{"contributor-id" "2bc2738c-f70a-4eb1-b180-67562ea7e4d6"
 "description" "Li cubic spline potential fit to the mlearn Li training set using KLIFF. The training set contained ground-state crystal, strained, and slab configurations, as well as configurations taken from NVT AIMD simulations at 300 K and 0.5×, 0.9×, 1.5×, and 2.0× the melting point, with and without a single vacancy."
 "developer" ["2bc2738c-f70a-4eb1-b180-67562ea7e4d6"
              "84f67c76-08c3-4e0e-97bd-57a5e4125439"]
 "disclaimer" "This potential is designed for Li bcc systems."
 "doi" "10.25950/af92efa7"
 "domain" "openkim.org"
 "executables" []
 "extended-id" "MEAM_LAMMPS_FuemmelerVita_2023_Li__MO_386038428339_000"
 "kim-api-version" "2.2"
 "maintainer-id" "2bc2738c-f70a-4eb1-b180-67562ea7e4d6"
 "model-driver" "MEAM_LAMMPS__MD_249792265679_002"
 "potential-type" "meam"
 "publication-year" "2023"
 "source-citations" [{"author" "Zuo, Yunxing and Chen, Chi and Li, Xiangguo and Deng, Zhi and Chen, Yiming and Behler, Jörg and Csányi, Gábor and Shapeev, Alexander V. and Thompson, Aidan P. and Wood, Mitchell A. and Ong, Shyue Ping"
                      "doi" "10.1021/acs.jpca.9b08723"
                      "eprint" "  https://doi.org/10.1021/acs.jpca.9b08723 "
                      "journal" "The Journal of Physical Chemistry A"
                      "note" "PMID: 31916773"
                      "number" "4"
                      "pages" "731-745"
                      "recordkey" "MO_386038428339_000a"
                      "recordtype" "article"
                      "title" "Performance and Cost Assessment of Machine Learning Interatomic Potentials"
                      "url" "  https://doi.org/10.1021/acs.jpca.9b08723 "
                      "volume" "124"
                      "year" "2020"}]
 "species" ["Li"]
 "title" "MEAM spline potential for Li developed by Fuemmeler and Vita (2023) v000"}