Calculation update! New properties have been added to the website for dislocation monopole core structures, dynamic relaxes of both crystal and liquid phases, and melting temperatures! Currently, the results for these properties predominately focus on EAM-style potentials, but the results will be updated for other potentials as the associated calculations finish. Feel free to give us feedback on the new properties so we can improve their representations as needed.
Warning! Note that elemental potentials taken from alloy descriptions may not work well for the pure species. This is particularly true if the elements were fit for compounds instead of being optimized separately. As with all interatomic potentials, please check to make sure that the performance is adequate for your problem.
Citation: R.S. Elliott, and A. Akerson (2015), "Efficient "universal" shifted Lennard-Jones model for all KIM API supported species".
Notes: This is the F interaction from the "Universal" parameterization for the openKIM LennardJones612 model driver.The parameterization uses a shifted cutoff so that all interactions have a continuous energy function at the cutoff radius. This model was automatically fit using Lorentz-Berthelotmixing rules. It reproduces the dimer equilibrium separation (covalent radii) and the bond dissociation energies. It has not been fitted to other physical properties and its ability to model structures other than dimers is unknown. See the README and params files on the KIM model page for more details.
Citation: X.W. Zhou, F.P. Doty, and P. Yang (2011), "Atomistic simulation study of atomic size effects on B1 (NaCl), B2 (CsCl), and B3 (zinc-blende) crystal stability of binary ionic compounds", Computational Materials Science50(8), 2470-2481. DOI: 10.1016/j.commatsci.2011.03.028.
Abstract: Ionic compounds exhibit a variety of crystal structures that can critically affect their applications. Traditionally, relative sizes of cations and anions have been used to explain coordination of ions within the crystals. Such approaches assume atoms to be hard spheres and they cannot explain the observed structures of some crystals. Here we develop an atomistic method and use it to explore the structure-determining factors beyond the limitations of the hard sphere approach. Our approach is based upon a calibrated interatomic potential database that uses independent intrinsic bond lengths to measure atomic sizes. By carrying out extensive atomistic simulations, striking relationships among intrinsic bond lengths are discovered to determine the B1 (NaCl), B2 (CsCl), and B3 (zinc-blende) structure of binary ionic compounds.
See Computed Properties Notes: This file was taken from the August 22, 2018 LAMMPS distribution. It is listed as being contributed by Xiaowang Zhou (Sandia) File(s):
Citation: Z. Fan, M.L. Whittaker, and M. Asta (2025), "Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF", Physical Review Materials9(10). DOI: 10.1103/xbfm-clgd.
Abstract: Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan et al., Proc. Natl. Acad. Sci. USA 122, e2425702122 (2025)] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0-100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts.
See Computed Properties Notes: These files were provided by Sergei Starikov on July 15, 2026. The .yaml file is the fitted potential in the original format, while the .yace file is in the LAMMPS-compatible format. The .asi file can be used with pace/extrapolation to perform additional active learning. The link was pulled from the paper and contains training and testing data. File(s):
Citation: Z. Fan, M.L. Whittaker, and M. Asta (2025), "Efficient machine learning interatomic potentials robust for liquid and multiple solid polymorphs of NaF and KF", Physical Review Materials9(10). DOI: 10.1103/xbfm-clgd.
Abstract: Achieving atomic-level understanding of crystallization of molten salts is of importance to a wide range of technological applications. Recent work [Fan et al., Proc. Natl. Acad. Sci. USA 122, e2425702122 (2025)] revealed that crystal nucleation in molten LiF salt is a multistage process according to the molecular-dynamics (MD) simulations based on an atomic cluster expansion (ACE) machine-learning interatomic potential (MLIP). In order to understand the influence of increasing cation size on nucleation pathways and nucleation rates of molten fluoride salts, here we develop two new ACE MLIPs for NaF and KF. The two ACE MLIPs feature DFT-SCAN-level accuracy for liquid and multiple solid polymorphs over a wide temperature (0–2000 K) and pressure (0-100 GPa) range, and also reproduce well a number of experimental data for solid and liquid equilibrium properties. The efficiency of the two ACE MLIPs enable million-atom-scale or microsecond-scale MD simulations. The two general-purpose ACE MLIPs are expected to be useful for atomistic simulations for different purposes, in addition to studying crystallization of molten NaF and KF salts.
See Computed Properties Notes: These files were provided by Sergei Starikov on July 15, 2026. The .yaml file is the fitted potential in the original format, while the .yace file is in the LAMMPS-compatible format. The .asi file can be used with pace/extrapolation to perform additional active learning. The link was pulled from the paper and contains training and testing data. File(s):