3D-QSAR as a Tool for Understanding and Improving Single-Site Polymerization Catalysts. A Review

A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis

Machine learning has permeated all fields of research, including chemistry, and is now an integral part of the design of novel compounds with desired properties. In the field of asymmetric catalysis, the preference still lies with models based on a physical understanding of the catalysis phenomenon and the electronic and steric properties of catalysts. However, such models require quantum chemical calculations and are thus limited by their computational cost. Here, we highlight the recent advances in modeling catalyst selectivity by using the 2D structures of catalysts and substrates. While these have a less explicit mechanistic connection to the modeled property, 2D descriptors, such as topological indices, molecular fingerprints, and fragments, offer the tremendous advantages of low cost and high speed of calculations. This makes them optimal for the in-silico screening of large amounts of data. We provide an overview of common quantitative structure-property relationship workflow, model building and validation techniques, applications of these methodologies in asymmetric catalysis design, and an outlook on improving the understanding of 2D-based models.

Bayesian-optimization-assisted discovery of stereoselective aluminum complexes for ring-opening polymerization of racemic lactide

Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%V_bur) and the highest occupied molecular orbital energy (E_HOMO), that can access quantitative and predictive models for catalyst development.

Predicting Highly Enantioselective Catalysts Using Tunable Fragment Descriptors

Catalyst optimization processes typically rely on inductive and qualitative assumptions of chemists based on screening data. While machine learning models using molecular properties or calculated 3D structures enable quantitative data evaluation, costly quantum chemical calculations are often required. In contrast, readily available binary fingerprint descriptors are time- and cost-efficient, but their predictive performance remains insufficient. Here, we describe a machine learning model based on fragment descriptors, which are fine-tuned for asymmetric catalysis and represent cyclic or polyaromatic hydrocarbons, enabling robust and efficient virtual screening. Using training data with only moderate selectivities, we designed theoretically and validated experimentally new catalysts showing higher selectivities in a challenging asymmetric tetrahydropyran synthesis.

Machine Learning for the Discovery, Design, and Engineering of Materials

Machine learning (ML) has become a part of the fabric of high-throughput screening and computational discovery of materials. Despite its increasingly central role, challenges remain in fully realizing the promise of ML. This is especially true for the practical acceleration of the engineering of robust materials and the development of design strategies that surpass trial and error or high-throughput screening alone. Depending on the quantity being predicted and the experimental data available, ML can either outperform physics-based modes, be used to accelerate such models, or be integrated with them to improve their performance. We cover recent advances in algorithms and in their application that are starting to make inroads toward (a) the discovery of new materials through large-scale enumerative screening, (b) the design of materials through identification of rules and principles that govern materials properties, and (c) the engineering of practical materials by satisfying multiple objectives. We conclude with opportunities for further advancement to realize ML as a widespread tool for practical computational materials design. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering, Volume 13 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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.

Anti-chronic myeloid leukemia activity and quantitative structure-activity relationship of novel thiazole aminobenzamide derivatives

The anti-chronic myeloid leukemia activity of thiazole aminobenzamide derivatives in vitro was tested by a methanethiosulfonate (MTS)-based viability assay method, and the result showed that some compounds exhibited good inhibitory activities against human chronic myeloid leukemia cell line K562, imatinib-resistant strain K562/R and T135I mutant cell line BaF₃-ABL-BCR-T315I. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) methods were used to analyze the relationship between the structure of thiazole aminobenzamide derivatives and the inhibition of K562/R cell activity. In CoMFA, Q² was 0.899 and R² was 0.963; in CoMSIA, Q² and R² were 0.840 and 0.903, respectively. These data indicated that the selected test set showed suitable external predictive ability. Combined with the contour map results, we further analyzed the three-dimensional quantitative structure (3D-QSAR) model. The results demonstrated that in the backbone of the thiazole aminobenzamide derivative, the substitution of a small group at R¹ position, or the introduction of a hydrophilic group at R² position, or the introduction of a large-volume amino acid at R³ position may be beneficial to improve the anti-CML activity of the compound.

