Dataset Information

Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes.

ABSTRACT: The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten-benzylidyne complexes of the form W([triple bond, length as m-dash]CArR)L4X. Using a reference molecular framework and the meta- and para-substituent Hammett parameters of the ligands, the models predict energies of frontier orbitals that correlate with redox potentials. The regressive models capture the multivariate character of electron-donating trends as influenced by multiple substituents even for non-aryl ligands, harnessing the breadth of Hammett parameters in a generalized model. We find a tungsten catalyst with tetramethylethylenediamine (tmeda) equatorial ligands and axial methoxyl substituents that should attract significant experimental interest since it is predicted to be highly reducing when photoactivated with visible light. The utilization of Hammett parameters in this study presents a generalizable and compact representation for exploring the effects of ligand substitutions.

SUBMITTER: Chang AM

PROVIDER: S-EPMC6657405 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes.

Chang Alexander M AM Freeze Jessica G JG Batista Victor S VS

Chemical science 20190612 28

The successful application of Hammett parameters as input features for regressive machine learning models is demonstrated and applied to predict energies of frontier orbitals of highly reducing tungsten-benzylidyne complexes of the form W([triple bond, length as m-dash]CArR)L4X. Using a reference molecular framework and the meta- and para-substituent Hammett parameters of the ligands, the models predict energies of frontier orbitals that correlate with redox potentials. ...[more]

PMID: 31391907

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Project description:Applicability of Hammett parameters (σ m and σ p ) was tested in extended π-systems in gas phase. Three different model graphene systems, viz. 5,5-graphene (GR), 3-B-5,5-graphene (3BGR), and 3-N-5,5-graphene (3NGR), were designed as extended π-systems, and interactions of various nitrobenzene derivatives (mainly m- and p-substituted together with some multiple substitutions) on such platforms were monitored using density functional theory (M06/cc-pVDZ, M06/cc-pVTZ, M06/sp-aug-cc-pVTZ) and Møller-Plesset second-order perturbation (MP2/cc-pV-DZ) theory. Offset face to face (OSFF) stackings were found to be the favored orientations, and reasonable correlations were found between binding energies (ΔE B) and the ∑|σ m | values of the substituted nitrobenzenes. It was proposed previously that |σ m | contains information about the substituents' polarizability and controls electrostatic and dispersion interactions. The combination of ∑|σ m | and molar refractivity (as ∑M r) or change in polarizability (Δα: with respect to benzene) of nitrobenzene derivatives generated statistically significant correlation with respect to ΔE B, thereby supporting the hypothesis related to the validity of |σ m | correlations. The |σ p | parameters also maintain similar correlations for the various p-substituted nitrobenzene derivatives together with several multiply-substituted nitrobenzene derivatives. The correlation properties in such cases are similar to the |σ m | cases, and the energy partition analysis for both the situations reveled importance of electrostatic and dispersion contributions in such interactions. The applicability of Hammett parameters was observed previously on the restricted parallel face to face orientation of benzene···substituted benzene systems, and the present results show that such an idea could be used to predict ΔE B values in OSFF orientations, if the scaffolds are designed in such a way that substituted benzene systems cannot escape their π-clouds.

Dataset Information

Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes.

Publications

Hammett neural networks: prediction of frontier orbital energies of tungsten-benzylidyne photoredox complexes.

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