Microkinetic Assessment of Ligand-Exchanging Catalytic Cycles

Computational chemistry has become a fundamental part of the understanding and optimization of catalytic processes. Among these, the characterization of homogeneous organometallic catalysts, combining an active transition metal atom and set of ligands, is one of the main fields of application of these kinds of studies. More recently, microkinetic studies have been employed to bridge the gap between experimental measurements such as conversion or selectivity and the Gibbs free energies gathered by computations. In this work, we have developed an automated framework (MicroKatc) for microkinetic analysis, to tackle the yet understudied effect of ligand exchange processes that modify the nature of the catalytic scaffold in situ. We report the application of such a framework to the rhodium-catalyzed hydroformylation of ethylene, confirming the acceleration of the reaction as trimethylphosphine (PMe3) displaces the carbonyl ligands in the catalyst by means of simulations at variable phosphine concentrations, as well as the determination of the degree of rate control (DRC) and apparent activation energies throughout the catalytic process.

LASP to the Future of Atomic Simulation: Intelligence and Automation

Atomic simulations aim to understand and predict complex physical phenomena, the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events. LASP software (large-scale atomic simulation with a Neural Network Potential), released in 2018, incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods. This review introduces the recent development of the software along two main streams, namely, higher intelligence and more automation, to solve complex material and reaction problems. The latest version of LASP (LASP 3.7) features the global many-body function corrected neural network (G-MBNN) to improve the PES accuracy with low cost, which achieves a linear scaling efficiency for large-scale atomic simulations. The key functionalities of LASP are updated to incorporate (i) the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions; (ii) the ML-TS and MMLPS methods to identify the lowest energy reaction pathway. With these powerful functionalities, LASP now serves as an intelligent data generator to create computational databases for end users. We exemplify the recent LASP database construction in zeolite and the metal-ligand properties for a new catalyst design.

Ichor: A Python library for computational chemistry data management and machine learning force field development

We present ichor, an open-source Python library that simplifies data management in computational chemistry and streamlines machine learning force field development. Ichor implements many easily extensible file management tools, in addition to a lazy file reading system, allowing efficient management of hundreds of thousands of computational chemistry files. Data from calculations can be readily stored into databases for easy sharing and post-processing. Raw data can be directly processed by ichor to create machine learning-ready datasets. In addition to powerful data-related capabilities, ichor provides interfaces to popular workload management software employed by High Performance Computing clusters, making for effortless submission of thousands of separate calculations with only a single line of Python code. Furthermore, a simple-to-use command line interface has been implemented through a series of menu systems to further increase accessibility and efficiency of common important ichor tasks. Finally, ichor implements general tools for visualization and analysis of datasets and tools for measuring machine-learning model quality both on test set data and in simulations. With the current functionalities, ichor can serve as an end-to-end data procurement, data management, and analysis solution for machine-learning force-field development.

Data-driven discovery of active phosphine ligand space for cross-coupling reactions

The design of highly active catalysts is a main theme in organic chemistry, but it still relies heavily on expert experience. Herein, powered by machine-learning global structure exploration, we forge a Metal-Phosphine Catalyst Database (MPCD) with a meticulously designed ligand replacement energy metric, a key descriptor to describe the metal-ligand interactions. It pushes the rational design of organometallic catalysts to a quantitative era, where a ±10 kJ mol-1 window of relative ligand binding strength, a so-called active ligand space (ALS), is identified for highly effective catalyst screening. We highlight the chemistry interpretability and effectiveness of ALS for various C-N, C-C and C-S cross-coupling reactions via a Sabatier-principle-based volcano plot and demonstrate its predictive power in discovering low-cost ligands in catalyzing Suzuki cross-coupling involving aryl chloride. The advent of the MPCD provides a data-driven new route for speeding up organometallic catalysis and other applications.

