posted on 2021-04-29, 08:41authored byGanesh Sivaraman, Jicheng Guo, Logan Ward, Nathaniel Hoyt, Mark Williamson, Ian Foster, Chris Benmore, Nicholas Jackson
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 19 000-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.