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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.
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Updated! Potentials that share interactions are now listed as related models.
2022--Guo-J-Ward-L-Babuji-Y-et-al--Li-K-Cl
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.
See Computed PropertiesNotes: These files were provided by Ganesh Sivaraman on August 6, 2022. The link also hosts the potential files and some MD results for the potential.
File(s): Link(s):
Date Created: October 5, 2010 | Last updated: August 23, 2022