PyDFT-QMMM: A modular, extensible software framework for DFT-based QM/MM molecular dynamics

PyDFT-QMMM is a Python-based package for performing hybrid quantum mechanics/molecular mechanics (QM/MM) simulations at the density functional level of theory. The program is designed to treat short-range and long-range interactions through user-specified combinations of electrostatic and mechanical embedding procedures within periodic simulation domains, providing necessary interfaces to external quantum chemistry and molecular dynamics software. To enable direct embedding of long-range electrostatics in periodic systems, we have derived and implemented force terms for our previously described QM/MM/PME approach [Pederson and McDaniel, J. Chem. Phys. 156, 174105 (2022)]. Communication with external software packages Psi4 and OpenMM is facilitated through Python application programming interfaces (APIs). The core library contains basic utilities for running QM/MM molecular dynamics simulations, and plug-in entry-points are provided for users to implement custom energy/force calculation and integration routines, within an extensible architecture. The user interacts with PyDFT-QMMM primarily through its Python API, allowing for complex workflow development with Python scripting, for example, interfacing with PLUMED for free energy simulations. We provide benchmarks of forces and energy conservation for the QM/MM/PME and alternative QM/MM electrostatic embedding approaches. We further demonstrate a simple example use case for water solute in a water solvent system, for which radial distribution functions are computed from 100 ps QM/MM simulations; in this example, we highlight how the solvation structure is sensitive to different basis-set choices due to under- or over-polarization of the QM water molecule's electron density.

Advances and Prospects in Understanding London Dispersion Interactions in Molecular Chemistry

London dispersion (LD) interactions are the main contribution of the attractive part of the van der Waals potential. Even though LD effects are the driving force for molecular aggregation and recognition, the role of these omnipresent interactions in structure and reactivity had been largely underappreciated over decades. However, in the recent years considerable efforts have been made to thoroughly study LD interactions and their potential as a chemical design element for structures and catalysis. This was made possible through a fruitful interplay of theory and experiment. This review highlights recent results and advances in utilizing LD interactions as a structural motif to understand and utilize intra- and intermolecularly LD-stabilized systems. Additionally, we focus on the quantification of LD interactions and their fundamental role in chemical reactions.

%VBur index and steric maps: from predictive catalysis to machine learning

Steric indices are parameters used in chemistry to describe the spatial arrangement of atoms or groups of atoms in molecules. They are important in determining the reactivity, stability, and physical properties of chemical compounds. One commonly used steric index is the steric hindrance, which refers to the obstruction or hindrance of movement in a molecule caused by bulky substituents or functional groups. Steric hindrance can affect the reactivity of a molecule by altering the accessibility of its reactive sites and influencing the geometry of its transition states. Notably, the Tolman cone angle and %VBur are prominent among these indices. Actually, steric effects can also be described using the concept of steric bulk, which refers to the space occupied by a molecule or functional group. Steric bulk can affect the solubility, melting point, boiling point, and viscosity of a substance. Even though electronic indices are more widely used, they have certain drawbacks that might shift preferences towards others. They present a higher computational cost, and often, the weight of electronics in correlation with chemical properties, e.g. binding energies, falls short in comparison to %VBur. However, it is worth noting that this may be because the steric index inherently captures part of the electronic content. Overall, steric indices play an important role in understanding the behaviour of chemical compounds and can be used to predict their reactivity, stability, and physical properties. Predictive chemistry is an approach to chemical research that uses computational methods to anticipate the properties and behaviour of these compounds and reactions, facilitating the design of new compounds and reactivities. Within this domain, predictive catalysis specifically targets the prediction of the performance and behaviour of catalysts. Ultimately, the goal is to identify new catalysts with optimal properties, leading to chemical processes that are both more efficient and sustainable. In this framework, %VBur can be a key metric for deepening our understanding of catalysis, emphasizing predictive catalysis and sustainability. Those latter concepts are needed to direct our efforts toward identifying the optimal catalyst for any reaction, minimizing waste, and reducing experimental efforts while maximizing the efficacy of the computational methods.

