The Atomic Partial Charges Arboretum: Trying to See the Forest for the Trees. Issue 8 (23rd March 2020)

• Record Type: Journal Article
• Authors: Cho, Minsik; Sylvetsky, Nitai; Eshafi, Sarah; Santra, Golokesh; Efremenko, Irena; Martin, Jan M. L.
• Is Part Of: Chemphyschem. Volume 21:Issue 8(2020)
• Page Start: 688
• Page End: 696
• Publication Date: 2020-03-23
• Subjects: atoms in molecules -- charge distributions -- molecular modelling -- population analysis -- principal components of ionicity; Chemistry, Physical and theoretical -- Periodicals; 541.05
• DOI: 10.1002/cphc.202000040
• Languages: English
• ISSNs: 1439-4235

Abstract

Abstract: Atomic partial charges are among the most commonly used interpretive tools in quantum chemistry. Dozens of different 'population analyses' are in use, which are best seen as proxies (indirect gauges) rather than measurements of a 'general ionicity'. For the GMTKN55 benchmark of nearly 2, 500 main‐group molecules, which span a broad swathe of chemical space, some two dozen different charge distributions were evaluated at the PBE0 level near the 1‐particle basis set limit. The correlation matrix between the different charge distributions exhibits a block structure; blocking is, broadly speaking, by charge distribution class. A principal component analysis on the entire dataset suggests that nearly all variation can be accounted for by just two 'principal components of ionicity': one has all the distributions going in sync, while the second corresponds mainly to Bader QTAIM vs. all others. A weaker third component corresponds to electrostatic charge models in opposition to the orbital‐based ones. The single charge distributions that have the greatest statistical similarity to the first principal component are iterated Hirshfeld (Hirshfeld‐I) and a minimal‐basis projected modification of Bickelhaupt charges. If three individual variables, rather than three principal components, are to be identified that contain most of the information in the whole dataset, one representative for each of the three classes of Corminboeuf et al. is needed: one based on partitioning of the density (such as QTAIM), a second based on orbital partitioning (such as NPA), and a third based on the molecular electrostatic potential (such as HLY or CHELPG). Abstract : Forest of ionicity : The dozens of different population analysis (POP) techniques in existence leave the end‐user perplexed. Through principal component analysis on a sample of over 2, 000 main‐group molecules, it emerges that just three features account for nearly all the variation: a "general ionicity" that has all POPs going in sync, a second component that pits QTAIM (plus APT) against the rest, and a weaker third that has ESP‐ vs. orbital‐based POPs.