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\begin{document} |
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\title{Velocity Shearing and Scaling RNEMD: a minimally perturbing |
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method for producing temperature and momentum gradients} |
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\author{Shenyu Kuang and J. Daniel |
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Gezelter\footnote{Corresponding author. \ Electronic mail: gezelter@nd.edu} \\ |
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Department of Chemistry and Biochemistry,\\ |
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University of Notre Dame\\ |
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Notre Dame, Indiana 46556} |
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\date{\today} |
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\maketitle |
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\begin{doublespace} |
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\begin{abstract} |
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We present a new method for introducing stable nonequilibrium |
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velocity and temperature gradients in molecular dynamics simulations |
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of heterogeneous systems. This method conserves both the linear |
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momentum and total energy of the system and improves previous |
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reverse non-equilibrium molecular dynamics (RNEMD) methods while |
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retaining equilibrium thermal velocity distributions in each region |
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of the system. The new method avoids the thermal anisotropy |
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produced by previous methods by using isotropic velocity scaling and |
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shearing on all of the molecules in specific regions. To test the |
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method, we have computed the thermal conductivity and shear |
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viscosity of model liquid systems as well as the interfacial |
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friction coeefficients of a series of solid / liquid interfaces. The |
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method's ability to combine the thermal and momentum gradients |
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allows us to obtain shear viscosity data for a range of temperatures |
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from a single trajectory. |
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\end{abstract} |
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\newpage |
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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% BODY OF TEXT |
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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\section{Introduction} |
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One of the standard ways to compute transport coefficients such as the |
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viscosities and thermal conductivities of liquids is to use |
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imposed-flux non-equilibrium molecular dynamics |
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methods.\cite{MullerPlathe:1997xw,ISI:000080382700030,kuang:164101} |
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These methods establish stable non-equilibrium conditions in a |
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simulation box using an applied momentum or thermal flux between |
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different regions of the box. The corresponding temperature or |
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velocity gradients which develop in response to the applied flux is |
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related (via linear response theory) to the transport coefficient of |
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interest. These methods are quite efficient, in that they do not need |
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many trajectories to provide information about transport properties. To |
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date, they have been utilized in computing thermal and mechanical |
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transfer of both homogeneous liquids as well as heterogeneous systems |
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such as solid-liquid |
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interfaces.\cite{garde:nl2005,garde:PhysRevLett2009,kuang:AuThl} |
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|
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The reverse non-equilibrium molecular dynamics (RNEMD) methods utilize |
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additional constraints that ensure conservation of linear momentum and |
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total energy of the system while imposing the desired flux. The RNEMD |
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methods are therefore capable of sampling various thermodynamically |
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relevent ensembles, including the micro-canonical (NVE) ensemble, |
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without resorting to an external thermostat. The original approaches |
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proposed by M\"{u}ller-Plathe {\it et |
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al.}\cite{MullerPlathe:1997xw,ISI:000080382700030} utilize simple |
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momentum swapping moves for generating energy/momentum fluxes. The |
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swapping moves can also be made compatible with particles of different |
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identities. Although the swapping moves are simple to implement in |
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molecular simulations, Tenney and Maginn have shown that they create |
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nonthermal velocity distributions.\cite{Maginn:2010} Furthermore, this |
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approach is not particularly efficient for kinetic energy transfer |
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between particles of different identities, particularly when the mass |
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difference between the particles becomes significant. This problem |
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makes applying swapping-move RNEMD methods on heterogeneous |
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interfacial systems somewhat difficult. |
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|
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Recently, we developed a somewhat different approach to applying |
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thermal fluxes in RNEMD simulation using a Non-Isotropic Velocity |
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Scaling (NIVS) algorithm.\cite{kuang:164101} This algorithm scales all |
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atomic velocity vectors in two separate regions of a simulated system |
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using two diagonal scaling matrices. The scaling matrices are |