An easy-to-perform evaluation of steric properties of Lewis acids

Steric and electronic effects play a very important role in chemistry, as these effects influence the shape and reactivity of molecules. Herein, an easy-to-perform approach to assess and quantify steric properties of Lewis acids with differently substituted Lewis acidic centers is reported. This model applies the concept of the percent buried volume (%V _Bur) to fluoride adducts of Lewis acids, as many fluoride adducts are crystallographically characterized and are frequently calculated to judge fluoride ion affinities (FIAs). Thus, data such as cartesian coordinates are often easily available. A list of 240 Lewis acids together with topographic steric maps and cartesian coordinates of an oriented molecule suitable for the SambVca 2.1 web application is provided, together with different FIA values taken from the literature. Diagrams of %V _Bur as a scale for steric demand vs. FIA as a scale for Lewis acidity provide valuable information about stereo-electronic properties of Lewis acids and an excellent evaluation of steric and electronic features of the Lewis acid under consideration. Furthermore, a novel LAB-Rep model (Lewis acid/base repulsion model) is introduced, which judges steric repulsion in Lewis acid/base pairs and helps to predict if an arbitrary pair of Lewis acid and Lewis base can form an adduct with respect to their steric properties. The reliability of this model was evaluated in four selected case studies, which demonstrate the versatility of this model. For this purpose, a user-friendly Excel spreadsheet was developed and is provided in the ESI, which works with listed buried volumes of Lewis acids %V _{Bur_LA} and of Lewis bases %V _{Bur_LB}, and no results from experimental crystal structures or quantum chemical calculations are necessary to evaluate steric repulsion in these Lewis acid/base pairs.

DFT-based QM/MM with Particle-Mesh Ewald for Direct, Long-Range Electrostatic Embedding

We present a DFT-based, QM/MM implementation with long-range electrostatic embedding achieved by direct real-space integration of the particle mesh Ewald (PME) computed electrostatic potential. The key transformation is the interpolation of the electrostatic potential from the PME grid to the DFT quadrature grid, from which integrals are easily evaluated utilizing standard DFT machinery. We provide benchmarks of the numerical accuracy with choice of grid size and real-space corrections, and demonstrate that good convergence is achieved while introducing nominal computational overhead. Furthermore, the approach requires only small modification to existing software packages, as is demonstrated with our implementation in the OpenMM and Psi4 software. After presenting convergence benchmarks, we evaluate the importance of long-range electrostatic embedding in three solute/solvent systems modeled with QM/MM. Water and BMIM/BF4 ionic liquid were considered as ``simple' and ``complex' solvents respectively, with water and p-phenylenediamine (PPD) solute molecules treated at QM level of theory. While electrostatic embedding with standard real-space truncation may introduce negligible error for simple systems such as water solute in water solvent, errors become more significant when QM/MM is applied to complex solvents such as ionic liquids. An extreme example is the electrostatic embedding energy for oxidized PPD in BMIM/BF4 for which real-space truncation produces severe error even at 2-3 nm cutoff distances. This latter example illustrates that utilization of QM/MM to compute redox potentials within concentrated electrolytes/ionic media requires carefully chosen long-range electrostatic embedding algorithms, with our presented algorithm providing a general and robust approach.

Exploring steric and electronic parameters of biaryl phosphacycles

Steric and electronic parameters of the newly developed biaryl phosphacycles derived from the phobane[3.3.1] (Phob) and phosphatrioxa-adamantane (Cg) moieties were quantified from various experimental and theoretical methods.

Organometallic chemistry and application of palladacycles in asymmetric hydrophosphination reactions

A number of palladacycles containing chiral chelating auxiliaries have been utilized as efficient catalysts for asymmetric hydrophosphination reactions. In all cases, the chiral auxiliaries remained coordinated to the palladium centres throughout the course of the reactions. Despite the presence of a large quantity of powerful tertiary phosphines, which are known to be strong metal ion sequesters, the expected catalyst poisoning was rarely observed in these palladacycle catalyzed processes. This review highlights the unique stereoelectronic features and the important organometallic chemistry of palladacycle catalysts which are essential to their synthetic operations.

