DFT and QSAR Studies of Ethylene Polymerization by Zirconocene Catalysts

Revisiting Stereoselective Propene Polymerization Mechanisms: Insights through the Activation Strain Model

The stereoelectronic factors responsible for stereoselectivity in propene polymerization with several metallocene and post-metallocene transition metal catalysts have been revisited using a combined approach of DFT calculations, the Activation Strain Model, Natural Energy Decomposition Analysis and a molecular descriptor (%VBur). There are in most cases two different paths leading to the formation of stereoerrors (SE), and the classical model does not suffice to fully understand stereoregulation. Improving stereoselectivity requires raising the energies of both SE insertion transition states. Our analyses show that the degrees of deformation of the active site (catalyst+chain) and the prochiral monomer differ for these two paths, and between different catalyst classes. Based on such analyses we discuss: a) the subtle differences in SE formation between stereoselective catalysts with different ligand frameworks; b) the reason for exceptional stereoselectivity reported for a special ansa-metallocene catalyst; c) the (double) stereocontrol origin for isoselective catalysts; d) the electronic contribution for isoselective catalysts generating SE by a modification of the ligand wrapping mode during the polymerization. Although this study will not immediately suggest new catalyst structures, we believe that understanding stereoregulation in great detail will increase our chances of success.

A human-machine interface for automatic exploration of chemical reaction networks

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates.

Machine Learning Models for Predicting Zirconocene Properties and Barriers

Zr metallocenes have significant potential to be highly tunable polyethylene catalysts through modification of the aromatic ligand framework. Here we report the development of multiple machine learning models using a large library (>700 systems) of DFT-calculated zirconocene properties and barriers for ethylene polymerization. We show that very accurate machine learning models are possible for HOMO-LUMO gaps of precatalysts but the performance significantly depends on the machine learning algorithm and type of featurization, such as fingerprints, Coulomb matrices, smooth overlap of atomic positions, or persistence images. Surprisingly, the description of the bonding hapticity, the number of direct connections between Zr and the ligand aromatic carbons, only has a moderate influence on the performance of most models. Despite robust models for HOMO-LUMO gaps, these types of machine learning models based on structure connectivity type features perform poorly in predicting ethylene migratory insertion barrier heights. Therefore, we developed several relatively robust and accurate machine learning models for barrier heights that are based on quantum-chemical descriptors (QCDs). The quantitative accuracy of these models depends on which potential energy surface structure QCDs were harvested from. This revealed a Hammett-type principle to naturally emerge showing that QCDs from the π-coordination complexes provide much better descriptions of the transition states than other potential-energy structures. Feature importance analysis of the QCDs provides several fundamental principles that influence zirconocene catalyst reactivity.

Towards designer polyolefins: highly tuneable olefin copolymerisation using a single permethylindenyl post-metallocene catalyst

Using a highly active permethylindenyl-phenoxy (PHENI*) titanium catalyst, high to ultra-high molecular weight ethylene-linear-α-olefin (E/LAO) copolymers are prepared in high yields under mild conditions (2 bar, 30-90 °C). Controllable, efficient, and predictable comonomer enchainment provides access to a continuum of copolymer compositions and a vast range of material properties using a single monomer-agnostic catalyst. Multivariate statistical tools are employed that combine the tuneability of this system with the analytical and predictive power of data-derived models, this enables the targeting of polyolefins with designer properties directly through predictive alteration of reaction conditions.

Mechanistic Studies on Aluminum-Catalyzed Ring-Opening Alternating Copolymerization of Maleic Anhydride with Epoxides: Ligand Effects and Quantitative Structure-Activity Relationship Model

