Computational Transition-State Design Provides Experimentally Verified Cr(P,N) Catalysts for Control of Ethylene Trimerization and Tetramerization

Paving the road towards automated homogeneous catalyst design

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.

ReaLigands: A Ligand Library Cultivated from Experiment and Intended for Molecular Computational Catalyst Design

Computational catalyst design requires identification of a metal and ligand that together result in the desired reaction reactivity and/or selectivity. A major impediment to translating computational designs to experiments is evaluating ligands that are likely to be synthesized. Here, we provide a solution to this impediment with our ReaLigands library that contains >30,000 monodentate, bidentate (didentate), tridentate, and larger ligands cultivated by dismantling experimentally reported crystal structures. Individual ligands from mononuclear crystal structures were identified using a modified depth-first search algorithm and charge was assigned using a machine learning model based on quantum-chemical calculated features. In the library, ligands are sorted based on direct ligand-to-metal atomic connections and on denticity. Representative principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) analyses were used to analyze several tridentate ligand categories, which revealed both the diversity of ligands and connections between ligand categories. We also demonstrated the utility of this library by implementing it with our building and optimization tools, which resulted in the very rapid generation of barriers for 750 bidentate ligands for Rh-hydride ethylene migratory insertion.

Cr/PCCP-Catalysed Selective Ethylene Oligomerization: Analysis of Various Conformations and the Hemilabile Methoxy Group

In this work, the effect of the hemilabile methoxy group in the Cr-based catalyst bearing (C6H5)2-P(CH2)2P-(C6H5)2 (PCCP) and (o-MeOC6H4)(C6H5)-P(CH2)2P-(C6H5)(o-MeOC6H4) (PCCPOMe) ligands on ethylene tri- and tetramerization were systematically investigated by...

Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior of the metal-organic bond, while very tunable for achieving target properties, is challenging to predict and necessitates searching a wide and complex space to identify needles in haystacks for target applications. This review will focus on the techniques that make high-throughput search of transition-metal chemical space feasible for the discovery of complexes with desirable properties. The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e., force field, semiempirical, and density functional theory methods) as it pertains to data generation for inorganic molecular discovery. The review will also discuss the opportunities and limitations in leveraging experimental data sources. We will focus on how advances in statistical modeling, artificial intelligence, multiobjective optimization, and automation accelerate discovery of lead compounds and design rules. The overall objective of this review is to showcase how bringing together advances from diverse areas of computational chemistry and computer science have enabled the rapid uncovering of structure-property relationships in transition-metal chemistry. We aim to highlight how unique considerations in motifs of metal-organic bonding (e.g., variable spin and oxidation state, and bonding strength/nature) set them and their discovery apart from more commonly considered organic molecules. We will also highlight how uncertainty and relative data scarcity in transition-metal chemistry motivate specific developments in machine learning representations, model training, and in computational chemistry. Finally, we will conclude with an outlook of areas of opportunity for the accelerated discovery of transition-metal complexes.

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.

Reducing Challenges in Organic Synthesis with Stereoselective Hydrogenation and Tandem Catalysis

Tandem catalysis enables the rapid construction of complex architectures from simple building blocks. This Perspective shares our interest in combining stereoselective hydrogenation with transformations such as isomerization, oxidation, and epimerization to solve diverse challenges. We highlight the use of tandem hydrogenation for preparing complex natural products from simple prochiral building blocks and present tandem catalysis involving transfer hydrogenation and dynamic kinetic resolution. Finally, we underline recent breakthroughs and opportunities for asymmetric hydrogenation.

