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Trends in Structure and Ethylene Polymerization Reactivity of Transition-Metal Permethylindenyl-phenoxy (PHENI*) Complexes.

ABSTRACT: A family of ansa-permethylindenyl-phenoxy (PHENI*) transition-metal chloride complexes has been synthesized and characterized (1-7; {(η⁵-C₉Me₆)Me(R″)Si(2-R-4-R'-C₆H₂O)}MCl₂; R,R' = Me, ^tBu, Cumyl (CMe₂Ph); R″ = Me, ⁿPr, Ph; M = Ti, Zr, Hf). The ancillary chloride ligands could readily be exchanged with halides, alkyls, alkoxides, aryloxides, or amides to form PHENI* complexes [L]TiX₂ (8-17; X = Br, I, Me, CH₂SiMe₃, CH₂Ph, NMe₂, OEt, ODipp). The solid-state crystal structures of these PHENI* complexes indicate that one of two conformations may be preferred, parametrized by a characteristic torsion angle (TA'), in which the η⁵ system is either disposed away from the metal center or toward it. Compared to indenyl PHENICS complexes, the permethylindenyl (I*) ligand appears to favor a conformation in which the metal center is more accessible. When heterogenized on solid polymethylaluminoxane (sMAO), titanium PHENI* complexes exhibit exceptional catalytic activity toward the polymerization of ethylene. Substantially greater activities are reported than for comparable PHENICS catalysts, along with the formation of ultrahigh-molecular-weight polyethylenes (UHMWPE). Catalyst-cocatalyst ion pairing effects are observed in cationization experiments and found to be significant in homogeneous catalytic regimes; these effects are also related to the influence of the ancillary ligand leaving groups in slurry-phase polymerizations. Catalytic efficiency and polyethylene molecular weight are found to increase with pressure, and PHENI* catalysts can be categorized as being among the most active for the controlled synthesis of UHMWPE.

SUBMITTER: Collins Rice CG

PROVIDER: S-EPMC10900520 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Trends in Structure and Ethylene Polymerization Reactivity of Transition-Metal Permethylindenyl-phenoxy (PHENI*) Complexes.

Collins Rice Clement G CG Hayden Justin A JA Hawkins Adam D AD Morris Louis J LJ Turner Zoë R ZR Buffet Jean-Charles JC O'Hare Dermot D

Organometallics 20240214 4

A family of ansa-permethylindenyl-phenoxy (PHENI*) transition-metal chloride complexes has been synthesized and characterized (1-7; {(η5-C9Me6)Me(R″)Si(2-R-4-R'-C6H2O)}MCl2; R,R' = Me, tBu, Cumyl (CMe2Ph); R″ = Me, nPr, Ph; M = Ti, Zr, Hf). The ancillary chloride ligands could readily be exchanged with halides, alkyls, alkoxides, aryloxides, or amides to form PHENI* comp ...[more]

PMID: 38425384

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Project description:High-throughput computational screening has emerged as a critical component of materials discovery. Direct density functional theory (DFT) simulation of inorganic materials and molecular transition metal complexes is often used to describe subtle trends in inorganic bonding and spin-state ordering, but these calculations are computationally costly and properties are sensitive to the exchange-correlation functional employed. To begin to overcome these challenges, we trained artificial neural networks (ANNs) to predict quantum-mechanically-derived properties, including spin-state ordering, sensitivity to Hartree-Fock exchange, and spin-state specific bond lengths in transition metal complexes. Our ANN is trained on a small set of inorganic-chemistry-appropriate empirical inputs that are both maximally transferable and do not require precise three-dimensional structural information for prediction. Using these descriptors, our ANN predicts spin-state splittings of single-site transition metal complexes (i.e., Cr-Ni) at arbitrary amounts of Hartree-Fock exchange to within 3 kcal mol-1 accuracy of DFT calculations. Our exchange-sensitivity ANN enables improved predictions on a diverse test set of experimentally-characterized transition metal complexes by extrapolation from semi-local DFT to hybrid DFT. The ANN also outperforms other machine learning models (i.e., support vector regression and kernel ridge regression), demonstrating particularly improved performance in transferability, as measured by prediction errors on the diverse test set. We establish the value of new uncertainty quantification tools to estimate ANN prediction uncertainty in computational chemistry, and we provide additional heuristics for identification of when a compound of interest is likely to be poorly predicted by the ANN. The ANNs developed in this work provide a strategy for screening transition metal complexes both with direct ANN prediction and with improved structure generation for validation with first principles simulation.

Dataset Information

Trends in Structure and Ethylene Polymerization Reactivity of Transition-Metal Permethylindenyl-phenoxy (PHENI*) Complexes.

Publications

Trends in Structure and Ethylene Polymerization Reactivity of Transition-Metal Permethylindenyl-phenoxy (PHENI*) Complexes.

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