Literature DB >> 26808717

Benchmarking DFT and semi-empirical methods for a reliable and cost-efficient computational screening of benzofulvene derivatives as donor materials for small-molecule organic solar cells.

Sara Tortorella1, Maurizio Mastropasqua Talamo, Antonio Cardone, Mariachiara Pastore, Filippo De Angelis.   

Abstract

A systematic computational investigation on the optical properties of a group of novel benzofulvene derivatives (Martinelli 2014 Org. Lett. 16 3424-7), proposed as possible donor materials in small molecule organic photovoltaic (smOPV) devices, is presented. A benchmark evaluation against experimental results on the accuracy of different exchange and correlation functionals and semi-empirical methods in predicting both reliable ground state equilibrium geometries and electronic absorption spectra is carried out. The benchmark of the geometry optimization level indicated that the best agreement with x-ray data is achieved by using the B3LYP functional. Concerning the optical gap prediction, we found that, among the employed functionals, MPW1K provides the most accurate excitation energies over the entire set of benzofulvenes. Similarly reliable results were also obtained for range-separated hybrid functionals (CAM-B3LYP and wB97XD) and for global hybrid methods incorporating a large amount of non-local exchange (M06-2X and M06-HF). Density functional theory (DFT) hybrids with a moderate (about 20-30%) extent of Hartree-Fock exchange (HFexc) (PBE0, B3LYP and M06) were also found to deliver HOMO-LUMO energy gaps which compare well with the experimental absorption maxima, thus representing a valuable alternative for a prompt and predictive estimation of the optical gap. The possibility of using completely semi-empirical approaches (AM1/ZINDO) is also discussed.

Entities:  

Year:  2016        PMID: 26808717     DOI: 10.1088/0953-8984/28/7/074005

Source DB:  PubMed          Journal:  J Phys Condens Matter        ISSN: 0953-8984            Impact factor:   2.333


  2 in total

1.  Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications.

Authors:  Sara Tortorella; Emanuele Carosati; Giulia Sorbi; Giovanni Bocci; Simon Cross; Gabriele Cruciani; Loriano Storchi
Journal:  J Comput Chem       Date:  2021-08-19       Impact factor: 3.672

2.  The role of the solvent and the size of the nanotube in the non-covalent dispersion of carbon nanotubes with short organic oligomers - a DFT study.

Authors:  Ahmad I Alrawashdeh; Jolanta B Lagowski
Journal:  RSC Adv       Date:  2018-08-29       Impact factor: 4.036

  2 in total

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