  Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain
key materials' properties, but the iterative solution of the KS equations is a
numerically intensive task, which limits its application to complex systems. To
address this issue, machine learning (ML) models can be used as surrogates to
find the ground-state charge density and reduce the computational overheads. We
develop a grid-centred structural representation, based on Jacobi and Legendre
polynomials combined with a linear regression, to accurately learn the
converged DFT charge density. This integrates into a ML pipeline that can
return any density-dependent observable, including energy and forces, at the
quality of a converged DFT calculation, but at a fraction of the computational
cost. Fast scanning of energy landscapes and producing starting densities for
the DFT self-consistent cycle are among the applications of our scheme.
