Electronic structure of BN-aromatics: Choice of reliable computational tools

The importance of having reliable calculation tools to interpret and predict the electronic properties of BN-aromatics is directly linked to the growing interest for these very promising new systems in the field of materials science, biomedical research, or energy sustainability. Ionization energy (IE) is one of the most important parameters to approach the electronic structure of molecules. It can be theoretically estimated, but in order to evaluate their persistence and propose the most reliable tools for the evaluation of different electronic properties of existent or only imagined BN-containing compounds, we took as reference experimental values of ionization energies provided by ultra-violet photoelectron spectroscopy (UV-PES) in gas phase-the only technique giving access to the energy levels of filled molecular orbitals. Thus, a set of 21 aromatic molecules containing B-N bonds and B-N-B patterns has been merged for a comparison between experimental IEs obtained by UV-PES and various theoretical approaches for their estimation. Time-Dependent Density Functional Theory (TD-DFT) methods using B3LYP and long-range corrected CAM-B3LYP functionals are used, combined with the ΔSCF approach, and compared with electron propagator theory such as outer valence Green's function (OVGF, P3) and symmetry adapted cluster-configuration interaction ab initio methods. Direct Kohn-Sham estimation and "corrected" Kohn-Sham estimation are also given. The deviation between experimental and theoretical values is computed for each molecule, and a statistical study is performed over the average and the root mean square for the whole set and sub-sets of molecules. It is shown that (i) ΔSCF+TDDFT(CAM-B3LYP), OVGF, and P3 are the most efficient way for a good agreement with UV-PES values, (ii) a CAM-B3LYP range-separated hybrid functional is significantly better than B3LYP for the purpose, especially for extended conjugated systems, and (iii) the "corrected" Kohn-Sham result is a fast and simple way to predict IEs.