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DFT Study of the Molecular and Electronic Structure of Metal-Free Tetrabenzoporphyrin and Its Metal Complexes with Zn, Cd, Al, Ga, In.

ABSTRACT: The electronic and molecular structures of metal-free tetrabenzoporphyrin (H₂TBP) and its complexes with zinc, cadmium, aluminum, gallium and indium were investigated by density functional theory (DFT) calculations with a def2-TZVP basis set. A geometrical structure of ZnTBP and CdTBP was found to possess D_4h symmetry; AlClTBP, GaClTBP and InClTBP were non-planar complexes with C_4v symmetry. The molecular structure of H₂TBP belonged to the point symmetry group of D_2h. According to the results of the natural bond orbital (NBO) analysis, the M-N bonds had a substantial ionic character in the cases of the Zn(II) and Cd(II) complexes, with a noticeably increased covalent contribution for Al(III), Ga(III) and In(III) complexes with an axial -Cl ligand. The lowest excited states were computed with the use of time-dependent density functional theory (TDDFT) calculations. The model electronic absorption spectra indicated a weak influence of the nature of the metal on the Q-band position.

SUBMITTER: Eroshin AV

PROVIDER: S-EPMC8781462 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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DFT Study of the Molecular and Electronic Structure of Metal-Free Tetrabenzoporphyrin and Its Metal Complexes with Zn, Cd, Al, Ga, In.

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