentially explain the plasmon contribution to electrochemical reaction rate and regioselectivity.

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Herein, we present a practical strategy to enhance the performance of a multiphase system for bicarbonate hydrogenation based on a molecular Ru-PNP pincer catalyst. This study demonstrates that the use of organic antioxidants not only mitigates catalyst degradation but also significantly boosts its intrinsic activity. This enables efficient catalyst recycling at ultra-low concentrations. Systematic screening and optimization of a range of organic antioxidants has identified TDTBP (tris(2,4-di-tert-butylphenyl) phosphite) as being exceptionally efficient in stabilizing and enhancing the performance of the Ru-PNP catalyst. With the optimized system an unprecedented integral turnover frequency (TOF) of 115,000 h⁻¹ and a total turnover number (TTON) of 9.43×10⁶ across four recycling tests were demonstrated, conducted at a reaction temperature of 90 ºC and H₂ pressure of 50 bar. These findings represent a substantial advancement in sustainable formate/formic acid production, offering a scalable and highly efficient method suitable for industrial-scale application.

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Adding Zn to the ZSM-5 zeolite effectively increases the aromatic selectivity in the methanol-to-aromatics (MTA) process. The formation of metal-derived Lewis acid sites promotes the dehydrogenation but at the cost of a rapid deactivation of the catalyst by coke, due to the increased aromatic formation. In this work, we impregnated a Zn-modified catalyst (2 wt%) with variable contents of Ca (0.02 and 0.5 wt%) and evaluated their kinetic behavior in the MTA and ethane dehydrogenation reactions. The results proved the superior performance of the Zn(2)Ca(0.02) catalyst due to a synergistic effect between the two metals. The Ca ions limit coke formation from excessive aromatization, increasing catalyst stability and removing Zn clusters, resulting in a recovery of Brønsted acid sites (BAS) active for the formation of light aromatics. Combining these effects results in a more efficient and viable catalyst for aromatic production from methanol.@en

Some of our recent developments and applications of algorithmic graph theory for extracting the physical and chemical properties of materials from molecular dynamics simulations are presented. From the chemical viewpoint, the power of graph theory is illustrated in the search for a catalyst's active sites at a silica solid surface. From the physical viewpoint, we present graph algorithms that recognize the structural motifs that exist at the silica/liquid water interface. Statistical analyses of the instances of these surface–water motifs provide a detailed understanding of the structures and dynamics at the aqueous interface.@en

Deep eutectic solvents (DESs) represent an environmentally friendly alternative to conventional organic solvents. Their liquid range determines the areas of application, and therefore, the prediction of solid-liquid equilibrium (SLE) diagrams is essential for developing new DESs. Such predictions are not yet possible by using the current state-of-the-art computational models. Herein, we present an alternative model based on support vector regression integrating experimental data, a conductor-like screening model for real solvents simulations, and cheminformatic descriptors for predicting melting temperatures of binary metal-free DESs or ionic liquids, allowing the researcher to estimate the eutectic formation and SLE for specific combinations of components. The model was developed based on the manually collected database of 1648 mixture melting temperatures for 237 experimentally described DESs, and its accuracy was demonstrated by 5-fold cross-validation (R² ∼ 0.8). The presented machine learning methodology empowers researchers to predefine the liquid range of the mixture and holds promise for efficient molecular combination screening, facilitating the discovery of tailored DESs for desired applications from catalysis and extraction to energy storage. By enabling a deeper understanding of DES behavior and the targeted design of these solvents, the proposed approach contributes to advancing green chemistry practices and to promoting sustainable solvent usage.