Previous work has indicated that aluminum (Al) complexes supported by a bipyridine bisphenolate (BpyBph) ligand exhibit higher activity in the ring-opening copolymerization (ROCOP) of maleic anhydride (MAH) and propylene oxide (PO) than their salen counterparts. Such a ligand effect in Al-catalyzed MAH-PO copolymerization reactions has yet to be clarified. Herein, the origin and applicability of the ligand effect have been explored by density functional theory, based on the mechanistic analysis for chain initiation and propagation. We found that the lower LUMO energy of the (BpyBph)AlCl complex accounts for its higher activity than the (salen)AlCl counterpart in MAH/epoxide copolymerizations. Inspired by the ligand effect, a structure-energy model was further established for catalytic activity (TOF value) predictions. It is found that the LUMO energies of aluminum chloride complexes and their average NBO charges of coordinating oxygen atoms correlate with the catalytic activity (TOF value) of Al complexes (R2 value of 0.98 and '3-fold' cross-validation Q2 value of 0.88). This verified that such a ligand effect is generally applicable in anhydride/epoxide ROCOP catalyzed by aluminum complex and provides hints for future catalyst design.

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.

Transition Metal-(μ-Cl)-Aluminum Bonding in α-Olefin and Diene Chemistry

Olefin and diene transformations, catalyzed by organoaluminum-activated metal complexes, are widely used in synthetic organic chemistry and form the basis of major petrochemical processes. However, the role of M−(μ-Cl)−Al bonding, being proven for certain >C=C< functionalization reactions, remains unclear and debated for essentially more important industrial processes such as oligomerization and polymerization of α-olefins and conjugated dienes. Numerous publications indirectly point at the significance of M−(μ-Cl)−Al bonding in Ziegler−Natta and related transformations, but only a few studies contain experimental or at least theoretical evidence of the involvement of M−(μ-Cl)−Al species into catalytic cycles. In the present review, we have compiled data on the formation of M−(μ-Cl)−Al complexes (M = Ti, Zr, V, Cr, Ni), their molecular structure, and reactivity towards olefins and dienes. The possible role of similar complexes in the functionalization, oligomerization and polymerization of α-olefins and dienes is discussed in the present review through the prism of the further development of Ziegler−Natta processes and beyond.

Data-Driven Materials Innovation and Applications

Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data-driven scientific research. This transition requires the development of authoritative and up-to-date frameworks for data-driven approaches for material innovation. A critical discussion on the current advances in the data-driven discovery of materials with a focus on frameworks, machine-learning algorithms, material-specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data-driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data-intensive strategies and machine-learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data-driven processes. Furthermore, an in-depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data-driven paradigms is outlined, and the opportunities and challenges in data-driven material innovation are highlighted.

Theoretical Study on Ethylene Polymerization Catalyzed by Half-Titanocenes Bearing Different Ancillary Groups

Half-titanocenes are well known to show high activity for ethylene polymerization and good capability for copolymerization of ethylene with other olefins, and the ancillary ligands can crucially affect the catalytic performance. In this paper, the mechanisms of ethylene polymerization catalyzed by three half-metallocenes, (η5-C5Me5)TiCl2(O-2,6-iPr2C6H3) (1), (η5-C5Me5)TiCl2(N=CtBu2) (2) and [Me2Si(η5-C5Me4)(NtBu)]TiCl2 (3), have been investigated by density functional theory (DFT) method. At the initiation stage, a higher free energy barrier was determined for complex 1, probably due to the presence of electronegative O atom in phenoxy ligand. At the propagation stage, front-side insertion of the second ethylene is kinetically more favorable than back-side insertion for complexes 1 and 2, while both side insertion orientations are comparable for complex 3. The energy decomposition showed that the bridged cyclopentadienyl amide ligand could enhance the rigidity of the active species as suggested by the lowest deformation energy derived from 3. At the chain termination stage, β-H transfer was calculated to be a dominant chain termination route over β-H elimination, presumably owing to the thermodynamic perspective.

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.

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.

Thermochemistry of Tungsten-3p Elements for Density Functional Theory, Caveat Lector!

