Equilibrium and nonequilibrium molecular dynamics simulations of the thermal conductivity of molten alkali halides

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the probability distribution of the off-diagonal elements of a Wishart matrix, we develop a consistent and unbiased estimator for the Seebeck coefficient, whose statistical uncertainty can be arbitrarily reduced in the long-time limit. We assess the efficacy of our method by benchmarking it against extensive equilibrium molecular dynamics simulations conducted on molten CsF using empirical force fields. We then employ this procedure to calculate the Seebeck coefficient of molten NaCl, KCl, and LiCl using neural network force fields trained on ab initio data over a range of pressure-temperature conditions.

Microstructure and mechanical properties of BT/PVTC composite ferroelectric thin films

CONTEXT

Ferroelectric ceramic polymer composites have become the preferred electrocaloric materials due to their light weight and high polarization strength. But the mechanical properties were desired to be improved. In this study, the polyvinylidene fluoride trifluoro ethylene chloride (PVTC) and barium titanate (BT) composites were prepared, and the microstructure and mechanical properties were investigated by molecular dynamics simulations and experiments. It was found that with the increase of BT ceramic content in the composites, the yield stress is significantly reduced, which can be reduced by 16.07%. By comparing with the experimental data, the agglomeration and stress mechanism of the composites were proposed.

METHOD

The microstructure of the composite was analyzed using radial distribution function, self-diffusion coefficient, and glass transition temperature. The agglomeration mechanism of the composite was revealed from the microscopic point of view, and the rationality of the agglomeration behavior was verified by experiments. The calculations were performed by Material Studio 2019 software and the COMPASS force field was adopted.

Recent advances on graphyne and its family members as membrane materials for water purification and desalination

Graphyne and its family members (GFMs) are allotropes of carbon (a class of 2D materials) having unique properties in form of structures, pores and atom hybridizations. Owing to their unique properties, GFMs have been widely utilized in various practical and theoretical applications. In the past decade, GFMs have received considerable attention in the area of water purification and desalination, especially in theoretical and computational aspects. More recently, GFMs have shown greater prospects in achieving optimal separation performance than the experimentally derived commercial polyamide membranes. In this review, recent theoretical and computational advances made in the GFMs research as it relates to water purification and desalination are summarized. Brief details on the properties of GFMs and the commonly used computational methods were described. More specifically, we systematically reviewed the various computational approaches employed with emphasis on the predicted permeability and selectivity of the GFM membranes. Finally, the current challenges limiting their large-scale practical applications coupled with the possible research directions for overcoming the challenges are proposed.

Computational methods to simulate molten salt thermophysical properties

Molten salts are important thermal conductors used in molten salt reactors and solar applications. To use molten salts safely, accurate knowledge of their thermophysical properties is necessary. However, it is experimentally challenging to measure these properties and a comprehensive evaluation of the full chemical space is unfeasible. Computational methods provide an alternative route to access these properties. Here, we summarize the developments in methods over the last 70 years and cluster them into three relevant eras. We review the main advances and limitations of each era and conclude with an optimistic perspective for the next decade, which will likely be dominated by emerging machine learning techniques. This article is aimed to help researchers in peripheral scientific domains understand the current challenges of molten salt simulation and identify opportunities to contribute.

Investigating finite-size effects in molecular dynamics simulations of ion diffusion, heat transport, and thermal motion in superionic materials

The effects of the finite size of the simulation box in equilibrium molecular dynamics simulations are investigated for prototypical superionic conductors of different types, namely, the fluorite-structure materials PbF₂, CaF₂, and UO₂ (type II), and the α phase of AgI (type I). Largely validated empirical force-fields are employed to run ns-long simulations and extract general trends for several properties, at increasing size and in a wide temperature range. This work shows that, for the considered type-II superionic conductors, the diffusivity dramatically depends on the system size and that the superionic regime is shifted to larger temperatures in smaller cells. Furthermore, only simulations of several hundred atoms are able to capture the experimentally observed, characteristic change in the activation energy of the diffusion process, occurring at the order-disorder transition to the superionic regime. Finite-size effects on ion diffusion are instead much weaker in α-AgI. The thermal conductivity is found generally smaller for smaller cells, where the temperature-independent (Allen-Feldman) regime is also reached at significantly lower temperatures. The finite-size effects on the thermal motion of the non-mobile ions composing the solid matrix follow the simple law that holds for solids.

