Recently Zhao et al. reported a study regarding 3D-CNN which utilizes the electron localization function (ELF) for prediction of various properties for materials. The ELF does capture both the localization and delocalization of the electron density. Generation of an ELF map requires quantum chemical calculations. A fast ML algorithm would require rapid calculation of electron density which is not possible using traditional computations. The solution to this problem is the construction of Hirshfeld partitioning based promolecule densities for the clusters/molecules examined. For a better understanding of the promolecule density, we need to know about the Hirshfeld partitioning scheme. The Hirshfeld partitioning scheme is basically a partition of the molecular electron density into its constituent atomic density. In this scheme, a promolecule density is assigned to a molecule by superposition of precalculated electron densities of the atomic constituents of the molecule considered. Then this electron density is partitioned by giving weightage to the atomic constituents proportionated to their contribution to the electron density. The promolecule density (ρ o (r)) can be written as a weighed summation of atomic densities (3):