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LiCl_JPCL.pdf (1.55 MB)

Automated Development of Molten Salt Machine Learning Potentials: Application to LiCl

submitted on 17.03.2021, 16:13 and posted on 18.03.2021, 13:06 by ganesh sivaraman, Jicheng Guo, Logan Ward, Nathaniel Hoyt, Mark Williamson, Ian Foster, Chris Benmore, Nicholas Jackson

The in silico modeling of molten salts is of crucial importance to emerging "carbon free" energy applications, but is inhibited by the computational cost of quantum mechanically treating the high polarizabilities characteristic of molten salts. Here, we integrate configurational sampling using classical force-fields with active learning to automate the generation of near-DFT accurate machine learning Gaussian Approximation Potentials (GAP) for molten LiCl using fewer than 600 atomic configurations. Relative to conventional ab initio molecular dynamics, the molten LiCl GAP model exhibits a 19,000x speedup and improved experimental agreement as gauged by calculated R-factors. The accuracy of the GAP parametrization workflow is validated by its ability to reproduce experimental structure factors, densities, self-diffusion coefficients, and ionic conductivities for molten LiCl. This hybrid simulation strategy significantly accelerates the generation of machine learning potentials for molten salts by reducing the expensive ab initio calculations required for parameterization to O(100) evaluations, enabling the facile generation of first-principles quality predictions of structural and dynamical properties of molten salts.


This material is based upon work supported by Laboratory Directed Research and Development (LDRD-2020-0226, LDRD-CLS-1-630) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.

This research was supported in part by the Exascale Computing Project (17-SC-20-SC) of the U.S. Department of Energy (DOE), by DOE’s Advanced Scientific Research Office (ASCR) under contract DE-AC02-06CH11357.

We gratefully acknowledge the computing resources provided on Bebop; a high-performance computing cluster operated by the Laboratory Computing Resource Center at Argonne National Laboratory.

HEXRD measurements were made on beamline 6-ID-D at the Advanced Photon Source, a U.S. Department of Energy (DOE) Office of Science User Facility operated for the DOE Office of Science by Argonne National Laboratory under Contract No. DE-AC02-06CH11357. Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, under contract DE-AC02-06CH11357.


Email Address of Submitting Author


Argonne National Laboratory



ORCID For Submitting Author


Declaration of Conflict of Interest

There are no conflicts to declare.