Thermodynamic and Transport Properties of LiF and FLiBe Molten Salts with Deep Learning Potentials

Rodriguez, Alejandro; Lam, Stephen T.; Hu, Ming

doi:10.1021/acsami.1c17942

Cited by 56 publications

(57 citation statements)

References 43 publications

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“…Other DPs were developed to calculate transport properties of silicate in the mantle [144][145][146]. DPs were also employed in large-scale calculations of thermodynamic, transport, and structural properties in different molten salts [149][150][151][152][153][154][155][156][157].…”

Section: Multi-element Bulk Systemsmentioning

confidence: 99%

Deep potentials for materials science

Zhang²,

et al. 2022

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To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions of atomic interactions has emerged and been widely applied; i.e., machine learning potentials (MLPs). One recently developed type of MLP is the Deep Potential (DP) method. In this review, we provide an introduction to DP methods in computational materials science. The theory underlying the DP method is presented along with a step-by-step introduction to their development and use. We also review materials applications of DPs in a wide range of materials systems. The DP Library provides a platform for the development of DPs and a database of extant DPs. We discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

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Section: Multi-element Bulk Systemsmentioning

confidence: 99%

Deep potentials for materials science

Zhang²,

et al. 2022

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“…While it suggests that a similar re-parameterization of RIM parameters for the LiF-NaF-ZrF 4 salt system while implicitly including the dispersion effects may lead to obtaining an accurate short to the intermediate-range salt structure, the transferability of the refitted force-field parameters would be compromised. In quest to obtain an AIMD-accurate salt structure in comparatively efficient molecular dynamics simulations, recent success of AIMDtrained neural network potentials for salts containing divalent Be 2+ cations [32,33] also provides motivation for the use of machine-learned force-field for ZrF 4 molten salts.…”

Section: Intermediate-range Fluorozirconate Chain Formationmentioning

confidence: 99%

Short- to Intermediate-Range Structure, Transport, and Thermophysical Properties of LiF–NaF–ZrF4 Molten Salts

2022

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LiF–NaF–ZrF4 multicomponent molten salts are identified as promising candidates for coolant salts in molten salt reactors and advanced high-temperature reactors. This study focused on low-melting point salt compositions of interest: 38LiF–51NaF–11ZrF4, 42LiF–29NaF–29ZrF4, and 26LiF–37NaF–37ZrF4. Ab-initio molecular dynamics (AIMD) calculations were performed and compared with available experimental data to assess the ability of rigid ion models (RIM) to reproduce short- to intermediate-range structure, transport, and thermophysical properties of the LiF–NaF–ZrF4 salt mixtures. It is found that as ZrF4 mol% increases, the average cation–anion coordination number (CN) of monovalent cations (Li+, Na+) obtained from RIM calculations decreases, while multivalent Zr4+ CN varied from 15% to 19% in comparison to corresponding AIMD values. In addition, RIM is found to predict the existence of 7, 8, and 9 coordinated fluorozirconate complexes, while AIMD and the available experimental data showed an occurrence of 6, 7, and 8 coordinated complexes in the melt. The intermediate-range structure analysis revealed that while the RIM parameters are able to reproduce a local structure for lower ZrF4 mol% salts such as in 38LiF–51NaF–11ZrF4, an extensive fluorozirconate network formation is observed in RIM simulations for higher ZrF4 mol% compositions. The network generated by RIM parameters is found to be mainly connected by “corner-sharing” fluorozirconate complexes as opposed to both “edge-sharing” and “corner-sharing” connectively portrayed by AIMD. It is found that a close agreement between AIMD and the RIM salt structure for the 11-mol% ZrF4 salt resulted in good agreement in the calculated Zr diffusivities and the viscosity values. However, due to the inaccurate short- to intermediate-range structure prediction by RIM for higher ZrF4 mol% compositions, thermophysical properties such as densities and heat capacity differ by up to 26% and 27%, respectively, upon comparison with AIMD and experimental values. Also, the network-dominated properties such as diffusion coefficients and viscosities differed by up to two and three orders of magnitude, respectively. This study signifies the importance of accurate salt structure generation for an accurate prediction of transport and thermophysical properties of multicomponent molten salts.

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“…Other DPs were developed to calculate transport properties of silicate in the mantle [151][152][153]. DPs were also employed in large-scale calculations of thermodynamic, transport, and structural properties in different molten salts [156][157][158][159][160][161][162][163][164].…”

Section: B Multi-element Bulk Systemsmentioning

confidence: 99%