× Updated! Potentials that share interactions are now listed as related models.
Citation: R.S. Elliott, and A. Akerson (2015), "Efficient "universal" shifted Lennard-Jones model for all KIM API supported species".

Notes: This is the Cl 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.

See Computed Properties
Notes: Listing found at https://openkim.org.
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 Science, 50(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)
Citation: J. Guo, L. Ward, Y. Babuji, N. Hoyt, M. Williamson, I. Foster, N. Jackson, C. Benmore, and G. Sivaraman (2022), "Composition-transferable machine learning potential for LiCl-KCl molten salts validated by high-energy x-ray diffraction", Physical Review B, 106(1), 014209. DOI: 10.1103/physrevb.106.014209.
Abstract: Unraveling the liquid structure of multicomponent molten salts is challenging due to the difficulty in conducting and interpreting high-temperature diffraction experiments. Motivated by this challenge, we developed composition-transferable Gaussian approximation potential (GAP) for molten LiCl-KCl. A DFT-SCAN accurate GAP is active-learned from only ~1100 training configurations drawn from 10 unique mixture compositions enriched with metadynamics. The GAP-computed structures show strong agreement across high-energy x-ray diffraction experiments, including for a eutectic not explicitly included in model training, thereby opening the possibility of composition discovery.

Notes: This potential was designed for molten LiCl-KCl. The fit did not include any pure LiCl melt training data, so it should be used with caution at ultra low concentrations of LiCl. For pure LiCl, it is recommended to use 2021--Sivaraman-G-Guo-J-Ward-L-et-al--Li-Cl.

Citation: G. Sivaraman, J. Guo, L. Ward, N. Hoyt, M. Williamson, I. Foster, C. Benmore, and N. Jackson (2021), "Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl", The Journal of Physical Chemistry Letters, 12(17), 4278-4285. DOI: 10.1021/acs.jpclett.1c00901.
Abstract: The in silico modeling of molten salts is critical for emerging "carbon-free" energy applications but is inhibited by the cost of quantum mechanically treating the high polarizabilities of molten salts. Here, we integrate configurational sampling using classical force fields with active learning to automate and accelerate the generation of Gaussian approximation potentials (GAP) for molten salts. This methodology reduces the number of expensive ab initio evaluations required for training set generation to O(100), enabling the facile parametrization of a molten LiCl GAP model that exhibits a 19000-fold speedup relative to AIMD. The developed molten LiCl GAP model is applied to sample extended spatiotemporal scales, permitting new physical insights into molten LiCl's coordination structure as well as experimentally validated predictions of structures, densities, self-diffusion constants, and ionic conductivities. The developed methodology significantly lowers the barrier to the in silico understanding and design of molten salts across the periodic table.

Notes: This potential was designed for molten LiCl.

Date Created: October 5, 2010 | Last updated: August 23, 2022