  Interatomic potentials have been widely used in atomistic simulations such as
molecular dynamics. Recently, frameworks to construct accurate interatomic
potentials that combine a systematic set of density functional theory (DFT)
calculations with machine learning techniques have been proposed. One of these
methods is to use compressed sensing to derive a sparse representation for the
interatomic potential. This facilitates the control of the accuracy of
interatomic potentials. In this study, we demonstrate the applicability of
compressed sensing to deriving the interatomic potential of ten elemental
metals, namely, Ag, Al, Au, Ca, Cu, Ga, In, K, Li and Zn. For each elemental
metal, the interatomic potential is obtained from DFT calculations using
elastic net regression. The interatomic potentials are found to have prediction
errors of less than 3.5 meV/atom, 0.03 eV/\AA\ and 0.15 GPa for the energy,
force and the stress tensor, respectively, which enable the accurate prediction
of physical properties such as lattice constants and the phonon dispersion
relationship.