Ethylene oligomerisation chromium catalysts with unsymmetrical PCNP ligands

Chromium(iii) complexes of chelating diphosphines, with PNP or PCNCP backbones, are excellent catalysts for ethylene tetra- and/or trimerisations. A missing link within this ligand series are unsymmetric chelating diphosphines based on a PCNP scaffold. New bidentate PCNP ligands of the type Ph₂PCH₂N(R)PPh₂ (R = 1-naphthyl or 5-quinoline groups, 2a-d) have been synthesised and shown to be extremely effective ligands for ethylene tri-/tetramerisations. Three representative tetracarbonyl Cr⁰ complexes bearing a single PN(R)P (5), PCN(R)P (6), or PCN(R)CP (7) diphosphine (R = 1-naphthyl) have been prepared from Cr(CO)₄(η⁴-nbd) (nbd = norbornadiene). Furthermore we report a single crystal X-ray diffraction study of these compounds and discuss their structural parameters.

Diastereoselective Synthesis of Functionalized 5-Amino-3,4-Dihydro-2H-Pyrrole-2-Carboxylic Acid Esters: One-Pot Approach Using Commercially Available Compounds and Benign Solvents

A novel three-step four-transformation approach to highly functionalized 5-amino-3,4-dihydro-2H-pyrrole-2-carboxylic acid esters, starting from commercially available phenylsulfonylacetonitrile, aldehydes, and N-(diphenylmethylene)glycine tert-butyl ester, was developed. The one-pot strategy delivered this class of amidines bearing, for the first time, three contiguous stereocenters, in good to high yield and diastereoselectivity. The entire sequence was carried out using diethyl carbonate and 2-methyl tetrahydrofuran as benign solvents, operating under metal-free conditions. The process could be conveniently scaled-up, and the synthetic utility of the products was demonstrated.

Computer-assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands

Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring significant researcher time to implement. We automate this workflow using all open-source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious effects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save significant experimental effort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔG_PPN), and establish a strong correlation between ΔG_PPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving significant time by ruling out candidates with non-trivial synthetic routes and poor expected catalytic performance.

Effect of an additional donor on decene formation in ethylene oligomerization catalyzed by a Cr/PCCP system: a combined experimental and DFT study

Cr catalyst based on a PCCP ligand shows high activity in ethylene oligomerization, giving 1-hexene and considerable C₁₀ fraction. DFT calculation results are consistent with the experimental observations on the distribution of C₁₀ isomers.

Chromium complexes supported by bidentate thioether-imine [N,S] ligands: synthesis and ethylene oligomerization studies

Chromium complexes bearing thioether-imine ligands were synthesized and their catalytic behavior in ethylene oligomerization has been investigated, evaluating the effect of the ligand and the experimental parameters on the activity, selectivity, and product distribution.

Quantum-mechanical transition-state model combined with machine learning provides catalyst design features for selective Cr olefin oligomerization

The use of data science tools to provide the emergence of non-trivial chemical features for catalyst design is an important goal in catalysis science. Additionally, there is currently no general strategy for computational homogeneous, molecular catalyst design. Here, we report the unique combination of an experimentally verified DFT-transition-state model with a random forest machine learning model in a campaign to design new molecular Cr phosphine imine (Cr(P,N)) catalysts for selective ethylene oligomerization, specifically to increase 1-octene selectivity. This involved the calculation of 1-hexene : 1-octene transition-state selectivity for 105 (P,N) ligands and the harvesting of 14 descriptors, which were then used to build a random forest regression model. This model showed the emergence of several key design features, such as Cr-N distance, Cr-α distance, and Cr distance out of pocket, which were then used to rapidly design a new generation of Cr(P,N) catalyst ligands that are predicted to give >95% selectivity for 1-octene.

Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex

A machine learning exploration of the chemical space surrounding Vaska's complex.

