QUIP GAP potential developed by Sivaraman et al. for modeling molten LiCl salt (2021) v000

Open Knowledgebase of Interatomic Models (OpenKIM) 2026 Open access

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Authors: Ganesh Sivaraman, Jicheng Guo, Logan Ward, Nathaniel C. Hoyt, Mark Williamson, Ian Foster, Chris J. Benmore, Nicholas Jackson
DOI: 10.25950/2837c625
Cited by: 0

Abstract

QUIP Gaussian approximation potentials potential developed by Sivaraman et al. for modeling molten LiCl salt. Integrates configurational sampling using classical force fields with active learning. Developed to model molten LiCl’s coordination structure as well as predictions of densities, self-diffusion constants, and ionic conductivities. Trained on DFT data using an active-learning approach.

Fields of Study & Subjects

Machine Learning in Materials Science Molten salt chemistry and electrochemical processes Thermodynamic and Structural Properties of Metals and Alloys

Publication details

Journal / source
Open Knowledgebase of Interatomic Models (OpenKIM)
Publication date
1 January 2026
Language
EN