Functional and Material Properties in Nanocatalyst Design: A Data Handling and Sharing Problem

(1) Background: Properties and descriptors are two forms of molecular in silico representations. Properties can be further divided into functional, e.g., catalyst or drug activity, and material, e.g., X-ray crystal data. Millions of real measured functional property records are available for drugs or drug candidates in online databases. In contrast, there is not a single database that registers a real conversion, TON or TOF data for catalysts. All of the data are molecular descriptors or material properties, which are mainly of a calculation origin. (2) Results: Here, we explain the reason for this. We reviewed the data handling and sharing problems in the design and discovery of catalyst candidates particularly, material informatics and catalyst design, structural coding, data collection and validation, infrastructure for catalyst design and the online databases for catalyst design. (3) Conclusions: Material design requires a property prediction step. This can only be achieved based on the registered real property measurement. In reality, in catalyst design and discovery, we can observe either a severe functional property deficit or even property famine.

Chain Transfer to Solvent and Monomer in Early Transition Metal Catalyzed Olefin Polymerization: Mechanisms and Implications for Catalysis

Even after several decades of intense research, mechanistic studies of olefin polymerization by early transition metal catalysts continue to reveal unexpected elementary reaction steps. In this mini-review, the recent discovery of two unprecedented chain termination processes is summarized: chain transfer to solvent (CTS) and chain transfer to monomer (CTM), leading to benzyl/tolyl and allyl type chain ends, respectively. Although similar transfer reactions are well-known in radical polymerization, only very recently they have been observed also in olefin insertion polymerization catalysis. In the latter context, these processes were first identified in Ti-catalyzed propene and ethene polymerization; more recently, CTS was also reported in Sc-catalyzed styrene polymerization. In the Ti case, these processes represent a unique combination of insertion polymerization, organic radical chemistry and reactivity of a M(IV)/M(III) redox couple. In the Sc case, CTS occurs via a σ-bond metathesis reactivity, and it is associated with a significant boost of catalytic activity and/or with tuning of polystyrene molecular weight and tacticity. The mechanistic studies that led to the understanding of these chain transfer reactions are summarized, highlighting their relevance in olefin polymerization catalysis and beyond.

Design and prediction of high potent ansa-zirconocene catalyst for olefin polymerizations: combined DFT calculations and QSPR approach

Density functional calculations were carried out to predict activities, regio- and stereoselectivity, and to design new ansa-zirconocene catalysts for olefin polymerizations.

tmQM Dataset-Quantum Geometries and Properties of 86k Transition Metal Complexes

We report the transition metal quantum mechanics (tmQM) data set, which contains the geometries and properties of a large transition metal-organic compound space. tmQM comprises 86,665 mononuclear complexes extracted from the Cambridge Structural Database, including Werner, bioinorganic, and organometallic complexes based on a large variety of organic ligands and 30 transition metals (the 3d, 4d, and 5d from groups 3 to 12). All complexes are closed-shell, with a formal charge in the range {+1, 0, -1}e. The tmQM data set provides the Cartesian coordinates of all metal complexes optimized at the GFN2-xTB level, and their molecular size, stoichiometry, and metal node degree. The quantum properties were computed at the DFT(TPSSh-D3BJ/def2-SVP) level and include the electronic and dispersion energies, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, HOMO/LUMO gap, dipole moment, and natural charge of the metal center; GFN2-xTB polarizabilities are also provided. Pairwise representations showed the low correlation between these properties, providing nearly continuous maps with unusual regions of the chemical space, for example, complexes combining large polarizabilities with wide HOMO/LUMO gaps and complexes combining low-energy HOMO orbitals with electron-rich metal centers. The tmQM data set can be exploited in the data-driven discovery of new metal complexes, including predictive models based on machine learning. These models may have a strong impact on the fields in which transition metal chemistry plays a key role, for example, catalysis, organic synthesis, and materials science. tmQM is an open data set that can be downloaded free of charge from https://github.com/bbskjelstad/tmqm.

Machine learning dihydrogen activation in 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.

Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning

This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst.

