{
    "content-other-locations" "https://arxiv.org/abs/1706.09122\nhttps://journals.aps.org/prmaterials/abstract/10.1103/PhysRevMaterials.1.043603" 
    "contributor-id" "b0f22d8c-cf78-4215-b781-c110020850a3" 
    "description" "A spectral neighbor analysis potential for Mo. The potential is trained against diverse and large materials data, including bulk bcc Mo, strained bcc Mo, ab-initio molecular dynamics (AIMD) simulated random structures, melted structures, vacancy-containing structures, surfaces, grain boundaries, strained melted structures. The potential gives accurate predictions of structural energies, forces, stresses, elasticity, lattice parameters, vacancy migration barrier, equation-of-state, phonon, free energies, melting point, surface energies, and grain boundary energies." 
    "developer" [
        "e88a6256-7ac1-4ccf-834f-ec3ce55075b6" 
        "37acc91d-c3e6-4b04-b47b-5d07f2afa8ad" 
        "eca34712-4e7b-4b53-b94b-f56928bd1126" 
        "48d590b5-bf20-4376-a1b2-d5464b39962d" 
        "b0f22d8c-cf78-4215-b781-c110020850a3" 
        "40313cf5-d4a5-489e-8e22-2d6e9c97176e"
    ] 
    "disclaimer" "This potential is designed for Mo bcc systems.  It is not appropriate for other elements. The potential was trained using LAMMPS version 17Nov2016. Newer LAMMPS may see energy differences, but the relative values should remain to be the same." 
    "doi" "10.25950/63ad82cb" 
    "domain" "openkim.org" 
    "executables" [] 
    "extended-id" "SNAP_ChenDengTran_2017_Mo__MO_698578166685_000" 
    "kim-api-version" "2.0" 
    "maintainer-id" "b0f22d8c-cf78-4215-b781-c110020850a3" 
    "model-driver" "SNAP__MD_536750310735_000" 
    "potential-type" "snap" 
    "publication-year" "2020" 
    "source-citations" [
        {
            "author" "Chen, Chi and Deng, Zhi and Tran, Richard and Tang, Hanmei and Chu, Iek-Heng and Ong, Shyue Ping" 
            "doi" "10.1103/PhysRevMaterials.1.043603" 
            "journal" "Physical Review Materials" 
            "number" "4" 
            "pages" "043603" 
            "recordkey" "MO_698578166685_000a" 
            "recordprimary" "recordprimary" 
            "recordtype" "article" 
            "title" "Accurate Force Field for Molybdenum by Machine Learning Large Materials Data" 
            "volume" "1" 
            "year" "2017"
        }
    ] 
    "species" [
        "Mo"
    ] 
    "title" "A spectral neighbor analysis potential for Mo developed by Chi Chen (2019) v000"
}