Trends in computational molecular catalyst design

Computational methods have emerged as a powerful tool to augment traditional experimental molecular catalyst design by providing useful predictions of catalyst performance and decreasing the time needed for catalyst screening. In this perspective, we discuss three approaches for computational molecular catalyst design: (i) the reaction mechanism-based approach that calculates all relevant elementary steps, finds the rate and selectivity determining steps, and ultimately makes predictions on catalyst performance based on kinetic analysis, (ii) the descriptor-based approach where physical/chemical considerations are used to find molecular properties as predictors of catalyst performance, and (iii) the data-driven approach where statistical analysis as well as machine learning (ML) methods are used to obtain relationships between available data/features and catalyst performance. Following an introduction to these approaches, we cover their strengths and weaknesses and highlight some recent key applications. Furthermore, we present an outlook on how the currently applied approaches may evolve in the near future by addressing how recent developments in building automated computational workflows and implementing advanced ML models hold promise for reducing human workload, eliminating human bias, and speeding up computational catalyst design at the same time. Finally, we provide our viewpoint on how some of the challenges associated with the up-and-coming approaches driven by automation and ML may be resolved.

Building a Toolbox for the Analysis and Prediction of Ligand and Catalyst Effects in Organometallic Catalysis

Computers have become closely involved with most aspects of modern life, and these developments are tracked in the chemical sciences. Recent years have seen the integration of computing across chemical research, made possible by investment in equipment, software development, improved networking between researchers, and rapid growth in the application of predictive approaches to chemistry, but also a change of attitude rooted in the successes of computational chemistry-it is now entirely possible to complete research projects where computation and synthesis are cooperative and integrated, and work in synergy to achieve better insights and improved results. It remains our ambition to put computational prediction before experiment, and we have been working toward developing the key ingredients and workflows to achieve this.The ability to precisely tune selectivity along with high catalyst activity make organometallic catalysts using transition metal (TM) centers ideal for high-value-added transformations, and this can make them appealing for industrial applications. However, mechanistic variations of TM-catalyzed reactions across the vast chemical space of different catalysts and substrates are not fully explored, and such an exploration is not feasible with current resources. This can lead to complete synthetic failures when new substrates are used, but more commonly we see outcomes that require further optimization, such as incomplete conversion, insufficient selectivity, or the appearance of unwanted side products. These processes consume time and resources, but the insights and data generated are usually not tied to a broader predictive workflow where experiments test hypotheses quantitatively, reducing their impact.These failures suggest at least a partial deviation of the reaction pathway from that hypothesized, hinting at quite complex mechanistic manifolds for organometallic catalysts that are affected by the combination of input variables. Mechanistic deviation is most likely when challenging multifunctional substrates are being used, and the quest for so-called privileged catalysts is quickly replaced by a need to screen catalyst libraries until a new "best" match between the catalyst and substrate can be identified and the reaction conditions can be optimized. As a community we remain confined to broad interpretations of the substrate scope of new catalysts and focus on small changes based on idealized catalytic cycles rather than working toward a "big data" view of organometallic homogeneous catalysis with routine use of predictive models and transparent data sharing.Databases of DFT-calculated steric and electronic descriptors can be built for such catalysts, and we summarize here how these can be used in the mapping, interpretation, and prediction of catalyst properties and reactivities. Our motivation is to make these databases useful as tools for synthetic chemists so that they challenge and validate quantitative computational approaches. In this Account, we demonstrate their application to different aspects of catalyst design and discovery and their integration with computational mechanistic studies and thus describe the progress of our journey toward truly predictive models in homogeneous organometallic catalysis.

Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next

Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from in silico libraries of catalyst structures.

