2015/05/15 by Atsuto Seko, Akira Takahashi, Isao Tanaka · 86 citations
Chemistry · Materials Science · Physics and Astronomy · #Atom (system on chip) #Atomic physics #Boron and Carbon Nanomaterials Research #Chemistry #Computational chemistry #Computer science #Density functional theory #Embedded atom model #Interatomic potential #Machine Learning in Materials Science #Materials science #Molecular dynamics #Molecular physics #Physics #Thermal Expansion and Ionic Conductivity #cond-mat.mtrl-sci
paper · pdf · open access · doi:10.1103/physrevb.92.054113
published in Physical Review B 92(5) (American Physical Society) · 11 pages, 5 figures
arxiv created 2015/05/15 · openalex publication_date 2015/08/31 · arxiv updated 2015/09/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Interatomic potentials have been widely used in atomistic simulations such as molecular dynamics. Recently, frameworks to construct accurate interatomic potentials that combine a 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.