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determined by solving single quartic equation which includes linear |
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momentum and kinetic energy conservation constraints as well as the |
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target thermal flux between the regions. The NIVS method is able to |
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effectively impose a wide range of kinetic energy fluxes without |
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significant perturbation to the velocity distributions away from ideal |
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Maxwell-Boltzmann distributions, even in the presence of heterogeneous |
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interfaces. We successfully applied this approach in studying the |
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interfacial thermal conductance at chemically-capped metal-solvent |
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interfaces.\cite{kuang:AuThl} |
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The NIVS approach works very well for preparing stable {\it thermal} |
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gradients. However, as we pointed out in the original paper, it has |
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limited application in imposing {\it linear} momentum fluxes (which |
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are required for measuring shear viscosities). The reason for this is |
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that linear momentum flux was being imposed by scaling random |
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fluctuations of the center of the velocity distributions. Repeated |
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application of the original NIVS approach resulted in temperature |
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anisotropy, i.e. the width of the velocity distributions depended on |
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coordinates perpendicular to the desired gradient direction. For this |
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reason, combining thermal and momentum fluxes was difficult with the |
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original NIVS algorithm. However, combinations of thermal and |
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velocity gradients would provide a means to simulate thermal-linear |
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coupled processes such as Soret effect in liquid flows. Therefore, |
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developing improved approaches to the scaling imposed-flux methods |
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would be useful. |
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In this paper, we improve the RNEMD methods by introducing a novel |
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approach to creating imposed fluxes. This approach separates the |
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shearing and scaling of the velocity distributions in different |
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spatial regions and can apply both transformations within a single |
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time step. The approach retains desirable features of previous RNEMD |
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approaches and is simpler to implement compared to the earlier NIVS |
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method. In what follows, we first present the Shearing-and-Scaling |
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(SS) RNEMD method and its implementation in a simulation. Then we |
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compare the SS-RNEMD method in bulk fluids to previous methods. We |
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also compute interfacial frictions are computed for a series of |
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heterogeneous interfaces. |
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\section{Methodology} |
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In an approach similar to the earlier NIVS method,\cite{kuang:164101} |
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we consider a periodic system which has been divided into a series of |
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slabs along a single axis (e.g. $z$). The unphysical thermal and |
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momentum fluxes are applied from one of the end slabs to the center |
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slab, and thus the thermal flux produces a higher temperature in the |
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center slab than in the end slab, and the momentum flux results in a |
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center slab moving along the positive $y$ axis (see Fig. \ref{cartoon}). |
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The applied fluxes can be set to negative values if the reverse |
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gradients are desired. For convenience the center slab is denoted as |
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the {\it hot} or {\it ``H''} slab, while the end slab is denoted {\it |
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``C''} (or {\it cold}). |
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\begin{figure} |
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\includegraphics[width=\linewidth]{cartoon} |
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\caption{The SS-RNEMD approach we are introducing imposes unphysical |
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transfer of both momentum and kinetic energy between a ``hot'' slab |
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and a ``cold'' slab in the simulation box. The molecular system |
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responds to this imposed flux by generating both momentum and |
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temperature gradients. The slope of the gradients can then be used |
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to compute transport properties (e.g. shear viscosity and thermal |
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conductivity).} |
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\label{cartoon} |
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\end{figure} |
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To impose these fluxes, we periodically apply a set of operations on |
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the velocities of particles {$i$} within the cold slab and a separate |
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operation on particles {$j$} within the hot slab. |
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\begin{eqnarray} |
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\vec{v}_i & \leftarrow & c\cdot\left(\vec{v}_i - \langle\vec{v}_c |
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\rangle\right) + \left(\langle\vec{v}_c\rangle + \vec{a}_c\right) \\ |
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\vec{v}_j & \leftarrow & h\cdot\left(\vec{v}_j - \langle\vec{v}_h |
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\rangle\right) + \left(\langle\vec{v}_h\rangle + \vec{a}_h\right) |
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\end{eqnarray} |
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where $\langle\vec{v}_c\rangle$ and $\langle\vec{v}_h\rangle$ denote |
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the instantaneous average velocity of the molecules within slabs $C$ |
