{
    "content-origin" "https://www.ctcms.nist.gov/potentials/entry/2015--Botu-V-Ramprasad-R--Al/" 
    "contributor-id" "4ad03136-ed7f-4316-b586-1e94ccceb311" 
    "description" "This is an AGNI potential for Aluminum. AGNI potentials are machine learning potentials designed to directly reproduce forces and therefore do not directly compute atomic energies." 
    "developer" [
        "88a3fce6-f0f8-4f0d-9959-182fabd1e8c3" 
        "30feeff0-e5cf-46ee-b318-7d9a9543464e"
    ] 
    "doi" "10.25950/c4a647d9" 
    "domain" "openkim.org" 
    "executables" [] 
    "extended-id" "Sim_LAMMPS_AGNI_BotuRamprasad_2015_Al__SM_526060833691_000" 
    "funding" [
        {
            "award-number" "00014-14-1-0098" 
            "funder-identifier" "https://doi.org/10.13039/100000006" 
            "funder-identifier-type" "Crossref Funder ID" 
            "funder-name" "Office of Naval Research" 
            "scheme-uri" "http://doi.org/"
        }
    ] 
    "kim-api-version" "2.2" 
    "maintainer-id" "4ad03136-ed7f-4316-b586-1e94ccceb311" 
    "potential-type" "agni" 
    "publication-year" "2022" 
    "run-compatibility" "portable-models" 
    "simulator-name" "LAMMPS" 
    "simulator-potential" "agni" 
    "source-citations" [
        {
            "author" "Botu, V. and Ramprasad, R." 
            "doi" "10.1103/PhysRevB.92.094306" 
            "issue" "9" 
            "journal" "Phys. Rev. B" 
            "month" "sep" 
            "numpages" "5" 
            "pages" "094306" 
            "publisher" "American Physical Society" 
            "recordkey" "SM_526060833691_000a" 
            "recordprimary" "recordprimary" 
            "recordtype" "article" 
            "title" "Learning scheme to predict atomic forces and accelerate materials simulations" 
            "url" "https://link.aps.org/doi/10.1103/PhysRevB.92.094306" 
            "volume" "92" 
            "year" "2015"
        }
    ] 
    "species" [
        "Al"
    ] 
    "title" "LAMMPS AGNI potential for Al developed by Botu and Ramprasad (2015) v000"
}