  Electron density $\rho(\vec{r})$ is the fundamental variable in the
calculation of ground state energy with density functional theory (DFT). Beyond
total energy, features and changes in $\rho(\vec{r})$ distributions are often
used to capture critical physicochemical phenomena in functional materials. We
present a machine learning framework for the prediction of $\rho(\vec{r})$. The
model is based on equivariant graph neural networks and the electron density is
predicted at special query point vertices that are part of the message passing
graph, but only receive messages. The model is tested across multiple data sets
of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and
LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the
accuracy of the proposed model exceeds typical variability in $\rho(\vec{r})$
obtained from DFT done with different exchange-correlation functionals. The
accuracy on all three datasets is beyond state of the art and the computation
time is orders of magnitude faster than DFT.