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and $H$ respectively. When a momentum flux $\vec{j}_z(\vec{p})$ is |
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present, these slab-averaged velocities also get corresponding |
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incremental changes ($\vec{a}_c$ and $\vec{a}_h$ respectively) that |
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are applied to all particles within each slab. The incremental |
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changes are obtained using Newton's second law: |
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\begin{eqnarray} |
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M_c \vec{a}_c & = & -\vec{j}_z(\vec{p}) \Delta t \label{eq:newton1} \\ |
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M_h \vec{a}_h & = & \vec{j}_z(\vec{p}) \Delta t |
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\label{eq:newton2} |
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\end{eqnarray} |
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where $M$ denotes total mass of particles within a slab: |
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\begin{eqnarray} |
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M_c & = & \sum_{i = 1}^{N_c} m_i \\ |
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M_h & = & \sum_{j = 1}^{N_h} m_j |
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\end{eqnarray} |
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and $\Delta t$ is the interval between two separate operations. |
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The operations in Eqs. \ref{eq:newton1} and \ref{eq:newton2} already |
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conserve the linear momentum of a periodic system. To further satisfy |
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total energy conservation as well as to impose the thermal flux $J_z$, |
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the following constraint equations must be solved for the two scaling |
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variables $c$ and $h$: |
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\begin{eqnarray} |
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K_c - J_z\Delta t & = & c^2 (K_c - \frac{1}{2}M_c \langle\vec{v}_c |
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\rangle^2) + \frac{1}{2}M_c (\langle \vec{v}_c \rangle + \vec{a}_c)^2 \\ |
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K_h + J_z\Delta t & = & h^2 (K_h - \frac{1}{2}M_h \langle\vec{v}_h |
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\rangle^2) + \frac{1}{2}M_h (\langle \vec{v}_h \rangle + \vec{a}_h)^2. |
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\label{constraint} |
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\end{eqnarray} |
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Here $K_c$ and $K_h$ denote the translational kinetic energy of slabs |
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$C$ and $H$ respectively. These conservation equations are sufficient |
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to ensure total energy conservation, as the operations applied in our |
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method do not change the kinetic energy related to orientational |
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degrees of freedom or the potential energy of a system (as long as the |
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potential energy is independent of particle velocity). |
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Equations \ref{eq:newton1}-\ref{constraint} are sufficient to |
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determine the velocity scaling coefficients ($c$ and $h$) as well as |
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$\vec{a}_c$ and $\vec{a}_h$. Note that there are usually two roots |
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respectively for $c$ and $h$. However, the positive roots (which are |
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closer to 1) are chosen so that the perturbations to the system are |
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minimal. Figure \ref{method} illustrates the implementation of this |
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algorithm in an individual step. |
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\begin{figure} |
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\centering |
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\includegraphics[width=5in]{method} |
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\caption{Illustration of a single step implementation of the |
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algorithm. Starting with the velocity distributions for the two |
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slabs in a shearing fluid, the transformation is used to apply the |
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effect of both a thermal and a momentum flux from the ``c'' slab to |
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the ``h'' slab. As the figure shows, gaussian distributions are |
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preserved by both the scaling and shearing operations.} |
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\label{method} |
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\end{figure} |
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By implementing these operations at a fixed frequency, stable thermal |
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and momentum fluxes can both be applied and the corresponding |
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temperature and momentum gradients can be established. |
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Compared to the previous NIVS method, the SS-RNEMD approach is |
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computationally simpler in that only quadratic equations are involved |
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to determine a set of scaling coefficients, while the NIVS method |
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required solution of quartic equations. Furthermore, this method |
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implements {\it isotropic} scaling of velocities in respective slabs, |
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unlike NIVS, which required extra flexibility in the choice of scaling |
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coefficients to allow for the energy and momentum constraints. Most |
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importantly, separating the linear momentum flux from velocity scaling |
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avoids the underlying cause of the thermal anisotropy in NIVS. In |
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later sections, we demonstrate that this can improve the calculated |
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shear viscosities from RNEMD simulations. |
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The SS-RNEMD approach has advantages over the original momentum |
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swapping in many respects. In the swapping method, the velocity |
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vectors involved are usually very different (or the generated flux |
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would be quite small), thus the swapping tends to create strong |
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perturbations in the neighborhood of the particles involved. In |
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comparison, the SS approach distributes the flux widely to all |