Development of a Computer-Guided Workflow for Catalyst Optimization. Descriptor Validation, Subset Selection, and Training Set Analysis

Modern, enantioselective catalyst development is driven largely by empiricism. Although this approach has fostered the introduction of most of the existing synthetic methods, it is inherently limited by the skill, creativity, and chemical intuition of the practitioner. Herein, we present a complementary approach to catalyst optimization in which statistical methods are used at each stage to streamline development. To construct the optimization informatics workflow, a number of critical components had to be subjected to rigorous validation. First, the critically important molecular descriptors were validated in two case studies to establish the importance of conformation-dependent molecular representations. Next, with a large data set available, it was possible to investigate the amount of data necessary to make predictive models with different modeling methods. Given the commercial availability of many catalyst structures, it was possible to compare models generated with algorithmically selected training sets and commercially available training sets. Finally, the augmentation of limited data sets is demonstrated in a method informed by unsupervised learning to restore the accuracy of the generated models.

Substituent Effect Parameters: Extending the Applications to Organometallic Chemistry

Typically, metal complexes are constituted of an acceptor metal ion and one or more Iigands containing the donor atoms. Accordingly, the properties of a metal complex are equally dependent on the nature of the metal ion and the ligands. Minute structural variations in the ligand will may result in linear changes in the respective energetic parameters and such linear relationships have paramount importance in organometallic chemistry. The variation in ligands is virtually limitless and substantial because of the extent of organic chemistry available for the modelling of desirable ligands, apart from the variation in metal ions. Anyhow, there is still a need for new parameters for the design and quantification of new ligands which in turn leads to the synthesis of metal complexes with possibly predictable chemical properties. Previous studies have demonstrated that quantum chemically derived molecular electrostatic potential (MESP) parameters can be listed as one of the superior quantifiers in this regard, which can act as an effective ligand electronic parameter. The interaction between the ligand part and metal-containing part will be crucial in assessing the reactivity of organometallic complexes. Here we are applying MESP based substituent constants derived from substituted benzenes to forecast the interaction energies in (pyr^* )W(CO)₅ , (NHC^* )Mo(CO)₅ and (η⁶ -arene^* )Cr(CO)₃ complexes. Ligands and metal ions are varied in each case for better understanding and transparency.

Quantitative Structure-Selectivity Relationships in Enantioselective Catalysis: Past, Present, and Future

The dawn of the 21st century has brought with it a surge of research related to computer-guided approaches to catalyst design. In the past two decades, chemoinformatics, the application of informatics to solve problems in chemistry, has increasingly influenced prediction of activity and mechanistic investigations of organic reactions. The advent of advanced statistical and machine learning methods, as well as dramatic increases in computational speed and memory, has contributed to this emerging field of study. This review summarizes strategies to employ quantitative structure-selectivity relationships (QSSR) in asymmetric catalytic reactions. The coverage is structured by initially introducing the basic features of these methods. Subsequent topics are discussed according to increasing complexity of molecular representations. As the most applied subfield of QSSR in enantioselective catalysis, the application of local parametrization approaches and linear free energy relationships (LFERs) along with multivariate modeling techniques is described first. This section is followed by a description of global parametrization methods, the first of which is continuous chirality measures (CCM) because it is a single parameter derived from the global structure of a molecule. Chirality codes, global, multivariate descriptors, are then introduced followed by molecular interaction fields (MIFs), a global descriptor class that typically has the highest dimensionality. To highlight the current reach of QSSR in enantioselective transformations, a comprehensive collection of examples is presented. When combined with traditional experimental approaches, chemoinformatics holds great promise to predict new catalyst structures, rationalize mechanistic behavior, and profoundly change the way chemists discover and optimize reactions.

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

Evaluating continuous chirality measure as a 3D descriptor in chemoinformatics applied to asymmetric catalysis

Continuous Chirality Measure (CCM) is a computational metric by which to quantify the chirality of a compound. In enantioselective catalysis, prior work has postulated that CCM is correlated to selectivity and can be used to understand which structural features dictate catalyst efficacy. Herein, the investigation of CCM as a metric capable of guiding catalyst optimization is explored. Conformer-dependent CCM is also explored. Finally, CCM is used with Sterimol parameters to significantly improve the performance of Random Forest models.

