  Machine learning is used to approximate the kinetic energy of one dimensional
diatomics as a functional of the electron density. The functional can
accurately dissociate a diatomic, and can be systematically improved with
training. Highly accurate self-consistent densities and molecular forces are
found, indicating the possibility for ab-initio molecular dynamics simulations.