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particles in a slab so that perturbations in the flux generating |
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region are minimized. Additionally, momentum swapping steps tend to |
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result in nonthermal velocity distributions when the imposed flux is |
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large and diffusion from the neighboring slabs cannot carry momentum |
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away quickly enough. In comparison, the scaling and shearing moves |
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made in the SS approach preserve the shapes of the equilibrium |
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velocity disributions (e.g. Maxwell-Boltzmann). The results presented |
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in later sections will illustrate that this is quite helpful in |
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retaining reasonable thermal distributions in a simulation. |
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|
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\section{Computational Details} |
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The algorithm has been implemented in our MD simulation code, |
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OpenMD.\cite{Meineke:2005gd,openmd} We will first compare the method |
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with previous RNEMD methods and equilibrium MD in homogeneous |
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fluids (Lennard-Jones and SPC/E water). We have also used the new |
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method to simulate the interfacial friction of different heterogeneous |
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interfaces (Au (111) with organic solvents, Au(111) with SPC/E water, |
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and the Ice Ih - liquid water interface). |
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\subsection{Simulation Protocols} |
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The systems we investigated were set up in orthorhombic simulation |
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cells with periodic boundary conditions in all three dimensions. The |
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$z$-axes of these cells were typically quite long and served as the |
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temperature and/or momentum gradient axes. The cells were evenly |
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divided into $N$ slabs along this axis, with $N$ varying for the |
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individual system. The $x$ and $y$ axes were of similar lengths in all |
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simulations. In all cases, before introducing a nonequilibrium method |
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to establish steady thermal and/or momentum gradients, equilibration |
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simulations were run under the canonical ensemble with a Nos\'e-Hoover |
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thermostat\cite{hoover85} followed by further equilibration using |
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standard constant energy (NVE) conditions. For SPC/E water |
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simulations, isobaric-isothermal equilibrations\cite{melchionna93} |
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were performed before equilibration to reach standard densities at |
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atmospheric pressure (1 bar); for interfacial systems, similar |
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equilibrations with anisotropic box relaxations are used to relax the |
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surface tension in the $xy$ plane. |
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While homogeneous fluid systems can be set up with random |
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configurations, interfacial systems are typically prepared with a |
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single crystal face presented perpendicular to the $z$-axis. This |
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crystal face is aligned in the x-y plane of the periodic cell, and the |
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solvent occupies the region directly above and below a crystalline |
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slab. The preparation and equilibration of butanethiol covered |
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Au(111) surfaces, as well as the solvation and equilibration processes |
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used for these interfaces are described in detail in reference |
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\cite{kuang:AuThl}. |
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For the ice / liquid water interfaces, the basal surface of ice |
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lattice was first constructed. Hirsch and Ojam\"{a}e |
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\cite{doi:10.1021/jp048434u} explored the energetics of ice lattices |
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with all possible proton ordered configurations. We utilized Hirsch |
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and Ojam\"{a}e's structure 6 ($P2_12_12_1$) which is an orthorhombic |
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cell giving a proton-ordered version of Ice Ih. The basal face of ice |
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in this unit cell geometry is the $\{0~0~1\}$ face. Although |
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experimental solid/liquid coexistant temperature under normal pressure |
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gezelter |
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are close to 273K, Bryk and Haymet's simulations of ice/liquid water |
321 |
|
|
interfaces with different models suggest that for SPC/E, the most |
322 |
|
|
stable interface is observed at 225$\pm$5K.\cite{bryk:10258} |
323 |
|
|
Therefore, our ice/liquid water simulations were carried out at an |
324 |
|
|
average temperature of 225K. Molecular translation and orientational |
325 |
|
|
restraints were applied in the early stages of equilibration to |
326 |
|
|
prevent melting of the ice slab. These restraints were removed during |
327 |
|
|
NVT equilibration, well before data collection was carried out. |
328 |
gezelter |
3769 |
|
329 |
|
|
\subsection{Force Field Parameters} |
330 |
gezelter |
3786 |
For comparing the SS-RNEMD method with previous work, we retained |
331 |
skuang |
3784 |
force field parameters consistent with previous simulations. Argon is |
332 |
|
|
the Lennard-Jones fluid used here, and its results are reported in |
333 |
gezelter |
3786 |
reduced units for purposes of direct comparison with previous |
334 |
|
|
calculations. |
335 |
gezelter |
3769 |
|
336 |
gezelter |
3786 |
For our water simulations, we utilized the SPC/E model throughout this |
337 |
|
|
work. Previous work for transport properties of SPC/E water model is |
338 |
|
|
available\cite{Bedrov:2000,10.1063/1.3330544,Medina2011} so direct |
339 |
|
|
comparison with previous calculation methods is possible. |
340 |
gezelter |
3769 |
|
341 |
skuang |
3774 |
The Au-Au interaction parameters in all simulations are described by |
342 |
|
|
the quantum Sutton-Chen (QSC) formulation.\cite{PhysRevB.59.3527} The |