Computational Ligand Descriptors for Catalyst Design

Ligands, especially phosphines and carbenes, can play a key role in modifying and controlling homogeneous organometallic catalysts, and they often provide a convenient approach to fine-tuning the performance of known catalysts. The measurable outcomes of such catalyst modifications (yields, rates, selectivity) can be set into context by establishing their relationship to steric and electronic descriptors of ligand properties, and such models can guide the discovery, optimization, and design of catalysts. In this review we present a survey of calculated ligand descriptors, with a particular focus on homogeneous organometallic catalysis. A range of different approaches to calculating steric and electronic parameters are set out and compared, and we have collected descriptors for a range of representative ligand sets, including 30 monodentate phosphorus(III) donor ligands, 23 bidentate P,P-donor ligands, and 30 carbenes, with a view to providing a useful resource for analysis to practitioners. In addition, several case studies of applications of such descriptors, covering both maps and models, have been reviewed, illustrating how descriptor-led studies of catalysis can inform experiments and highlighting good practice for model comparison and evaluation.

Computational assessment on the Tolman cone angles for P-ligands

P-ligand cone angles have been properly recomputed in different transition-metal coordination linear, tetrahedral and octahedral environments by combining molecular mechanics and DFT methodologies.

Hydrogen elimination reactivity of ruthenium pincer hydride complexes: a DFT study

The pincer effect is explained for various pincer hydride complexes, differing in the donor atoms, using activation barriers, and MESP parameters.

Computational chemistry strategies in natural product synthesis

The synthesis of natural products increasingly uses computational chemistry approaches to model and understand molecular phenomena. Calculations are employed to rationalize reaction outcomes, predict how a new system will perform, and inform synthetic design. As a result, new insights into the interactions of fundamental chemical forces have emerged that advance the field of complex small molecule synthesis. This review presents ten examples of computational techniques used in the synthesis of natural products, and discusses the unique perspectives afforded by these quantitative analyses.

Interpretation of Tolman electronic parameters in the light of natural orbitals for chemical valence

Understanding the nature and the strength of metal-ligand interactions in d- and f-block metal complexes has always been a central issue for both synthetic and theoretical chemists. These interactions are usually described according to the well accepted Dewar-Chatt-Duncanson model, and thus over the years numerous research groups directed their efforts to shed light on the role of σ- and π-contributions. Among others, the electronic parameter introduced by Tolman in the 1970s represents a milestone in this field. Herein we present a quantitative description of the nickel-phosphine bond in Tolman's nickel(0) carbonyl complexes. The combination of Natural Orbitals for Chemical Valence with Energy Decomposition Analysis resulted in the definition of a new parameter (T^phos) which comprises all the energetic contributions needed to describe the nickel-phosphine bond and thus stands as a reliable descriptor of the electronic properties of phosphines. Moreover, steric effects of phosphines (i.e. Tolman's cone angles) have been considered too, and a linear relation including Ni-P bond distances, T^phos and cone angle has been found.

Ligand-Substrate Dispersion Facilitates the Copper-Catalyzed Hydroamination of Unactivated Olefins

The current understanding of ligand effects in transition metal catalysis is mostly based on the analysis of catalyst-substrate through-bond and through-space interactions, with the latter commonly considered to be repulsive in nature. The dispersion interaction between the ligand and the substrate, a ubiquitous type of attractive noncovalent interaction, is seldom accounted for in the context of transition-metal-catalyzed transformations. Herein we report a computational model to quantitatively analyze the effects of different types of catalyst-substrate interactions on reactivity. Using this model, we show that in the copper(I) hydride (CuH)-catalyzed hydroamination of unactivated olefins, the substantially enhanced reactivity of copper catalysts based on bulky bidentate phosphine ligands originates from the attractive ligand-substrate dispersion interaction. These computational findings are validated by kinetic studies across a range of hydroamination reactions using structurally diverse phosphine ligands, revealing the critical role of bulky P-aryl groups in facilitating this process.

