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I. Introduction

By using IGMPlot you can identify, characterize, and quantify molecular interactions over a broad range: from non-covalent to covalent bonding, through metal coordination. This tool can be helpful for interpretation accessible to a wide community of chemists (organic, inorganic chemistry, including transition metal complexes and reaction mechanisms). From a practical perspective, an attractive feature of the IGM approach is to provide an automatic workflow delivering properties that provides chemists with a visual and quantitative understanding of interactions. Although IGMPlot relies on the electron density (ED) topology, no topological analysis is required like in QTAIM and an IGM analysis can be achieved with little preparation. For more detailed information on the concept please refer to the original papers:
  1. Lefebvre C., Rubez G., Khartabil H., Boisson JC., Contreras-García J., Hénon E. Phys Chem Chem Phys. 2017, 19, 17928 doi:
  2. Lefebvre C., Khartabil H., Boisson JC., Contreras-García J., Piquemal J.-P., Hénon E. Chem. Phys. Chem. 2018, 19, 1 doi:
  3. Miguel Ponce-Vargas, Corentin Lefebvre, Jean-Charles Boisson, Eric Hénon J. Chem. Inf. Model. 2020, 60, 1, 268 doi:
  4. Johanna Klein, Hassan Khartabil, Jean-Charles Boisson, Julia Contreras-García, Jean-Philip Piquemal, Eric Hénon J. . Phys. Chem. A 2020, 124, 9, 1850 doi:


IGMPlot is based on the IGM concept and its local descriptor called δg, which can be calculated from the electron density ρ (ED). The IGM-δg approach was initially designed to work with promolecular density, to describe weak interactions. The term promolecular ED refers to an ED model prior to molecule formation. The promolecular ED is a non-relaxed electron density, the sum of spherically averaged neutral atomic densities ρi. It lacks the relaxation introduced in the SCF procedure or in DFT calculations, and then it fails to describe covalent situations for which a wavefunction description is required. In the promolecular mode, only the atomic coordinates have to be supplied and the promolecular ED estimations are fast compared to Quantum Mechanical calculations.

But in 2018, we proposed the Gradient-Based Partitioning (GBP) that extends the IGM concept to electron density derived from a wavefunction. Thus, thanks to the new IGM approach, detailed information can be directly obtained either on the covalent or on the non-covalent domain, for small and larger molecular systems.

In the absence of interactions, for instance, in an isolated atom, ρ shows an exponential decay far from the nucleus. In contrast, in molecular systems, deviations from this exponential decay can be observed. δg captures these deviations. From a practical perspective, IGMPlot employs a numeric grid and calculates local δg at each node of the grid. δg is obtained by doing the difference between the ED gradient of a reference non-interacting system (the so-called Independent Gradient Model) and the ED gradient of the real system.

More precisely, the δg local descriptor quantifies the contragradience between the two fragment ED sources. The so-called ED contragradience situation indicates the mutual penetration of electron charge densities, which is expected when fragments sources are brought closer to each other, and is also sometimes referred to hereafter as density overlap or density sharing. The IGM definition grants that positions of space with non-zero values of δg exclusively correspond to interaction situations and the greater δg, the stronger the interaction. δg is not dimensionless.

In order to better highlight peaks in δg(ρ) plots, a new qg descriptor has been devised to color points of this plot. It is the ’quotient’ twin of the 'difference' δg descriptor. It is dimensionless and it is very sensitive to ED contragradience situation, but it cannot serve to quantify the interaction since it tends to infinity as ∇ρ approaches 0 close to critical points, even for weak interactions like hydrogen-bonding. From a practical perspective, this descriptor qg serves to color IGM and NCI plots and it is also employed in IGMPlot to find critical points.

The IGM framework as described above gives rise to a number of analysing and interpretative tools in IGMPlot:
For more detailed information we refer the interested reader to the original articles.