An Integrated High Throughput Experimentation/Predictive QSAR Modeling Approach to ansa-Zirconocene Catalysts for Isotactic Polypropylene

Compared to heterogenous Ziegler-Natta systems (ZNS), ansa-metallocene catalysts for the industrial production of isotactic polypropylene feature a higher cost-to-performance balance. In particular, the C2-symmetric bis(indenyl) ansa-zirconocenes disclosed in the 1990s are complex to prepare, less stereo- and/or regioselective than ZNS, and lose performance at practical application temperatures. The golden era of these complexes, though, was before High Throughput Experimentation (HTE) could contribute significantly to their evolution. Herein, we illustrate a Quantitative Structure - Activity Relationship (QSAR) model trained on a robust and highly accurate HTE database. The clear-box QSAR model utilizes, in particular, a limited number of chemically intuitive 3D geometric descriptors that screen various regions of space in and around the catalytic pocket in a modular way thus enabling to quantify individual substituent contributions. The main focus of the paper is on the methodology, which should be of rather broad applicability in molecular organometallic catalysis. Then again, it is worth emphasizing that the specific application reported here led us to identify in a comparatively short time novel zirconocene catalysts rivaling or even outperforming all previous homologues which strongly indicates that the metallocene story is not over yet.

Machine Learning in Catalysis, From Proposal to Practicing

Recently, machine learning (ML) methods have gained popularity and have performed as powerfully predictive tools in various areas of academic and industrious activities. In comparison, their application in catalysis has been underdeveloped. Relying on the rapid development of different algorithms and their implementation, it is the right timing to harvest the potential of ML in catalysis across academy and industry spectra. Herein, we discuss the current applications in the field of homogeneous and heterogeneous catalysis by using various ML approaches. To the best of our knowledge, modern statistical learning techniques will be a strong tool for computational optimization and discovery. This in turn will accurately extract the underlying mechanism in the model that converts readily available data and precatalysts into their promising and useful ones.

On the limits of tuning comonomer affinity of ‘Spaleck-type’ ansa-zirconocenes in ethene/1-hexene copolymerization: a high-throughput experimentation/QSAR approach

A change in rate-limiting step imparts a natural limit for comonomer affinity of C₂-symmetric zirconocenes.

Theoretical QSAR modelling and molecular docking studies of some 4-hydroxyphenylpyruvate dioxygenase (HPPD) enzyme inhibitors potentially used as herbicides

Computational QSAR studies together with molecular docking calculations have been performed on 118 different derivatives of organic molecules potentially used as herbicides. The Becke's three parameter exchange functional (B3) hybrid with Lee, Yang and Parr correlation functional (LYP), termed as B3LYP hybrid function and 6-31G* basis set (B3LYP/6-31G*) were used to develop five models of QSAR using the GFA technique. Models 1, was preferred as the best model because it possesses certain statistical implications (Friedman LOF = 0.52567, R2 = 0.9034, Radjst2= 0.8943, QCV2= 0.87 98 and Rpred.2= 0.8403).” The prepared model was validated internally and externally using training and test inhibitors. The molecular docking studies conducted in this study has actually outline the binding affinities of the 10 selected compounds (5, 25, 26, 27, 29, 35, 52, 55, 98 and 114) which were all in good correlation with their pIC₅₀ values. The binding affinities of the 10 selected compounds range between -5.9 kcal/mol to -10.1 kcal/mol. The compounds 25 and 27 with binding affinities of -10.1 kcal/mol and -9.7 kcal/mol formed the most stable complexes with the receptor (HPPD) as compared to other inhibitors. The complexes of these inhibitors show two most important types of bonding; Hydrogen bonding and hydrophobic bond interaction with the target amino acid residues. The computational QSAR study together with the molecular docking has actually provided a valuable approach for agrochemical researchers in synthesizing and developing new herbicides with high potency against the target enzyme.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

