Abstract
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 103× 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.
Highlights
Molten salts have been widely exploited for clean energy applications, such as molten salt reactors (MSRs)[1,2,3] and concentrated solar power (CSP) technologies.[4,5] A major role played by molten salts in these applications is to transfer/store heat that can be subsequently converted into other energy forms
Molten salts are a promising class of ionic liquids for clean energy applications, such as nuclear and solar energy
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)
Summary
Molten salts have been widely exploited for clean energy applications, such as molten salt reactors (MSRs)[1,2,3] and concentrated solar power (CSP) technologies.[4,5] A major role played by molten salts in these applications is to transfer/store heat that can be subsequently converted into other energy forms. Safe and efficient heat transfer and storage require excellent thermophysical and transport properties of clean salts (e.g., melting point, heat capacity, thermal conductivity, viscosity, vapor pressure, diffusivity, etc.).[2] practical concerns, such as stability under extreme conditions,[6] tolerance to impurities,[7] and compatibility with major structural materials,[8] need to be addressed. Due to the high-dimensional nature of materials space, searching for appropriate salt systems and their optimization for various applications remain essential challenges, requiring a deep understanding of the underlying molecular structures, chemistry, and dynamics of relevant molten salts. Expediting the discovery of the desired salt system hidden in a vast materials space generally requires:
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