English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Interatomic potentials have been widely used in atomistic simulations such asmolecular dynamics. Recently, frameworks to construct accurate interatomicpotentials that combine a systematic set of density functional theory (DFT)calculations with machine learning techniques have been proposed. One of thesemethods is to use compressed sensing to derive a sparse representation for theinteratomic potential. This facilitates the control of the accuracy ofinteratomic potentials. In this study, we demonstrate the applicability ofcompressed sensing to deriving the interatomic potential of ten elementalmetals, namely, Ag, Al, Au, Ca, Cu, Ga, In, K, Li and Zn. For each elementalmetal, the interatomic potential is obtained from DFT calculations usingelastic net regression. The interatomic potentials are found to have predictionerrors 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 predictionof physical properties such as lattice constants and the phonon dispersionrelationship.

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