On the relation between carbonyl stretching frequencies and the donor power of chelating diphosphines in nickel dicarbonyl complexes

The relation between spectroscopic observables and the detailed metal-ligand bonding features in chelation complexes is addressed using both experimental and state-of-the-art theoretical and computational methods. We synthesized and characterized a set of six nickel dicarbonyl complexes of general formula [Ni(CO)2(PP)], where PP is an atropoisomeric chelating diphosphine ligand. The analysis of the obtained experimental data and the basicity and oxidative potentials of the free ligands suggests a close relation between the donor ability of the chelating ligand and the carbonyl stretching frequencies observed in the complexes. We then use theory to unravel the detailed mechanisms of chelation-bond formation in terms of partial charge flows between the molecular orbitals of the fragments. By extending the promising, recently published natural orbitals for chemical valence/charge displacement (NOCV/CD) analysis scheme we provide a thorough, quantitative description of the several charge fluxes following the metal-ligand bond formation and demonstrate that the carbonyl stretching frequencies in the considered complexes selectively respond to the σ-donation charge flow from the phosphorus lone pairs of the ligands, with the frequency shift being in quantitative correlation with the extent of the ligand-to-metal charge transfer.

Theoretical investigation on donor–acceptor interaction between a carbonyl compound and an N,N′-dioxide–Sc(iii) complex

The counterion and substituent on amide of the N,N′-dioxide ligand could affect electrostatic energy (ΔV_elstat) as well as orbital energy (ΔE_orb) between CH₂O and Sc(iii)-based catalyst, adjusting the Lewis acidity of the metal centre.

Theoretical and experimental studies on the structure–property relationship of chiral N,N′-dioxide–metal catalysts probed by the carbonyl–ene reaction of isatin

Variation of the linkage or chiral backbone of an N,N′-dioxide ligand adjusts the blocking effect of ortho-iPr on the reaction site, affecting the enantiodifferentiation of two competing pathways.

Catalytic applications of small bite-angle diphosphorus ligands with single-atom linkers

Diphosphorus ligands connected by a single atom (R₂PEPR₂; E = CR₂, CCR₂and NR) give chelating ligands with very small bite-angles as well as enable access to other properties such as bridging modes and hemilability. ThisPerspectivereviews the properties of diphosphorus ligands featuring a single-atom linker and their applications in catalysis, including transformations of alkenes and transfer hydrogenation and hydrogen-borrowing reactions.

Suzuki-Miyaura cross-coupling optimization enabled by automated feedback

An automated, droplet-flow microfluidic system explores and optimizes Pd-catalyzed Suzuki-Miyaura cross-coupling reactions. A smart optimal DoE-based algorithm is implemented to increase the turnover number and yield of the catalytic system considering both discrete variables-palladacycle and ligand-and continuous variables-temperature, time, and loading-simultaneously. The use of feedback allows for experiments to be run with catalysts and under conditions more likely to produce an optimum; consequently complex reaction optimizations are completed within 96 experiments. Response surfaces predicting reaction performance near the optima are generated and validated. From the screening results, shared attributes of successful precatalysts are identified, leading to improved understanding of the influence of ligand selection upon transmetalation and oxidative addition in the reaction mechanism. Dialkylbiarylphosphine, trialkylphosphine, and bidentate ligands are assessed.

Lost in chemical space? Maps to support organometallic catalysis

Descriptors calculated from molecular structures have been used to map different areas of chemical space. A number of applications for such maps can be identified, ranging from the fine-tuning and optimisation of catalytic activity and compound properties to virtual screening of novel compounds, as well as the exhaustive exploration of large areas of chemical space by automated combinatorial building and evaluation. This review focuses on organometallic catalysis, but also touches on other areas where similar approaches have been used, with a view to assessing the extent to which chemical space has been explored.

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