343 |
|
|
QSC potentials include zero-point quantum corrections and are |
344 |
gezelter |
3769 |
reparametrized for accurate surface energies compared to the |
345 |
skuang |
3775 |
Sutton-Chen potentials.\cite{Chen90} For gold/water interfaces, the |
346 |
|
|
Spohr potential was adopted\cite{ISI:000167766600035} to depict |
347 |
|
|
Au-H$_2$O interactions. |
348 |
gezelter |
3769 |
|
349 |
skuang |
3784 |
For our gold/organic liquid interfaces, the small organic molecules |
350 |
|
|
included in our simulations are the Au surface capping agent |
351 |
gezelter |
3786 |
butanethiol as well as liquid hexane and liquid toluene. The |
352 |
|
|
United-Atom |
353 |
skuang |
3774 |
models\cite{TraPPE-UA.thiols,TraPPE-UA.alkanes,TraPPE-UA.alkylbenzenes} |
354 |
gezelter |
3786 |
for these components were used in this work for computational |
355 |
skuang |
3774 |
efficiency, while maintaining good accuracy. We refer readers to our |
356 |
|
|
previous work\cite{kuang:AuThl} for further details of these models, |
357 |
|
|
as well as the interactions between Au and the above organic molecule |
358 |
|
|
components. |
359 |
gezelter |
3769 |
|
360 |
skuang |
3774 |
\subsection{Thermal conductivities} |
361 |
gezelter |
3786 |
When the linear momentum flux $\vec{j}_z(\vec{p})$ is set to zero and |
362 |
|
|
the target $J_z$ is non-zero, SS-RNEMD imposes kinetic energy transfer |
363 |
|
|
between the slabs, which can be used for computation of thermal |
364 |
|
|
conductivities. Similar to previous RNEMD methods, we assume that we |
365 |
|
|
are in the linear response regime of the temperature gradient with |
366 |
|
|
respect to the thermal flux. The thermal conductivity ($\lambda$) can |
367 |
|
|
be calculated using the imposed kinetic energy flux and the measured |
368 |
skuang |
3775 |
thermal gradient: |
369 |
|
|
\begin{equation} |
370 |
|
|
J_z = -\lambda \frac{\partial T}{\partial z} |
371 |
|
|
\end{equation} |
372 |
|
|
Like other imposed-flux methods, the energy flux was calculated using |
373 |
|
|
the total non-physical energy transferred (${E_{total}}$) from slab |
374 |
gezelter |
3786 |
``c'' to slab ``h'', which was recorded throughout the simulation, and |
375 |
|
|
the total data collection time $t$: |
376 |
skuang |
3775 |
\begin{equation} |
377 |
gezelter |
3786 |
J_z = \frac{E_{total}}{2 t L_x L_y}. |
378 |
skuang |
3775 |
\end{equation} |
379 |
gezelter |
3786 |
Here, $L_x$ and $L_y$ denote the dimensions of the plane in a |
380 |
skuang |
3775 |
simulation cell perpendicular to the thermal gradient, and a factor of |
381 |
gezelter |
3786 |
two in the denominator is necessary as the heat transport occurs in |
382 |
|
|
both the $+z$ and $-z$ directions. The average temperature gradient |
383 |
skuang |
3775 |
${\langle\partial T/\partial z\rangle}$ can be obtained by a linear |
384 |
|
|
regression of the temperature profile, which is recorded during a |
385 |
|
|
simulation for each slab in a cell. For Lennard-Jones simulations, |
386 |
|
|
thermal conductivities are reported in reduced units |
387 |
|
|
(${\lambda^*=\lambda \sigma^2 m^{1/2} k_B^{-1}\varepsilon^{-1/2}}$). |
388 |
|
|
|
389 |
skuang |
3774 |
\subsection{Shear viscosities} |
390 |
gezelter |
3786 |
Alternatively, when the linear momentum flux $\vec{j}_z(\vec{p})$ is |
391 |
|
|
non-zero in either the $x$ or $y$ directions, the method can be used |
392 |
|
|
to compute the shear viscosity. For isotropic systems, the direction |
393 |
|
|
of $\vec{j}_z(\vec{p})$ on the $xy$ plane should not matter, but the |
394 |
|
|
ability to arbitarily specify the vector direction in our method could |
395 |
|
|
provide convenience when working with anisotropic interfaces. |
396 |
skuang |
3775 |
|
397 |
gezelter |
3786 |
In a manner similar to the thermal conductivity calculations, a linear |
398 |
|
|
response of the momentum gradient with respect to the shear stress is |
399 |
|
|
assumed, and the shear viscosity ($\eta$) can be obtained with the |
400 |
|
|
imposed momentum flux (e.g. in $x$ direction) and the measured |
401 |
|
|
velocity gradient: |
402 |
skuang |
3775 |
\begin{equation} |
403 |
|
|
j_z(p_x) = -\eta \frac{\partial v_x}{\partial z} |
404 |
|
|
\end{equation} |
405 |
|
|
where the flux is similarly defined: |
406 |
|
|
\begin{equation} |
407 |
|
|
j_z(p_x) = \frac{P_x}{2 t L_x L_y} |
408 |
|
|
\end{equation} |
409 |
|
|
with $P_x$ being the total non-physical momentum transferred within |
410 |
skuang |
3784 |
the data collection time. Also, the averaged velocity gradient |
411 |
skuang |
3775 |
${\langle\partial v_x/\partial z\rangle}$ can be obtained using linear |
412 |
skuang |
3784 |
regression of the $x$ component of the mean velocity ($\langle |
413 |
|
|
v_x\rangle$) in each of the bins. For Lennard-Jones simulations, shear |
414 |
|
|
viscosities are also reported in reduced units |
415 |
skuang |
3775 |
(${\eta^*=\eta\sigma^2(\varepsilon m)^{-1/2}}$). |
416 |
|
|
|
417 |
gezelter |
3786 |
Although $J_z$ may be switched off for shear viscosity simulations, |
418 |
|
|
the SS-RNEMD method allows the user the ability to simultaneously |
419 |
|
|
impose both a thermal and a momentum flux during a single |
420 |
|
|
simulation. This can create system with coincident temperature and a |
421 |
|
|
velocity gradients. Since the viscosity is generally a function of |
422 |
|
|
temperature, the local viscosity depends on the local temperature in |
423 |
|
|
the fluid. Therefore, in a single simulation, viscosity at $z$ |
424 |
|
|
(corresponding to a certain $T$) can be computed with the applied |
425 |
|
|
shear flux and the local velocity gradient (which can be obtained by |
426 |
|
|
finite difference approximation). This means that the temperature |
427 |
|
|
dependence of the viscosity can be mapped out in only one |
428 |
|
|
trajectory. Results for shear viscosity computations of SPC/E water |
429 |
|
|
will demonstrate SS-RNEMD's efficiency in this respect. |
430 |
skuang |
3784 |
|
431 |
gezelter |
3786 |
\subsection{Interfacial friction and slip length} |
432 |
|
|
Shear stress creates a velocity gradient within bulk fluid phases, but |
433 |
|
|
at solid-liquid interfaces, the effects of a shear stress depend on |
434 |
|
|
the molecular details of the interface. The interfacial friction |
435 |
|
|
coefficient, $\kappa$, relates the shear stress (e.g. along the |
436 |
|
|
$x$-axis) with the relative fluid velocity tangent to the interface: |
437 |
skuang |
3775 |
\begin{equation} |
438 |
gezelter |
3786 |
j_z(p_x) = \kappa \left[v_x(fluid) - v_x(solid)\right] |
439 |
skuang |
3775 |
\end{equation} |
440 |
gezelter |
3786 |
where $v_x(fluid)$ and $v_x(solid)$ are the velocities measured |
441 |
|
|
directly adjacent to the interface. Under ``stick'' boundary |
442 |
|
|
condition, $\Delta v_x|_\mathrm{interface} \rightarrow 0$, which leads |
443 |
|
|
to $\kappa\rightarrow\infty$. However, for ``slip'' boundary |
444 |
|
|
conditions at a solid-liquid interface, $\kappa$ becomes finite. To |
445 |
|
|
characterize the interfacial boundary conditions, the slip length, |
446 |
|
|
$\delta$, is defined by the ratio of the fluid-phase viscosity to the |
447 |
|
|
friction coefficient of the interface: |
448 |
skuang |
3775 |
\begin{equation} |
449 |
|
|
\delta = \frac{\eta}{\kappa} |
450 |
|
|
\end{equation} |
451 |
gezelter |
3786 |
In ``no-slip'' or ``stick'' boundary conditions, $\delta\rightarrow |
452 |
|
|
0$, and $\delta$ is a measure of how ``slippery'' an interface is. |
453 |
|
|
Figure \ref{slipLength} illustrates how this quantity is defined and |
454 |
|
|
computed for a solid-liquid interface. |
455 |
gezelter |
3769 |
|
456 |
skuang |
3775 |
\begin{figure} |
457 |
|
|
\includegraphics[width=\linewidth]{defDelta} |
458 |
|
|
\caption{The slip length $\delta$ can be obtained from a velocity |
459 |
