TY - JOUR
T1 - Automated development of molten salt machine learning potentials
T2 - Application to LiCl
AU - Sivaraman, Ganesh
AU - Guo, Jicheng
AU - Ward, Logan
AU - Hoyt, Nathaniel
AU - Williamson, Mark
AU - Foster, Ian
AU - Benmore, Chris
AU - Jackson, Nicholas
N1 - 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 for 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. G.S. thanks Dr. Anand Narayanan Krishnamoorthy for fruitful discussions on ionic liquids and Samuel Tovey for implementing the Green-Kubo ionic conductivity for the NaCl study. J.G. thanks Dr. Zhi-Gang Mei for discussions on AIMD simulations.
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 for 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. G.S. thanks Dr. Anand Narayanan Krishnamoorthy for fruitful discussions on ionic liquids and Samuel Tovey for implementing the Green–Kubo ionic conductivity for the NaCl study. J.G. thanks Dr. Zhi-Gang Mei for discussions on AIMD simulations.
PY - 2021/5/6
Y1 - 2021/5/6
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.jpclett.1c00901
DO - 10.1021/acs.jpclett.1c00901
M3 - Article
C2 - 33908789
AN - SCOPUS:85106143469
SN - 1948-7185
VL - 12
SP - 4278
EP - 4285
JO - Journal of Physical Chemistry Letters
JF - Journal of Physical Chemistry Letters
IS - 17
ER -