The RunEDDB program imlements the recently proposed method of the one-electron densityED(r) decomposition into components representing different levels of electron delocalization:
EDLA(r) is the Density of Electrons Localized on Atoms and represents the distribution of electrons that do not participate in the covalent bonding (core electrons, lone-pairs, ionic bonds);
EDLB(r) is the Electron Density of Localized Bonds and represents the distribution of electrons that contributes to the chemical bonding but are delocalized in a two-center sense (standard Lewis-type covalent bonds);
EDDB(r) stands for the Electron Density of Delocalized Bonds, which complements the previous two components and represents the distribution of electrons delocalized between different chemical bonds (conjugated and multicenter bonds, aromatic rings). EDLB(r) and EDDB(r) combined together give rise to the Density of Electrons Delocalized between Atoms, EDDA(r), which describes the global effects of electron sharing in a molecule.
This novel charge decomposition scheme utilizes the recently proposed bond-orbital projection formalism, which is partially based on the age-old concept of bond-order orbitals and as such it is well defined for one-determinant wavefunctions (HF- and DFT-type) of both closed- and open-shell molecular systems. Its current implementation involves the Hilbert-space partitioning within the representation of natural atomic orbitals (NAO), which is widely available for most of the popular quantum-chemistry packages through NBO and JaNPA interfaces.
Features & Capabilities
The EDLB(r) and EDDB(r) functions provide a uniform approach to quantify chemical bonding and resonance, multicenter electron sharing and aromaticity within one theoretical paradigm. There are several important features that set the EDDB method apart from other measures of electron delocalization in aromatic rings:
Versality − EDDB can be used to study of almost all types of aromaticity found in the literature.
Intuitiveness − EDDB quantifies bond resonance within the framework of the population analysis (in |e|).
Definiteness − EDDB does not need parametrization to the reference model system.
Effectiveness − EDDB gives similar predictions to MCI but is much less computationally expensive.