First-principles interatomic potentials for ten elemental metals via compressed sensing

TL;DR

This paper demonstrates the use of compressed sensing with elastic net regression to develop accurate, sparse interatomic potentials for ten elemental metals, enabling precise atomistic simulations based on DFT data.

Contribution

It introduces a systematic approach to derive interatomic potentials for multiple metals using compressed sensing, improving control over potential accuracy and computational efficiency.

Findings

Prediction errors below 3.5 meV/atom for energy

Accurate prediction of lattice constants and phonon dispersion

Sparse potentials effectively model ten elemental metals

Abstract

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.