  We introduce new and robust decompositions of mean-field Hartree-Fock (HF)
and Kohn-Sham density functional theory (KS-DFT) relying on the use of
localized molecular orbitals and physically sound charge population protocols.
The new lossless property decompositions, which allow for partitioning
1-electron reduced density matrices into either bond-wise or atomic
contributions, are compared to alternatives from the literature with regards to
both molecular energies and dipole moments. Besides commenting on possible
applications as an interpretative tool in the rationalization of certain
electronic phenomena, we demonstrate how decomposed mean-field theory makes it
possible to expose and amplify compositional features in the context of
machine-learned quantum chemistry. This is made possible by improving upon the
granularity of the underlying data. On the basis of our preliminary
proof-of-concept results, we conjecture that many of the structure-property
inferences in existence today may be further refined by efficiently leveraging
an increase in dataset complexity and richness.