skuang |
3785 |
profile of a solid-liquid interface simulation, when a momentum flux |
460 |
gezelter |
3786 |
is applied. The data shown is for a simulated Au/hexane interface. |
461 |
|
|
The Au crystalline region is moving as a block (lower dots), while |
462 |
|
|
the measured velocity gradient in the hexane phase is discontinuous |
463 |
|
|
a the interface.} |
464 |
skuang |
3775 |
\label{slipLength} |
465 |
|
|
\end{figure} |
466 |
gezelter |
3769 |
|
467 |
gezelter |
3786 |
Since the method can be applied for interfaces as well as for bulk |
468 |
|
|
materials, the shear stress is applied in an identical manner to the |
469 |
|
|
shear viscosity computations, e.g. by applying an unphysical momentum |
470 |
|
|
flux, $j_z(\vec{p})$. With the correct choice of $\vec{p}$ in the |
471 |
|
|
$x-y$ plane, one can compute friction coefficients and slip lengths |
472 |
|
|
for a number of different dragging vectors on a given slab. The |
473 |
|
|
corresponding velocity profiles can be obtained as shown in Figure |
474 |
|
|
\ref{slipLength}, in which the velocity gradients within the liquid |
475 |
|
|
phase and the velocity difference at the liquid-solid interface can be |
476 |
|
|
easily measured from saved simulation data. |
477 |
skuang |
3775 |
|
478 |
skuang |
3776 |
\section{Results and Discussions} |
479 |
|
|
\subsection{Lennard-Jones fluid} |
480 |
gezelter |
3786 |
Our orthorhombic simulation cell for the Lennard-Jones fluid has |
481 |
|
|
identical parameters to our previous work\cite{kuang:164101} to |
482 |
|
|
facilitate comparison. Thermal conductivities and shear viscosities |
483 |
|
|
were computed with the new algorithm applied to the simulations. The |
484 |
|
|
results of thermal conductivity are compared with our previous NIVS |
485 |
|
|
algorithm. However, since the NIVS algorithm produced temperature |
486 |
|
|
anisotropy for shear viscocity computations, these results are instead |
487 |
|
|
compared to the previous momentum swapping approaches. Table \ref{LJ} |
488 |
|
|
lists these values with various fluxes in reduced units. |
489 |
skuang |
3770 |
|
490 |
skuang |
3776 |
\begin{table*} |
491 |
|
|
\begin{minipage}{\linewidth} |
492 |
|
|
\begin{center} |
493 |
gezelter |
3769 |
|
494 |
skuang |
3776 |
\caption{Thermal conductivity ($\lambda^*$) and shear viscosity |
495 |
|
|
($\eta^*$) (in reduced units) of a Lennard-Jones fluid at |
496 |
|
|
${\langle T^* \rangle = 0.72}$ and ${\rho^* = 0.85}$ computed |
497 |
|
|
at various momentum fluxes. The new method yields similar |
498 |
|
|
results to previous RNEMD methods. All results are reported in |
499 |
|
|
reduced unit. Uncertainties are indicated in parentheses.} |
500 |
|
|
|
501 |
|
|
\begin{tabular}{cccccc} |
502 |
|
|
\hline\hline |
503 |
|
|
\multicolumn{2}{c}{Momentum Exchange} & |
504 |
|
|
\multicolumn{2}{c}{$\lambda^*$} & |
505 |
|
|
\multicolumn{2}{c}{$\eta^*$} \\ |
506 |
|
|
\hline |
507 |
gezelter |
3786 |
Swap Interval & Equivalent $J_z^*$ or $j_z^*(p_x)$ & |
508 |
skuang |
3785 |
NIVS\cite{kuang:164101} & This work & Swapping & This work \\ |
509 |
skuang |
3776 |
\hline |
510 |
|
|
0.116 & 0.16 & 7.30(0.10) & 6.25(0.23) & 3.57(0.06) & 3.53(0.16)\\ |
511 |
|
|
0.232 & 0.09 & 6.95(0.09) & 8.02(0.56) & 3.64(0.05) & 3.43(0.17)\\ |
512 |
|
|
0.463 & 0.047 & 7.19(0.07) & 6.48(0.15) & 3.52(0.16) & 3.51(0.08)\\ |
513 |
|
|
0.926 & 0.024 & 7.19(0.28) & 7.02(0.13) & 3.72(0.05) & 3.79(0.11)\\ |
514 |
|
|
1.158 & 0.019 & 7.98(0.33) & 7.37(0.23) & 3.42(0.06) & 3.53(0.06)\\ |
515 |
|
|
\hline\hline |
516 |
|
|
\end{tabular} |
517 |
|
|
\label{LJ} |
518 |
|
|
\end{center} |
519 |
|
|
\end{minipage} |
520 |
|
|
\end{table*} |
521 |
gezelter |
3769 |
|
522 |
skuang |
3776 |
\subsubsection{Thermal conductivity} |
523 |
|
|
Our thermal conductivity calculations with this method yields |
524 |
|
|
comparable results to the previous NIVS algorithm. This indicates that |
525 |
skuang |
3785 |
the thermal gradients introduced using this method are also close to |
526 |
skuang |
3776 |
previous RNEMD methods. Simulations with moderately higher thermal |
527 |
|
|
fluxes tend to yield more reliable thermal gradients and thus avoid |
528 |
|
|
large errors, while overly high thermal fluxes could introduce side |
529 |
|
|
effects such as non-linear temperature gradient response or |
530 |
|
|
inadvertent phase transitions. |
531 |
gezelter |
3769 |
|
532 |
skuang |
3776 |
Since the scaling operation is isotropic in this method, one does not |
533 |
|
|
need extra care to ensure temperature isotropy between the $x$, $y$ |
534 |
skuang |
3785 |
and $z$ axes, while for NIVS, thermal anisotropy might happen if the |
535 |
|
|
criteria function for choosing scaling coefficients does not perform |
536 |
|
|
as expected. Furthermore, this method avoids inadvertent concomitant |
537 |
skuang |
3776 |
momentum flux when only thermal flux is imposed, which could not be |
538 |
|
|
achieved with swapping or NIVS approaches. The thermal energy exchange |
539 |
skuang |
3778 |
in swapping ($\vec{p}_i$ in slab ``c'' with $\vec{p}_j$ in slab ``h'') |
540 |
skuang |
3776 |
or NIVS (total slab momentum conponemts $P^\alpha$ scaled to $\alpha |
541 |
skuang |
3785 |
P^\alpha$) would not achieve this effect unless thermal flux vanishes |
542 |
|
|
(i.e. $\vec{p}_i=\vec{p}_j$ or $\alpha=1$ which do not contribute to |
543 |
gezelter |
3786 |
applying a thermal flux). In this sense, this method aids in achieving |
544 |
skuang |
3785 |
minimal perturbation to a simulation while imposing a thermal flux. |
545 |
skuang |
3776 |
|
546 |
|
|
\subsubsection{Shear viscosity} |
547 |
skuang |
3785 |
Table \ref{LJ} also compares our shear viscosity results with the |
548 |
|
|
momentum swapping approach. Our calculations show that our method |
549 |
|
|
predicted similar values of shear viscosities to the momentum swapping |
550 |
skuang |
3776 |
approach, as well as the velocity gradient profiles. Moderately larger |
551 |
|
|
momentum fluxes are helpful to reduce the errors of measured velocity |
552 |
|
|
gradients and thus the final result. However, it is pointed out that |
553 |
|
|
the momentum swapping approach tends to produce nonthermal velocity |
554 |
|
|
distributions.\cite{Maginn:2010} |
555 |
|
|
|
556 |
|
|
To examine that temperature isotropy holds in simulations using our |
557 |
|
|
method, we measured the three one-dimensional temperatures in each of |
558 |
|
|
the slabs (Figure \ref{tempXyz}). Note that the $x$-dimensional |
559 |
skuang |
3785 |
temperatures were calculated after subtracting the contribution from |
560 |
|
|
bulk velocities of the slabs. The one-dimensional temperature profiles |
561 |
skuang |
3776 |
showed no observable difference between the three dimensions. This |
562 |
|
|
ensures that isotropic scaling automatically preserves temperature |
563 |
|
|
isotropy and that our method is useful in shear viscosity |
564 |
|
|
computations. |
565 |
|
|
|
566 |
gezelter |
3769 |
\begin{figure} |
567 |
skuang |
3776 |
\includegraphics[width=\linewidth]{tempXyz} |
568 |
skuang |
3777 |
\caption{Unlike the previous NIVS algorithm, the new method does not |
569 |
|
|
produce a thermal anisotropy. No temperature difference between |
570 |
|
|
different dimensions were observed beyond the magnitude of the error |
571 |
|
|
bars. Note that the two ``hotter'' regions are caused by the shear |
572 |
|
|
stress, as reported by Tenney and Maginn\cite{Maginn:2010}, but not |
573 |
|
|
an effect that only observed in our methods.} |
574 |
skuang |
3776 |
\label{tempXyz} |
575 |
gezelter |
3769 |
\end{figure} |
576 |
|
|
|
577 |
skuang |
3776 |
Furthermore, the velocity distribution profiles are tested by imposing |
578 |
|
|
a large shear stress into the simulations. Figure \ref{vDist} |
579 |
|
|
demonstrates how our method is able to maintain thermal velocity |