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The nature of hydrocarbon pool (HCP) intermediates in the methanol-to-hydrocarbons (MTH) process has been thoroughly investigated, especially for BEA- and CHA-type zeolite catalysts like H-β and H-SAPO-34. Herein, we further reveal the dynamic mechanistic details of the MTH process over the H-ZSM-5 catalyst at 400 °C, based on the dual-cycle mechanism and HCP in this medium-pore zeolite. Application of switching sequences of ¹³C-labeled and unlabeled methanol pulses over a model H-ZSM-5 catalyst combined with on-line MS analysis and a recently reported technique called “fast scanning-pulse GC analysis” provides a direct and quantitative insight into the MTH reactions under quasi-steady-state conditions. The transient product responses showed the almost instant formation of hydrocarbons upon a small pulse of methanol, followed by secondary formation of light aromatics via HCP decomposition and olefin alkylation-dealkylation, especially in a long catalyst bed when methanol is quickly consumed in the initial reaction zone in the catalyst bed. The isotopic analysis of typical aliphatic C₃₊ product responses after switching ¹³C-methanol pulses to the unlabeled methanol pulses showed a fast isotope scrambling in the formation of C₃₊ species. MS analysis of the light aromatics indicates a complete consecutive but slower isotope incorporation process of ¹²C into ¹³C-aromatics. Results provide direct experimental confirmation of the kinetically preferred olefin-based cycle over the aromatic-based cycle. The sequential isotopic incorporation strongly suggests that the paring reaction pathway through aromatic ring contraction and re-expansion steps is operative. In the appearance of aromatics upon pulsing methanol over larger catalyst beds, four processes are directly discerned, involving the displacement of adsorbed species by formed water, isotope incorporation yielding directly labeled and unlabeled products through the paring mechanism and direct aromatization, and HCP conversion through secondary reactions.

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Rational plastic recycling is critical for addressing the environmental challenges associated with plastic waste. Among the various recycling methods, chemical recycling, particularly via homogeneous catalysis, holds promise for converting plastic waste into valuable products. Post-consumer polymer wastes could present a challenge for catalytic upcycling due to the structural inhomogeneity and functionalization of the polyolefin chains. The impact of substrate aging on the performance of the upcycling catalyst can be viewed as an “inverse problem” of heterogeneous catalysis and has not received sufficient attention in mechanistic studies on this subject. Herein, we present a density functional theory study on the dehydrogenative upcycling of polyethylene (PE) with different in-chain impurities, representing the chemistry of post-consumption PE wastes. We selected the (^tBu4POCOP)-Ir pincer complex catalyzed dehydrogenation of PE as our model reaction. The calculations reveal that common in-chain impurities found in PE, such as carbonyl, hydroxyl, epoxides, and chlorine atoms, inhibit the overall catalyst performance. These impurities form stable molecular complexes with the catalyst, leading to a substantial increase in the energy barriers of the initial reaction step, the C-H bond addition. We also observe that the reaction on the ideal crystalline PE is also impeded. However, highly distorted PE chains exhibit greater susceptibility toward the (^tBu4POCOP)-Ir catalyst. Our mechanistic studies demonstrated that the reaction on the side alkane chains is kinetically favorable compared with the reaction on the PE backbone. The study highlights the critical role of in-chain heterogenieties in the catalytic activation of polymer chains and provides valuable insights into the development of effective technologies for upcycling plastic waste.

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Monitoring complex catalytic pathways under industrially-relevant conditions is one of the key challenges in catalysis chemistry and technology. Herewith we describe a direct technique called ‘fast scanning-pulse analysis’ (FASPA) that allows the direct characterization and detailed kinetic analysis of intimately interweaved catalytic pathways. The power and potential of the FASPA approach are demonstrated with an industrially relevant methanol-to-hydrocarbons (MTH) process over H-ZSM-5 zeolite. This reaction proceeds via a hydrocarbon pool (HCP) mechanism producing olefins and aromatics. The HCP is built-up upon exposure to methanol during the induction period, followed by a transition regime to a quasi steady-state MTH operation. This FASPA technique allows (sub-)second resolution of the full temporal products response upon a methanol pulse providing direct and quantitative insights into the MTH reactions. Globally, two consecutive pathways can be discerned: a very fast primary product formation in the presence of methanol in a narrow active MTH reaction zone, followed by a slower formation of light aromatics, which is closely related to the decomposition and release of HCP species and secondary reactions in absence of methanol in the downstream part of the catalyst bed. The time delay between the appearance of inert tracer and primary products represents the time needed to build-up the HCP in the induction period, where methane is observed prior to other products. The primary products (alkanes, olefins, and light aromatics) are nearly instantaneously formed from the pulsed methanol. These results demonstrate the highly dynamic character of the HCP in the MTH process over H-ZSM-5.

