  We present a $\Delta$-machine learning model for obtaining Kohn-Sham accuracy
from orbital-free density functional theory (DFT) calculations. In particular,
we employ a machine learned force field (MLFF) scheme based on the kernel
method to capture the difference between Kohn-Sham and orbital-free DFT
energies/forces. We implement this model in the context of on-the-fly molecular
dynamics simulations, and study its accuracy, performance, and sensitivity to
parameters for representative systems. We find that the formalism not only
improves the accuracy of Thomas-Fermi-von Weizs{\"a}cker (TFW) orbital-free
energies and forces by more than two orders of magnitude, but is also more
accurate than MLFFs based solely on Kohn-Sham DFT, while being more efficient
and less sensitive to model parameters. We apply the framework to study the
structure of molten Al$_{0.88}$Si$_{0.12}$, the results suggesting no
aggregation of Si atoms, in agreement with a previous Kohn-Sham study performed
at an order of magnitude smaller length and time scales.