580 |
|
|
distributions against the momentum swapping approach even under large |
581 |
|
|
imposed fluxes. Previous swapping methods tend to deplete particles of |
582 |
|
|
positive velocities in the negative velocity slab (``c'') and vice |
583 |
skuang |
3785 |
versa in slab ``h'', where the distributions leave notchs. This |
584 |
skuang |
3776 |
problematic profiles become significant when the imposed-flux becomes |
585 |
|
|
larger and diffusions from neighboring slabs could not offset the |
586 |
skuang |
3785 |
depletions. Simutaneously, abnormal peaks appear corresponding to |
587 |
|
|
excessive particles having velocity swapped from the other slab. These |
588 |
|
|
nonthermal distributions limit applications of the swapping approach |
589 |
|
|
in shear stress simulations. Our method avoids the above problematic |
590 |
skuang |
3776 |
distributions by altering the means of applying momentum |
591 |
|
|
fluxes. Comparatively, velocity distributions recorded from |
592 |
|
|
simulations with our method is so close to the ideal thermal |
593 |
skuang |
3785 |
prediction that no obvious difference is shown in Figure |
594 |
|
|
\ref{vDist}. Conclusively, our method avoids problems that occurs in |
595 |
skuang |
3776 |
previous RNEMD methods and provides a useful means for shear viscosity |
596 |
|
|
computations. |
597 |
gezelter |
3769 |
|
598 |
skuang |
3776 |
\begin{figure} |
599 |
|
|
\includegraphics[width=\linewidth]{velDist} |
600 |
skuang |
3777 |
\caption{Velocity distributions that develop under the swapping and |
601 |
skuang |
3785 |
our methods at a large flux. These distributions were obtained from |
602 |
skuang |
3779 |
Lennard-Jones simulations with $j_z^*(p_x)\sim 0.4$ (equivalent to a |
603 |
skuang |
3777 |
swapping interval of 20 time steps). This is a relatively large flux |
604 |
|
|
to demonstrate the nonthermal distributions that develop under the |
605 |
skuang |
3785 |
swapping method. In comparison, distributions produced by our method |
606 |
|
|
are very close to the ideal thermal situations.} |
607 |
skuang |
3776 |
\label{vDist} |
608 |
|
|
\end{figure} |
609 |
gezelter |
3769 |
|
610 |
skuang |
3776 |
\subsection{Bulk SPC/E water} |
611 |
skuang |
3785 |
We extend our applications of thermal conductivity and shear viscosity |
612 |
|
|
computations to a complex fluid model of SPC/E water. A simulation |
613 |
|
|
cell with 1000 molecules was set up in the similar manner as in |
614 |
|
|
\cite{kuang:164101}. For thermal conductivity simulations, |
615 |
|
|
measurements were taken to compare with previous RNEMD methods; for |
616 |
|
|
shear viscosity computations, simulations were run under a series of |
617 |
|
|
temperatures (with corresponding pressure relaxation using the |
618 |
|
|
isobaric-isothermal ensemble\cite{melchionna93}), and results were |
619 |
|
|
compared to available data from EMD |
620 |
|
|
methods\cite{10.1063/1.3330544,Medina2011}. Besides, a simulation with |
621 |
|
|
both thermal and momentum gradient was carried out to map out shear |
622 |
|
|
viscosity as a function of temperature to see the effectiveness and |
623 |
|
|
accuracy our method could reach. |
624 |
skuang |
3777 |
|
625 |
skuang |
3776 |
\subsubsection{Thermal conductivity} |
626 |
skuang |
3778 |
Table \ref{spceThermal} summarizes our thermal conductivity |
627 |
|
|
computations under different temperatures and thermal gradients, in |
628 |
|
|
comparison to the previous NIVS results\cite{kuang:164101} and |
629 |
|
|
experimental measurements\cite{WagnerKruse}. Note that no appreciable |
630 |
|
|
drift of total system energy or temperature was observed when our |
631 |
|
|
method is applied, which indicates that our algorithm conserves total |
632 |
skuang |
3785 |
energy well for systems involving electrostatic interactions. |
633 |
gezelter |
3769 |
|
634 |
skuang |
3778 |
Measurements using our method established similar temperature |
635 |
|
|
gradients to the previous NIVS method. Our simulation results are in |
636 |
|
|
good agreement with those from previous simulations. And both methods |
637 |
|
|
yield values in reasonable agreement with experimental |
638 |
|
|
values. Simulations using moderately higher thermal gradient or those |
639 |
|
|
with longer gradient axis ($z$) for measurement seem to have better |
640 |
|
|
accuracy, from our results. |
641 |
|
|
|
642 |
|
|
\begin{table*} |
643 |
|
|
\begin{minipage}{\linewidth} |
644 |
|
|
\begin{center} |
645 |
|
|
|
646 |
|
|
\caption{Thermal conductivity of SPC/E water under various |
647 |
|
|
imposed thermal gradients. Uncertainties are indicated in |
648 |
|
|
parentheses.} |
649 |
|
|
|
650 |
|
|
\begin{tabular}{ccccc} |
651 |
|
|
\hline\hline |
652 |
|
|
$\langle T\rangle$ & $\langle dT/dz\rangle$ & \multicolumn{3}{c} |
653 |
|
|
{$\lambda (\mathrm{W m}^{-1} \mathrm{K}^{-1})$} \\ |
654 |
|
|
(K) & (K/\AA) & This work & Previous NIVS\cite{kuang:164101} & |
655 |
|
|
Experiment\cite{WagnerKruse} \\ |
656 |
|
|
\hline |
657 |
|
|
300 & 0.8 & 0.815(0.027) & 0.770(0.008) & 0.61 \\ |
658 |
|
|
318 & 0.8 & 0.801(0.024) & 0.750(0.032) & 0.64 \\ |
659 |
|
|
& 1.6 & 0.766(0.007) & 0.778(0.019) & \\ |
660 |
skuang |
3779 |
& 0.8 & 0.786(0.009)\footnote{Simulation with $L_z$ |
661 |
|
|
twice as long.} & & \\ |
662 |
skuang |
3778 |
\hline\hline |
663 |
|
|
\end{tabular} |
664 |
|
|
\label{spceThermal} |
665 |
|
|
\end{center} |
666 |
|
|
\end{minipage} |
667 |
|
|
\end{table*} |
668 |
|
|
|
669 |
skuang |
3776 |
\subsubsection{Shear viscosity} |
670 |
skuang |
3778 |
The improvement our method achieves for shear viscosity computations |
671 |
|
|
enables us to apply it on SPC/E water models. The series of |
672 |
|
|
temperatures under which our shear viscosity calculations were carried |
673 |
|
|
out covers the liquid range under normal pressure. Our simulations |
674 |
|
|
predict a similar trend of $\eta$ vs. $T$ to EMD results we refer to |
675 |
skuang |
3779 |
(Table \ref{spceShear}). Considering subtlties such as temperature or |
676 |
|
|
pressure/density errors in these two series of measurements, our |
677 |
|
|
results show no significant difference from those with EMD |
678 |
|
|
methods. Since each value reported using our method takes only one |
679 |
|
|
single trajectory of simulation, instead of average from many |
680 |
|
|
trajectories when using EMD, our method provides an effective means |
681 |
|
|
for shear viscosity computations. |
682 |
gezelter |
3769 |
|
683 |
skuang |
3778 |
\begin{table*} |
684 |
|
|
\begin{minipage}{\linewidth} |
685 |
|
|
\begin{center} |
686 |
|
|
|
687 |
|
|
\caption{Computed shear viscosity of SPC/E water under different |
688 |
|
|
temperatures. Results are compared to those obtained with EMD |
689 |
|
|
method[CITATION]. Uncertainties are indicated in parentheses.} |
690 |
|
|
|
691 |
|
|
\begin{tabular}{cccc} |
692 |
|
|
\hline\hline |
693 |
|
|
$T$ & $\partial v_x/\partial z$ & \multicolumn{2}{c} |
694 |
|
|
{$\eta (\mathrm{mPa}\cdot\mathrm{s})$} \\ |
695 |
skuang |
3785 |
(K) & (10$^{10}$s$^{-1}$) & This work & Previous |
696 |
|
|
simulations\cite{Medina2011} \\ |
697 |
skuang |
3778 |
\hline |
698 |
skuang |
3785 |
273 & 1.12 & 1.218(0.004) & 1.282(0.048) \\ |
699 |
|
|
& 1.79 & 1.140(0.012) & \\ |
700 |
|
|
303 & 2.09 & 0.646(0.008) & 0.643(0.019) \\ |
701 |
|
|
318 & 2.50 & 0.536(0.007) & \\ |
702 |
|
|
& 5.25 & 0.510(0.007) & \\ |
703 |
|
|
& 2.82 & 0.474(0.003)\footnote{Simulation with $L_z$ twice |
704 |
|
|
as long.} & \\ |
705 |
|
|
333 & 3.10 & 0.428(0.002) & 0.421(0.008) \\ |
706 |
|
|
363 & 2.34 & 0.279(0.014) & 0.291(0.005) \\ |
707 |
|
|
& 4.26 & 0.306(0.001) & \\ |
708 |
skuang |
3778 |
\hline\hline |
709 |
|
|
\end{tabular} |
710 |
|
|
\label{spceShear} |
711 |
|
|
\end{center} |
712 |
|
|
\end{minipage} |
713 |
|
|
