Meso-carbazole substituted porphyrin complexes: Synthesis and spectral properties according to experiment, DFT calculations and the prediction by machine learning methods

• Published in: Dyes and pigments 2022-08, Vol.204, p.110470, Article 110470
• Main Authors: Bichan, N.G., Ovchenkova, E.N., Ksenofontov, A.A., Mozgova, V.A., Gruzdev, M.S., Chervonova, U.V., Shelaev, I.V., Lomova, T.N.
• Format: Article
• Language: English
• Subjects: Absorption maximum wavelength prediction; Cobalt complexes; DFT calculations; Machine learning methods; OCHEM; QSPR; Spectroscopy; Substituted porphyrins; Synthesis; Zinc complexes
• ISSN: 0143-7208; 1873-3743
• DOI: 10.1016/j.dyepig.2022.110470

In this work, two porphyrins bearing [3,6-di-tert-butyl-carbazol-9-yl-benzoyloxy)]- (1) and [3,6-bis(3′,6′-di(tert-butyl)-9′H-carbazol)-9H-carbazolbenzoyloxy]phenyl (2) groups and their zinc (1Zn, 2Zn)/cobalt (1Co, 2Co) complexes were synthesized and studied by experimental and theoretical methods. The spectral parameters (UV–vis absorption/femtosecond transient absorption/fluorescence, IR, 1H NMR, mass spectra) of the compounds were observed. Their structure was also examined by the DFT method. The comparative study of the UV–vis spectra by the DFT/TDDFT calculation, and by the prediction of the Soret band maximum using machine learning methods, namely the consensus models based on the data of over 10000 porphyrin free bases and their complexes with metals was performed. The absorption maximum wavelength (Soret band) of porphyrins predicted with machine learning methods showed better agreement with the experimental data compared to the DFT/TDDFT calculation. The final consensus model is freely available at https://ochem.eu/article/145340 and can be used by the other researchers to obtain new functionalized porphyrins with desired optical properties. •New meso-carbazole substituted porphyrin complexes were synthesized.•They were characterized by UV–visible, infrared, fluorescence, transient absorption spectroscopy, and mass spectrometry.•Prediction of the Soret band maximum by DFT calculations and machine learning methods.