  Electron charge density distribution of materials is one of the key
quantities in computational materials science as theoretically it determines
the ground state energy and practically it is used in many materials analyses.
However, the scaling of density functional theory calculations with number of
atoms limits the usage of charge-density-based calculations and analyses. Here
we introduce a machine learning scheme with local-environment-based graphs and
graph convolutional neural networks to predict charge density on grid-points
from crystal structure. We show the accuracy of this scheme through a
comparison of predicted charge densities as well as properties derived from the
charge density, and the scaling is O(N). More importantly, the transferability
is shown to be high with respect to different compositions and structures,
which results from the explicit encoding of geometry.
