期刊
CELL REPORTS PHYSICAL SCIENCE
卷 2, 期 3, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.xcrp.2021.100359
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类别
资金
- Department of Energy [DE-NE0008751]
- Office of Naval Research Multidisciplinary University Research Initiative Award [ONR N0001418-1-2497]
- DOE [DE-SC0019300]
- FAS Division of Science Research Computing Group at Harvard University
Molten salts are a type of ionic liquids used in clean energy applications, and artificial neural networks can accurately model their properties, achieving high efficiency in atomistic simulations.
Molten salts are a promising class of ionic liquids for clean energy applications, such as nuclear and solar energy. However, efficient and accurate evaluation of salt properties from a fundamental, microscopic perspective remains a challenge. Here, we apply artificial neural networks to atomistic modeling of molten NaCl to accurately reproduce the properties from ab initio quantum mechanical calculations based on density functional theory (DFT). The obtained neural network interatomic potential (NNIP) effectively captures the effects of both long-range and short-range interactions, which are crucial for modeling ionic liquids. Extensive validations suggest that the NNIP is capable of predicting the structural, thermophysical, and transport properties of molten NaCl as well as properties of crystalline NaCl, demonstrating near-DFT accuracy and 10(3) x higher efficiency in atomistic simulations. This application of NNIP suggests a paradigm shift from empirical/semiempirical/ab initio approaches to an efficient and accurate machine learning scheme in molten salt modeling.
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