  We use voxel deep neural networks to predict the kinetic energy densities and
functional derivatives of kinetic energies for the Thomas-Fermi model and
Kohn-Sham density functional theory calculations. We show that an electron
density can be found self-consistently for a graphene lattice without any
projection scheme using a voxel deep neural network trained with the
Thomas-Fermi model. Additionally, we show that we can predict the kinetic
energy of a graphene lattice within chemical accuracy after training from only
2 density functional theory calculations. This accuracy is maintained to
arbitrary system size with linear scaling. Furthermore, we demonstrate an
alternative, functional derivative-free, Monte Carlo based orbital free density
functional theory algorithm to calculate an accurate 2-electron density in a
double inverted Gaussian potential with a machine-learned kinetic energy
functional.