Enhanced Thermal Transport Properties of Graphene/SiC Heterostructures on Nuclear Reactor Cladding Material: A Molecular Dynamics Insight

Owing to the excellent thermal properties of graphene, silicon carbide (SiC) combined with graphene is expected to obtain more outstanding thermal performance and structural stability at high temperatures. Herein, the thermal conductivity of graphene/SiC heterostructures (GS-Hs) with different structures and atomic orientations was calculated through non-equilibrium molecular dynamics (NEMD) simulations. The temperature dependence and size effect on the thermal transport properties of GS-Hs were systematically investigated and discussed. The continuous addition of graphene layers did not always have a positive effect. The thermal transport performance of GS-Hs approached the intrinsic thermal conductivity of SiC when the interaction gradually decreased with the distance between SiC and graphene. Studies on temperature and size dependence show opposite trends. The enhancement effect of graphene was limited at small distances. The thermal conductivity of GS-Hs had a negative correlation with temperature and increased with the system size. Meanwhile, the thermal conductivity of GS-Hs was predicted to be 156.25 (W·m⁻¹·K⁻¹) at the macroscopic scale via extrapolation. The model established in this paper is also applicable to other material simulation processes, as long as the corresponding parameters and potential functions are available. This study will provide inspiration for the optimized design and preparation of highly efficient cladding materials in nuclear reactors.

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

Molten salts have attracted interest as potential heat carriers and/or fuel solvents in the development of new Gen IV nuclear reactor designs, high-temperature batteries, and thermal energy storage. In nuclear engineering, salts containing lithium fluoride-based compounds are of particular interest due to their ability to lower the melting points of mixtures and their compatibility with alloys. A machine learning potential (MLP) combined with a molecular dynamics study is performed on two popular molten salts, namely, LiF (50% Li) and FLiBe (66% LiF and 33% BeF₂), to predict the thermodynamic and transport properties, such as density, diffusion coefficients, thermal conductivity, electrical conductivity, and shear viscosity. Due to the large possibilities of atomic environments, we employ training using Deep Potential Smooth Edition (DPSE) neural networks to learn from large datasets of 141,278 structures with 70 atoms for LiF and 238,610 structures with 91 atoms for FLiBe molten salts. These networks are then deployed in fast molecular dynamics to predict the thermodynamic and transport properties that are only accessible at longer time scales and are otherwise difficult to calculate with classical potentials, ab initio molecular dynamics, or experiments. The prospect of this work is to provide guidance for future works to develop general MLPs for high-throughput thermophysical database generation for a wide spectrum of molten salts.

Coordination and Thermophysical Properties of Transition Metal Chlorocomplexes in LiCl-KCl Eutectic

Eutectic LiCl-KCl molten salt is often used in molten salt reactors as the primary coolant due to its high thermal capacity and high solubility of fission products. Thermophysical properties, such as density, heat capacity, and viscosity, are important parameters for engineering applications of molten salts but may be significantly influenced by metal solutes from corrosion of metallic structural materials. The behavior of the LiCl-KCl eutectic composition is well researched, yet the effects on these properties due to chlorocomplex formation from metals dissolved in the salt are less well known. These properties are often difficult to accurately measure from experimental methods due to the issues arising from the dissolved species, such as volatility. Here, we applied a combination of quantum mechanics molecular dynamics (QM-MD) and deep machine learning force field (DP-FF) molecular dynamics simulations to investigate the structural and thermophysical properties of LiCl-KCl eutectic as well as the influence of dissolved transition metal chlorocomplexes NiCl₂ and CrCl₃ at low concentrations. We find that the dissolution of Ni and Cr in the LiCl-KCl system forms the local tetrahedral (NiCl₄)^2- and octahedral (CrCl₆)^3- chlorocomplexes, respectively, which do not have a significant impact on the overall liquid salt structures. In addition, the thermodynamic properties including diffusion constant and specific heat capacity are not significantly affected by these chlorocomplexes. However, the viscosity significantly increases in the temperature range of 673-773 K. This study thus provides essential information for evaluating the effects of dissolved metals on the thermophysical and transport properties of molten salts.

Invariance principles in the theory and computation of transport coefficients

ABSTRACT

In this work, we elaborate on two recently discovered invariance principles, according to which transport coefficients are, to a large extent, independent of the microscopic definition of the densities and currents of the conserved quantities being transported (energy, momentum, mass, charge). The first such principle, gauge invariance, allows one to define a quantum adiabatic energy current from density-functional theory, from which the heat conductivity can be uniquely defined and computed using equilibrium ab initio molecular dynamics. When combined with a novel topological definition of atomic oxidation states, gauge invariance also sheds new light onto the mechanisms of charge transport in ionic conductors. The second principle, convective invariance, allows one to extend the analysis to multi-component systems. These invariance principles can be combined with new spectral analysis methods for the current time series to be fed into the Green-Kubo formula to obtain accurate estimates of transport coefficients from relatively short molecular dynamics simulations.

