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Tuning steric and electronic effects in transition-metal ?-diketiminate complexes.

ABSTRACT: ?-Diketiminates are widely used supporting ligands for building a range of metal complexes with different oxidation states, structures, and reactivities. This Perspective summarizes the steric and electronic influences of ligand substituents on these complexes, with an eye toward informing the design of new complexes with optimized properties. The backbone and N-aryl substituents can give significant steric effects on structure, reactivity and selectivity of reactions. The electron density on the metal can be tuned by installation of electron withdrawing or donating groups on the ?-diketiminate ligand as well. Examples are shown from throughout the transition metal series to demonstrate different types of effects attributable to systematic variation of ?-diketiminate ligands.

SUBMITTER: Chen C

PROVIDER: S-EPMC4581995 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Publications

Tuning steric and electronic effects in transition-metal β-diketiminate complexes.

Chen Chi C Bellows Sarina M SM Holland Patrick L PL

Dalton transactions (Cambridge, England : 2003) 20150805 38

β-Diketiminates are widely used supporting ligands for building a range of metal complexes with different oxidation states, structures, and reactivities. This Perspective summarizes the steric and electronic influences of ligand substituents on these complexes, with an eye toward informing the design of new complexes with optimized properties. The backbone and N-aryl substituents can give significant steric effects on structure, reactivity and selectivity of reactions. The electron density on th ...[more]

PMID: 26244489

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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

Tuning steric and electronic effects in transition-metal ?-diketiminate complexes.

Publications

Tuning steric and electronic effects in transition-metal β-diketiminate complexes.

Similar Datasets

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