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The molecular-sized void space of the zeolitic micropores is perfect matrices to encapsulate and stabilize multicomponent and multifunctional complexes that can be used as active sites for a wide range of important catalytic transformations. In this article, we discuss and analyze the key developments of the last decade in the catalytic chemistry of metal-containing nanoclusters confined in zeolite micropores. We will present a concise summary of the recent developments in the tailored synthesis strategies, the advanced in-situ and operando characterization techniques, the enhanced performances of zeolite stabilized nanoclusters in various catalytic processes, and the application of computational modeling approaches for addressing the puzzle of catalyst-reactivity relationships. The article will be concluded with a brief discussion on the perspective for future developments anticipated for this field.

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Effective assessment of catalytic performance is the foundation for the rational design and development of new catalysts with superior performance. The ubiquitous screening/optimization studies use reaction yields as the sole performance metric in an approach that often neglects the complexity of the catalytic system and intrinsic reactivities of the catalysts. Using an example of hydrogenation catalysis, we examine the transient behavior of catalysts that are often encountered in activation, deactivation and catalytic turnover processes. Each of these processes and the reaction environment in which they take place are gradually shown to determine the real-time catalyst speciation and the resulting kinetics of the overall catalytic reaction. As a result, the catalyst performance becomes a complex and time-dependent metric defined by multiple descriptors apart from the reaction yield. This behaviour is not limited to hydrogenation catalysis and affects various catalytic transformations. In this feature article, we discuss these catalytically relevant descriptors in an attempt to arrive at a comprehensive depiction of catalytic performance.

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A method is introduced for the automated analysis of reactivity exploration for extended in silico databases of transition-metal catalysts. The proposed workflow is designed to tackle two key challenges for bias-free mechanistic explorations on large databases of catalysts: (1) automated exploration of the chemical space around each catalyst with unique structural and chemical features and (2) automated analysis of the resulting large chemical data sets. To address these challenges, we have extended the application of our previously developed ReNeGate method for bias-free reactivity exploration and implemented an automated analysis procedure to identify the classes of reactivity patterns within specific catalyst groups. Our procedure applied to an extended series of representative Mn(I) pincer complexes revealed correlations between structural and reactive features, pointing to new channels for catalyst transformation under the reaction conditions. Such an automated high-throughput virtual screening of systematically generated hypothetical catalyst data sets opens new opportunities for the design of high-performance catalysts as well as an accelerated method for expert bias-free high-throughput in silico reactivity exploration.

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Recently there has been growing interest in implementing the high-throughput approach to access the dynamics of chemical processes across different fields. With an ever-increasing amount of data provided by high-throughput experimentation, the development of fully-integrated workflows becomes crucial. These workflows should combine novel experimental tools and interpretation methods to convert the data into valuable information. To design feasible data-driven workflows, it is necessary to estimate the value of information and balance it with the number of experiments and resources required. Basing this kind of workflow on actual physical models appears to be a more feasible strategy as compared to data-extensive empirical statistical methods. Here we show an algorithm that constructs and evaluates kinetic models of different complexity. The algorithm facilitates the evaluation of the experimental data quality and quantity
requirements needed for the reliable discovery of the rates driving the corresponding chemical models. The influence of the quality and quantity of data on the obtained results was indicated by the accuracy of the estimates of the kinetic parameters. We also show that this method can be used to find correct reaction scenarios directly from simulated kinetic data with little to no overfitting. Well-fitting models for theoretical data can then be used as a proxy for optimizing the underlying chemical systems. Taking real physical effects into account, this approach goes beyond: we show that with the kinetic models, one can make a direct, unbiased, quantitative connection between kinetic data and the reaction scenario.@en