  We apply a number of atomic decomposition schemes across the standard QM7
dataset -- a small model set of organic molecules at equilibrium geometry -- to
inspect the possible emergence of trends among contributions to atomization
energies from distinct elements embedded within molecules. Specifically, a
recent decomposition scheme of ours based on spatially localized molecular
orbitals is compared to alternatives that instead partition molecular energies
on account of which nuclei individual atomic orbitals are centred on. We find
these partitioning schemes to expose the composition of chemical compound space
in very dissimilar ways in terms of the grouping, binning, and heterogeneity of
discrete atomic contributions, e.g., those associated with hydrogens bonded to
different heavy atoms. Furthermore, unphysical dependencies on the one-electron
basis set are found for some, but not all of these schemes. The relevance and
importance of these compositional factors for training tailored neural network
models based on atomic energies are next assessed. We identify both limitations
and possible advantages with respect to contemporary machine learning models
and discuss the design of potential counterparts based on atoms and the
intrinsic energies of these as the principal decomposition units.