There are two primary foci in this research on WE (E = Si, P, and S) bonds: prediction of their bond dissociation enthalpies (BDEs), including σ- and π-bond energy components, and assessing the uncertainty of these BDE predictions for levels of theory commonly used in the literature. The internal standards for computational accuracy include metal-element bond lengths (mean absolute error = 1.8 ± 1.2%), main group homolog BDEs versus higher levels of ab initio theory (W1U and G4 BDEs, R² = 0.98), and DLPNO-CCSD(T)/def2-QZVPP calculations for metal-ligand BDEs (R² = 0.88). The W═Si first π-bond is underreported for density functional theory (DFT)/MP2 methods versus DLPNO-CCSD(T), while the latter shows negligible strength for the W;Si second π-bond, consistent with the literature. This research highlights clear issues with the underlying assumptions required for the use of perturbation theory methods for the fragments derived from W-P homolysis. The difficulties associated with modeling the metal thermochemistry with DFT (and MP2) levels of theory are manifest in the broad standard deviations observed. However, the average BDEs found using 48 popular DFT and MP2 levels of theory are reliable, 10.8 ± 6.8% mean absolute error (with W-P removed) versus DLPNO-CCSD(T), with the caveat that the individual basis set/pseudopotential/valence basis set combination can vary wildly. Analysis of the absolute error percentages with respect to the level of theory indicates little benefit to going higher on Jacob's Ladder, as simpler methods have lower error versus high-level ab initio techniques such as G4 and DLPNO-CCSD(T).

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.

Experimental and Theoretical Study of Zirconocene-Catalyzed Oligomerization of 1-Octene

Zirconocene-catalyzed coordination oligomerization of higher α-olefins is of theoretical and practical interest. In this paper, we present the results of experimental and theoretical study of α-olefin oligomerization, catalyzed by (η⁵-C₅H₅)]₂ZrX₂1/1′ and O[SiMe₂(η⁵-C₅H₄)]₂ZrX₂2/2′ (X = Cl, Me) with the activation by modified methylalymoxane MMAO-12 or by perfluoroalkyl borate [PhNMe₂H][B(C₆F₅)₄] (NB^F) in the presence and in the absence of organoaluminium compounds, Al(CH₂CHMe₂)₃ (TIBA) and/or Et₂AlCl. Under the conditions providing a conventional mononuclear reaction mechanism, 1′ catalyzed dimerization with low selectivity, while 2′ initiated the formation of oligomers in equal mass ratio. The presence of TIBA and especially Et₂AlCl resulted in an increase of the selectivity of dimerization. Quantum chemical simulations of the main and side processes performed at the M-06x/ DGDZVP level of the density functional theory (DFT) allowed to explain experimental results involving traditional mononuclear and novel Zr-Al₁ and Zr-Al₂ mechanistic concepts.

Quantitative structure-property relationship of the photoelectrochemical oxidation of phenolic pollutants at modified nanoporous titanium oxide using supervised machine learning

Here we report on an advanced photoelectrochemical (PEC) oxidation of 22 phenolic pollutants based on modified nanoporous TiO2, which was directly grown on a titanium substrate electrochemically. Their degradation rate constants were experimentally determined and their physicochemical properties were computaionally calculated. The quantitative structure-property relationship (QSPR) was elucidated by employing multiple linear regression (MLR) method. A supervised machine learning approach was employed to build QSPR models. The high predictive abilities of the QSPR model were validated via leave-one-out (LOO) method and a strict regimen of statistical validation tests. The significant descriptors identified in the QSPR Model for the phenolic compounds were also assessed using a typical dye pollutant Rhodamine B, further confirming the high effectiveness and predictability of the optimized model. Our study has shown that the integrated effect of the structural, hydrophobic and topological properties along with electronic property should be considered in order to design an efficient PEC catalytic approach for environmental applications.

A Quantitative Model for Alkane Nucleophilicity Based on C-H Bond Structural/Topological Descriptors

A first quantitative model for calculating the nucleophilicity of alkanes is described. A statistical treatment was applied to the analysis of the reactivity of 29 different alkane C-H bonds towards in situ generated metal carbene electrophiles. The correlation of the recently reported experimental reactivity with two different sets of descriptors comprising a total of 86 parameters was studied, resulting in the quantitative descriptor-based alkane nucleophilicity (QDEAN) model. This model consists of an equation with only six structural/topological descriptors, and reproduces the relative reactivity of the alkane C-H bonds. This reactivity can be calculated from parameters emerging from the schematic drawing of the alkane and a simple set of sums.