Homogeneous catalysis using transition metal complexes is ubiquitously used for organic synthesis, as well as technologically relevant in applications such as water splitting and CO₂ reduction. The key steps underlying homogeneous catalysis require a specific combination of electronic and steric effects from the ligands bound to the metal center. Finding the optimal combination of ligands is a challenging task due to the exceedingly large number of possibilities and the non-trivial ligand–ligand interactions. The classic example of Vaska's complex, trans-[Ir(PPh₃)₂(CO)(Cl)], illustrates this scenario. The ligands of this species activate iridium for the oxidative addition of hydrogen, yielding the dihydride cis-[Ir(H)₂(PPh₃)₂(CO)(Cl)] complex. Despite the simplicity of this system, thousands of derivatives can be formulated for the activation of H₂, with a limited number of ligands belonging to the same general categories found in the original complex. In this work, we show how DFT and machine learning (ML) methods can be combined to enable the prediction of reactivity within large chemical spaces containing thousands of complexes. In a space of 2574 species derived from Vaska's complex, data from DFT calculations are used to train and test ML models that predict the H₂-activation barrier. In contrast to experiments and calculations requiring several days to be completed, the ML models were trained and used on a laptop on a time-scale of minutes. As a first approach, we combined Bayesian-optimized artificial neural networks (ANN) with features derived from autocorrelation and deltametric functions. The resulting ANNs achieved high accuracies, with mean absolute errors (MAE) between 1 and 2 kcal mol^–1, depending on the size of the training set. By using a Gaussian process (GP) model trained with a set of selected features, including fingerprints, accuracy was further enhanced. Remarkably, this GP model minimized the MAE below 1 kcal mol^–1, by using only 20% or less of the data available for training. The gradient boosting (GB) method was also used to assess the relevance of the features, which was used for both feature selection and model interpretation purposes. Features accounting for chemical composition, atom size and electronegativity were found to be the most determinant in the predictions. Further, the ligand fragments with the strongest influence on the H₂-activation barrier were identified.

Computational Insights on the Electrocatalytic Behavior of [Cp*Rh] Molecular Catalysts Immobilized on Graphene for Heterogeneous Hydrogen Evolution Reaction

The heterogeneous metal-based molecular electrocatalyst can typically exhibit attractive features compared to its homogeneous analogue including recoverability and durability. As such, it is necessary to evaluate the electrocatalytic behavior of heterogenized molecular catalysts of interest toward gaining insights concerning the retainability of such behaviors while benefiting from heterogenization. In this work, we examined computationally the electrochemical properties of nanographene-based heterogenized molecular complexes of Rhodium. We assessed, as well, the electrocatalytic behavior of the heterogenized molecular catalyst for hydrogen evolution reaction (HER). Two electrochemical pathways were examined, namely one- and two-electron electrochemical reduction pathways. Interestingly, it is computationally demonstrated that [Rh^III(Cp*)(phen)Cl]⁺-Gr can exhibit redox and electrocatalytic properties for HER that are comparable to its homogeneous analogue via a two-electron reduction pathway. On the other hand, the one-electron reduction pathway is notably found to be less favorable kinetically and thermodynamically. Furthermore, molecular insights are provided with respect to the HER employing molecular orbitals analyses and mechanistic aspects. Importantly, our findings may provide insights toward designing more efficient graphene-based molecular heterogeneous electrocatalysts for more efficient energy production.

Mechanistically Guided Predictive Models for Ligand and Initiator Effects in Copper-Catalyzed Atom Transfer Radical Polymerization (Cu-ATRP)

Copper-catalyzed atom transfer radical polymerization (Cu-ATRP) is one of the most widely used controlled radical polymerization techniques. Notwithstanding the extensive mechanistic studies in the literature, the transition states of the activation/deactivation of the growing polymer chain, a key equilibrium in Cu-ATRP, have not been investigated computationally. Therefore, the understanding of the origin of ligand and initiator effects on the rates of activation/deactivation is still limited. Here, we present the first computational analysis of Cu-ATRP activation transition states to reveal factors that affect the rates of activation and deactivation. The Br atom transfer between the polymer chain and the Cu catalyst occurs through an unusual bent geometry that involves pronounced interactions between the polymer chain end and the ancillary ligand on the Cu catalyst. Therefore, the rates of activation/deactivation are determined by both the electronic properties of the Cu catalyst and the ligand-initiator steric repulsions. In addition, our calculations revealed the important role of ligand backbone flexibility on the activation. These theoretical analyses led to the identification of three chemically meaningful descriptors, namely HOMO energy of the catalyst ( EHOMO), percent buried volume ( Vbur%), and distortion energy of the catalyst (Δ Edist), to describe the electronic, steric, and flexibility effects on reactivity, respectively. A robust and simple predictive model for ligand effect on reactivity is thereby established by correlating these three descriptors with experimental activation rate constants using multivariate linear regression. Validation using a structurally diverse set of ligands revealed the average error is less than ±2 kcal/mol compared to the experimentally derived activation energies. The same approach was also applied to develop a predictive model for reactivity of different alkyl halide initiators using R-X bond dissociation energy (BDE) and Cu-X halogenophilicity as descriptors.