Heat and charge transport in H2O at ice-giant conditions from ab initio molecular dynamics simulations

The impact of the inner structure and thermal history of planets on their observable features, such as luminosity or magnetic field, crucially depends on the poorly known heat and charge transport properties of their internal layers. The thermal and electric conductivities of different phases of water (liquid, solid, and super-ionic) occurring in the interior of ice giant planets, such as Uranus or Neptune, are evaluated from equilibrium ab initio molecular dynamics, leveraging recent progresses in the theory and data analysis of transport in extended systems. The implications of our findings on the evolution models of the ice giants are briefly discussed.

Theory and Numerical Simulation of Heat Transport in Multicomponent Systems

The thermal conductivity of classical multicomponent fluids is seemingly affected by the intrinsic arbitrariness in the definition of the atomic energies, and it is ill conditioned numerically, when evaluated from the Green-Kubo theory of linear response. To cope with these two problems, we introduce two new concepts: a convective invariance principle for transport coefficients, in the first case, and multivariate cepstral analysis, in the second. A combination of these two concepts allows one to substantially reduce the noise affecting the estimate of the thermal conductivity from equilibrium molecular dynamics, even for one-component systems.

Thermal Diffusion in Binary Mixtures: Transient Behavior and Transport Coefficients from Equilibrium and Nonequilibrium Molecular Dynamics

Equilibrium and nonequilibrium molecular dynamics simulations are combined to compute the full set of coefficients that appear in the phenomenological equations describing thermal transport in a binary mixture subject to a constant thermal gradient. The Dynamical Non-Equilibrium Molecular Dynamics approach (D-NEMD) is employed to obtain the microscopic time evolution of the density and temperature fields, together with that of the mass and energy fluxes. D-NEMD enables one to study not only the steady state, but also the evolution of the fields during the transient that follows the onset of the thermal gradient, up to the establishment of the steady state. This makes it possible to ensure that the system has indeed reached a stationary condition, and to analyze the transient mechanisms and time scales of the mass and energy transport. A local time averaging procedure is applied to each trajectory contributing to the calculation to improve the signal-to-noise ratio in the estimation of the fluxes and to obtain a clear signal with the, relatively limited, statistics available.

Spatial resolution of the electrical conductance of ionic fluids using a Green-Kubo method

We present a Green-Kubo method to spatially resolve transport coefficients in compositionally heterogeneous mixtures. We develop the underlying theory based on well-known results from mixture theory, Irving-Kirkwood field estimation, and linear response theory. Then, using standard molecular dynamics techniques, we apply the methodology to representative systems. With a homogeneous salt water system, where the expectation of the distribution of conductivity is clear, we demonstrate the sensitivities of the method to system size, and other physical and algorithmic parameters. Then we present a simple model of an electrochemical double layer where we explore the resolution limit of the method. In this system, we observe significant anisotropy in the wall-normal vs. transverse ionic conductances, as well as near wall effects. Finally, we discuss extensions and applications to more realistic systems such as batteries where detailed understanding of the transport properties in the vicinity of the electrodes is of technological importance.

Computation of thermodynamic and transport properties to predict thermophoretic effects in an argon-krypton mixture

Thermophoresis is the movement of molecules caused by a temperature gradient. Here we report the results of a study of thermophoresis using non-equilibrium molecular dynamics simulations of a confined argon-krypton fluid subject to two different temperatures at thermostated walls. The resulting temperature profile between the walls is used along with the Soret coefficient to predict the concentration profile that develops across the channel. We obtain the Soret coefficient by calculating the mutual diffusion and thermal diffusion coefficients. We report an appropriate method for calculating the transport coefficients for binary systems, using the Green-Kubo integrals and radial distribution functions obtained from equilibrium molecular dynamics simulations of the bulk fluid. Our method has the unique advantage of separating the mutual diffusion and thermal diffusion coefficients, and calculating the sign and magnitude of their individual contributions to thermophoresis in binary mixtures.

Thermal conductivity of ionic systems from equilibrium molecular dynamics

Thermal conductivities of ionic compounds (NaCl, MgO, Mg(2)SiO(4)) are calculated from equilibrium molecular dynamics simulations using the Green-Kubo method. Transferable interaction potentials including many-body polarization effects are employed. Various physical conditions (solid and liquid states, high temperatures, high pressures) relevant to the study of the heat transport in the Earth's mantle are investigated, for which experimental measures are very challenging. By introducing a frequency-dependent thermal conductivity, we show that important coupled thermoelectric effects occur in the energy conduction mechanism in the case of liquid systems.