  Machine learning is employed to build an energy density functional for
self-bound nuclear systems for the first time. By learning the kinetic energy
as a functional of the nucleon density alone, a robust and accurate
orbital-free density functional for nuclei is established. Self-consistent
calculations that bypass the Kohn-Sham equations provide the ground-state
densities, total energies, and root-mean-square radii with a high accuracy in
comparison with the Kohn-Sham solutions. No existing orbital-free density
functional theory comes close to this performance for nuclei. Therefore, it
provides a new promising way for future developments of nuclear energy density
functionals for the whole nuclear chart.