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.

Synthesis and characterization of chromium complexes 2-Me4CpC6H4CH2(R)NHCrCl2 and their catalytic properties in ethylene homo- and co-polymerization

A series of new half-sandwich secondary amine-coordinated dichlorochromium complexes chelated by 2-(tetramethylcyclopentadienyl)benzylamine ligands, 2-Me4CpC6H4CH2(R)NHCrCl2 [R = iPr (1), Cy (2), Ph (3), 4-MePh (4), 2,6-Me2Ph (5), 2,6-Et2Ph (6)], have been synthesized from the reactions of CrCl3(THF)3 with the dilithium salts of the corresponding ligands in THF, followed by the addition of 1/2 eq. of H2O to the reaction mixtures. The isolated yields of the chromium complexes were found to increase with the increase in the amount of H2O introduced and reach the highest values (66-76%) when 1/2 eq. of H2O is added. Attempts to isolate the 2-(tetramethylcyclopentadienyl)benzylamidochromium complexes, 2-Me4CpC6H4CH2(R)NCrCl, were not successful. The new dichlorochromium complexes were characterized by IR, 1H NMR, EPR, and UV/Vis spectroscopy and elemental analyses, and the molecular structures of complexes 1, 5 and 6 were determined by X-ray crystallography. The X-ray crystallographic analysis reveals that these chromium complexes possess a three-legged piano-stool geometry with the amine N atom in a mitered six-membered chelating ring and the two chloride atoms as the legs. Upon activation with AlR3 and Ph3CB(C6F5)4, complexes 1-6 exhibit reasonable catalytic activity for ethylene polymerization and copolymerization with 1-hexene, producing polyethylenes with moderate to high molecular weights and poly(ethylene-co-1-hexene)s with moderate comonomer incorporation which are typical linear low-density polyethylenes (LLDPE). Complex 4 was found to show higher catalytic activity for ethylene homo- and co-polymerization than other complexes under similar conditions, while complex 3 produced poly(ethylene-co-1-hexene)s with the highest comonomer incorporation.

Isotopic labelling in ethylene oligomerization: addressing the issue of 1-octene vs. 1-hexene selectivity

The selectivity-determining mechanistic steps of ethylene tetramerization and trimerization are evaluated in light of isotopic labeling experiments. A mechanism based upon a shared chromacycloheptane intermediate rather than the C-C coupling of chromacyclopentanes or Cr speciation into independent trimerization and tetramerization catalysts is consistent with the data, including observed upper limits on 1-octene selectivity.

Mechanistically Guided Design of Ligands That Significantly Improve the Efficiency of CuH-Catalyzed Hydroamination Reactions

Using a mechanically guided ligand design approach, a new ligand (SEGFAST) for the CuH-catalyzed hydroamination reaction of unactivated terminal olefins has been developed, providing a 62-fold rate increase over reactions compared to DTBM-SEGPHOS, the previous optimal ligand. Combining the respective strengths of computational chemistry and experimental kinetic measurements, we were able to quickly identify potential modifications that lead to more effective ligands, thus avoiding synthesizing and testing a large library of ligands. By optimizing the combination of attractive, noncovalent ligand-substrate interactions and the stability of the catalyst under the reaction conditions, we were able to identify a finely tuned hybrid ligand that greatly enables accelerated hydrocupration rates with unactivated alkenes. Moreover, a modular and robust synthetic sequence was devised, which allowed for the practical, gram-scale synthesis of these novel hybrid ligand structures.