Recent transformative advances in computing power and algorithms have made computational chemistry central to the discovery and design of new molecules and materials. First-principles simulations are increasingly accurate and applicable to large systems with the speed needed for high-throughput computational screening. Despite these strides, the combinatorial challenges associated with the vastness of chemical space mean that more than just fast and accurate computational tools are needed for accelerated chemical discovery. In transition-metal chemistry and catalysis, unique challenges arise. The variable spin, oxidation state, and coordination environments favored by elements with well-localized d or f electrons provide great opportunity for tailoring properties in catalytic or functional (e.g., magnetic) materials but also add layers of uncertainty to any design strategy. We outline five key mandates for realizing computationally driven accelerated discovery in inorganic chemistry: (i) fully automated simulation of new compounds, (ii) knowledge of prediction sensitivity or accuracy, (iii) faster-than-fast property prediction methods, (iv) maps for rapid chemical space traversal, and (v) a means to reveal design rules on the kilocompound scale. Through case studies in open-shell transition-metal chemistry, we describe how advances in methodology and software in each of these areas bring about new chemical insights. We conclude with our outlook on the next steps in this process toward realizing fully autonomous discovery in inorganic chemistry using computational chemistry.

Catalytic performance of bis(imino)pyridine Fe/Co complexes toward ethylene polymerization by 2D-/3D-QSPR modeling

The two-dimensional and three-dimensional quantitative structure-property relationship (2D- and 3D-QSPR) approaches are applied to investigate the catalytic performance for a total data set of 55 bis(imino)pryridine iron and cobalt complexes, including the catalytic activity, molecular weight, and melting temperature of the product. The obtained models for the catalytic performance of interest exhibit good results by both 2D- and 3D-QSPR modeling, meanwhile higher predictive and validation powers observed in the 3D type. The modeling results indicate that the bulky substituents on ortho-position of the singular side phenyl ring and positive charge on para-position of the phenyl ring within the ligand are favorable to catalytic activity, while unfavorable to the molecular weight of product. Based on the obtained QSPR models, four new complexes are designed and predicted with good catalytic activity and very high molecular weight, which are in good agreement with our recent experimental report. © 2019 Wiley Periodicals, Inc.

Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning

Catalyst design in asymmetric reaction development has traditionally been driven by empiricism, wherein experimentalists attempt to qualitatively recognize structural patterns to improve selectivity. Machine learning algorithms and chemoinformatics can potentially accelerate this process by recognizing otherwise inscrutable patterns in large datasets. Herein we report a computationally guided workflow for chiral catalyst selection using chemoinformatics at every stage of development. Robust molecular descriptors that are agnostic to the catalyst scaffold allow for selection of a universal training set on the basis of steric and electronic properties. This set can be used to train machine learning methods to make highly accurate predictive models over a broad range of selectivity space. Using support vector machines and deep feed-forward neural networks, we demonstrate accurate predictive modeling in the chiral phosphoric acid-catalyzed thiol addition to N-acylimines.

Straightforward Design for Phenoxy-Imine Catalytic Activity in Ethylene Polymerization: Theoretical Prediction

The quantitative structure-activity relationship (QSAR) of 18 Ti-phenoxy-imine (FI-Ti)-based catalysts was investigated to clarify the role of the structural properties of the catalysts in polyethylene polymerization activity. The electronic properties of the FI-Ti catalysts were analyzed based on density functional theory with the M06L/6-31G** and LANL2DZ basis functions. The analysis results of the QSAR equation with a genetic algorithm showed that the polyethylene catalytic activity mainly depended on the highest occupied molecular orbital energy level and the total charge of the substituent group on phenylimine ring. The QSAR models showed good predictive ability (R2) and R2 cross validation (R2cv) values of greater than 0.927. The design concept is “head-hat”, where the hats are the phenoxy-imine substituents, and the heads are the transition metals. Thus, for the newly designed series, the phenoxy-imine substituents still remained, while the Ti metal was replaced by Zr or Ni transition metals, entitled FI-Zr and FI-Ni, respectively. Consequently, their polyethylene polymerization activities were predicted based on the obtained QSAR of the FI-Ti models, and it is noteworthy that the FI-Ni metallocene catalysts tend to increase the polyethylene catalytic activity more than that of FI-Zr complexes. Therefore, the new designs of the FI-Ni series are proposed as candidate catalysts for polyethylene polymerization, with their predicted activities in the range of 35,000–48,000 kg(PE)/mol(Cat.)·MPa·h. This combined density functional theory and QSAR analysis is useful and straightforward for molecular design or catalyst screening, especially in industrial research.

Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network

Machine learning (ML) has emerged as a powerful complement to simulation for materials discovery by reducing time for evaluation of energies and properties at accuracy competitive with first-principles methods. We use genetic algorithm (GA) optimization to discover unconventional spin-crossover complexes in combination with efficient scoring from an artificial neural network (ANN) that predicts spin-state splitting of inorganic complexes. We explore a compound space of over 5600 candidate materials derived from eight metal/oxidation state combinations and a 32-ligand pool. We introduce a strategy for error-aware ML-driven discovery by limiting how far the GA travels away from the nearest ANN training points while maximizing property (i.e., spin-splitting) fitness, leading to discovery of 80% of the leads from full chemical space enumeration. Over a 51-complex subset, average unsigned errors (4.5 kcal/mol) are close to the ANN's baseline 3 kcal/mol error. By obtaining leads from the trained ANN within seconds rather than days from a DFT-driven GA, this strategy demonstrates the power of ML for accelerating inorganic material discovery.

In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations

Checkpoint kinase 1 (Chk1) is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure–activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q² values (0.531, 0.726), fitted correlation r² coefficients (higher than 0.90), and standard error of prediction (less than 0.250). These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.

The enantioselectivity in asymmetric ketone hydrogenation catalyzed by RuH2(diphosphine)(diamine) complexes: insights from a 3D-QSSR and DFT study

The 3D-QSSR method was carried out to investigate the enantioselectivity of the asymmetric ketone hydrogenation (AKH) catalyzed by RuH₂(diphosphine)(diamine) complexes integrating with DFT method, which could provide a way to design homogeneous transition-metal catalysts.

Toward the Prediction of Activity in the Ethylene Polymerisation of ansa-Bis(indenyl) Zirconocenes: Effect of the Stereochemistry and Hydrogenation of the Indenyl Moiety

A combined experimental and quantum chemical study has been performed on rac- and meso-[Zr{1-Me₂ Si(3-η⁵ -C₉ H₅ Et)₂ }Cl₂ ] (rac- and meso-1) and their hydrogenated forms (rac- and meso-2) to understand ligand effects and guide ligand design for more active ansa-bis(indenyl) zirconocenes for the polymerisation of ethylene. The rac-ansa-zirconocene rac-[Zr(1-Me₂ Si{3-Et-(η⁵ -C₉ H₉ )}₂ )Cl₂ ] (rac-2) has been prepared and fully characterised by NMR spectroscopy and elemental analysis. The molecular structure of rac-2 has also been determined by single-crystal XRD. The behaviour of the catalysts was analysed in the polymerisation of ethylene and higher activities were obtained for rac-1 and its hydrogenated form rac-2. The influence of the stereochemistry and hydrogenation of the indenyl ligand on the experimental activities has been evaluated by computational studies. The differences along the reaction pathway are dominated by changes in the relative stabilities of the catalytic intermediates. A hybrid density functional B3LYP study, in the presence of toluene as the solvent, indicates that the rac forms give rise to more active species than their meso counterparts. The hydrogenation of the rac forms is a very promising approach to increase activities in polymerisation, in contrast to the meso forms. Finally, the global mechanism rate constants for the polymerisation reaction for each metallocene were calculated by using the thermodynamic formulation of transition-state theory to complement the computational study.

Deactivation of Ti and Zr half-metallocene complexes activated with B(C6F5)3: a case study in constructing DFT-based QSARs to predict unimolecular rate constants

A DFT-based QSAR was constructed to predict the deactivation pathways and rate constants for twenty-seven Ti and Zr half-metallocene complexes activated with B(C₆F₅)₃.

1,1-Olefin-bridged bis-(2-indenyl) metallocenes of titanium and zirconium

Tetrasubstituted alkenes bearing geminal 2-indenyl substituents were synthesized and metallated to form a new class of ansa titanium and zirconium metallocene complexes containing a single sp²-hybridized carbon bridge.