\end{table*} |
714 |
gezelter |
3769 |
|
715 |
skuang |
3785 |
A more effective way to map out $\eta$ vs $T$ is to combine a momentum |
716 |
|
|
flux with a thermal flux. Figure \ref{Tvxdvdz} shows the thermal and |
717 |
|
|
velocity gradient in one such simulation. At different positions with |
718 |
|
|
different temperatures, the velocity gradient is not a constant but |
719 |
|
|
can be computed locally. With the data provided in Figure |
720 |
|
|
\ref{Tvxdvdz}, a series of $\eta$ is calculated as in Figure |
721 |
|
|
\ref{etaT} and a linear fit was performed to $\partial v_x/\partial z$ |
722 |
|
|
vs. $z$ so that the resulted $\eta$ can be present as a curve as |
723 |
|
|
well. For comparison, other results are also mapped in the figure. |
724 |
|
|
|
725 |
|
|
\begin{figure} |
726 |
|
|
\includegraphics[width=\linewidth]{tvxdvdz} |
727 |
|
|
\caption{With a combination of a thermal and a momentum flux, a |
728 |
|
|
simulation can have both a temperature (top) and a velocity (middle) |
729 |
|
|
gradient. Due to the thermal gradient, $\partial v_x/\partial z$ is |
730 |
|
|
not constant but can be computed using finite difference |
731 |
|
|
approximations (lower). These data can be used further to calculate |
732 |
|
|
$\eta$ vs $T$ (Figure \ref{etaT}).} |
733 |
|
|
\label{Tvxdvdz} |
734 |
|
|
\end{figure} |
735 |
|
|
|
736 |
|
|
From Figure \ref{etaT}, one can see that the generated curve agrees |
737 |
|
|
well with the above RNEMD simulations at different temperatures, as |
738 |
|
|
well as results reported using EMD |
739 |
|
|
methods\cite{10.1063/1.3330544,Medina2011} in much of the temperature |
740 |
|
|
range simulated. However, this curve has relatively large error in |
741 |
|
|
lower temperature regions and has some difference in predicting $\eta$ |
742 |
|
|
near 273K. Provided that this curve only takes one trajectory to |
743 |
|
|
generate, these results are of satisfactory efficiency and |
744 |
|
|
accuracy. Since previous work already pointed out that the SPC/E model |
745 |
|
|
tends to predict lower viscosity compared to experimental |
746 |
|
|
data,\cite{Medina2011} experimental comparison are not given here. |
747 |
|
|
|
748 |
|
|
\begin{figure} |
749 |
|
|
\includegraphics[width=\linewidth]{etaT} |
750 |
|
|
\caption{The curve generated by single simulation with thermal and |
751 |
|
|
momentum gradient predicts satisfatory values in much of the |
752 |
|
|
temperature range under test.} |
753 |
|
|
\label{etaT} |
754 |
|
|
\end{figure} |
755 |
|
|
|
756 |
skuang |
3779 |
\subsection{Interfacial frictions and slip lengths} |
757 |
skuang |
3785 |
Another attractive aspect of our method is the ability to apply |
758 |
|
|
momentum and/or thermal flux in nonhomogeneous systems, where |
759 |
|
|
molecules of different identities (or phases) are segregated in |
760 |
|
|
different regions. We have previously studied the interfacial thermal |
761 |
|
|
transport of a series of metal gold-liquid |
762 |
|
|
surfaces\cite{kuang:164101,kuang:AuThl}, and would like to further |
763 |
|
|
investigate the relationship between this phenomenon and the |
764 |
skuang |
3779 |
interfacial frictions. |
765 |
gezelter |
3769 |
|
766 |
skuang |
3779 |
Table \ref{etaKappaDelta} includes these computations and previous |
767 |
|
|
calculations of corresponding interfacial thermal conductance. For |
768 |
|
|
bare Au(111) surfaces, slip boundary conditions were observed for both |
769 |
|
|
organic and aqueous liquid phases, corresponding to previously |
770 |
skuang |
3785 |
computed low interfacial thermal conductance. In comparison, the |
771 |
|
|
butanethiol covered Au(111) surface appeared to be sticky to the |
772 |
|
|
organic liquid layers in our simulations. We have reported conductance |
773 |
|
|
enhancement effect for this surface capping agent,\cite{kuang:AuThl} |
774 |
|
|
and these observations have a qualitative agreement with the thermal |
775 |
|
|
conductance results. This agreement also supports discussions on the |
776 |
|
|
relationship between surface wetting and slip effect and thermal |
777 |
|
|
conductance of the |
778 |
|
|
interface.\cite{PhysRevLett.82.4671,doi:10.1080/0026897031000068578,garde:PhysRevLett2009} |
779 |
skuang |
3776 |
|
780 |
gezelter |
3769 |
\begin{table*} |
781 |
|
|
\begin{minipage}{\linewidth} |
782 |
|
|
\begin{center} |
783 |
|
|
|
784 |
skuang |
3779 |
\caption{Computed interfacial friction coefficient values for |
785 |
|
|
interfaces with various components for liquid and solid |
786 |
|
|
phase. Error estimates are indicated in parentheses.} |
787 |
gezelter |
3769 |
|
788 |
skuang |
3779 |
\begin{tabular}{llcccccc} |
789 |
gezelter |
3769 |
\hline\hline |
790 |
skuang |
3779 |
Solid & Liquid & $T$ & $j_z(p_x)$ & $\eta_{liquid}$ & $\kappa$ |
791 |
|
|
& $\delta$ & $G$\footnote{References \cite{kuang:AuThl} and |
792 |
|
|
\cite{kuang:164101}.} \\ |
793 |
skuang |
3785 |
surface & molecules & K & MPa & mPa$\cdot$s & |
794 |
|
|
10$^4$Pa$\cdot$s/m & nm & MW/m$^2$/K \\ |
795 |
gezelter |
3769 |
\hline |
796 |
skuang |
3785 |
Au(111) & hexane & 200 & 1.08 & 0.197(0.009) & 5.30(0.36) & |
797 |
|
|
3.72 & 46.5 \\ |
798 |
|
|
& & & 2.15 & 0.141(0.002) & 5.31(0.26) & |
799 |
|
|
2.76 & \\ |
800 |
|
|
Au-SC$_4$H$_9$ & hexane & 200 & 2.16 & 0.286(0.019) & $\infty$ |
801 |
|
|
& 0 & 131 \\ |
802 |
|
|
& & & 5.39 & 0.320(0.006) & $\infty$ |
803 |
|
|
& 0 & \\ |
804 |
gezelter |
3769 |
\hline |
805 |
skuang |
3785 |
Au(111) & toluene & 200 & 1.08 & 0.722(0.035) & 15.7(0.7) & |
806 |
|
|
4.60 & 70.1 \\ |
807 |
|
|
& & & 2.16 & 0.544(0.030) & 11.2(0.5) & |
808 |
|
|
4.86 & \\ |
809 |
|
|
Au-SC$_4$H$_9$ & toluene & 200 & 5.39 & 0.980(0.057) & |
810 |
|
|
$\infty$ & 0 & 187 \\ |
811 |
|
|
& & & 10.8 & 0.995(0.005) & |
812 |
|
|
$\infty$ & 0 & \\ |
813 |
skuang |
3779 |
\hline |
814 |
skuang |
3785 |
Au(111) & water & 300 & 1.08 & 0.399(0.050) & 1.928(0.022) & |
815 |
skuang |
3779 |
20.7 & 1.65 \\ |
816 |
skuang |
3785 |
& & & 2.16 & 0.794(0.255) & 1.895(0.003) & |
817 |
skuang |
3779 |
41.9 & \\ |
818 |
|
|
\hline |
819 |
skuang |
3785 |
ice(basal) & water & 225 & 19.4 & 15.8(0.2) & $\infty$ & 0 & \\ |
820 |
gezelter |
3769 |
\hline\hline |
821 |
|
|
\end{tabular} |
822 |
skuang |
3779 |
\label{etaKappaDelta} |
823 |
gezelter |
3769 |
\end{center} |
824 |
|
|
\end{minipage} |
825 |
|
|
\end{table*} |
826 |
|
|
|
827 |
skuang |
3779 |
An interesting effect alongside the surface friction change is |
828 |
|
|
observed on the shear viscosity of liquids in the regions close to the |
829 |
skuang |
3785 |
solid surface. In our results, $\eta$ measured near a ``slip'' surface |
830 |
|
|
tends to be smaller than that near a ``stick'' surface. This may |
831 |
|
|
suggest the influence from an interface on the dynamic properties of |
832 |
|
|
liquid within its neighbor regions. It is known that diffusions of |
833 |
|
|
solid particles in liquid phase is affected by their surface |
834 |
|
|
conditions (stick or slip boundary).\cite{10.1063/1.1610442} Our |
835 |
|
|
observations could provide a support to this phenomenon. |
836 |
gezelter |
3769 |
|
837 |
skuang |
3779 |
In addition to these previously studied interfaces, we attempt to |
838 |
|
|
construct ice-water interfaces and the basal plane of ice lattice was |
839 |
skuang |
3785 |
studied here. In contrast to the Au(111)/water interface, where the |
840 |
|
|
friction coefficient is substantially small and large slip effect |
841 |
skuang |
3779 |
presents, the ice/liquid water interface demonstrates strong |
842 |
skuang |
3785 |
solid-liquid interactions and appears to be sticky. The supercooled |
843 |
|
|
liquid phase is an order of magnitude more viscous than measurements |
844 |
|
|
in previous section. It would be of interst to investigate the effect |
845 |
|
|
of different ice lattice planes (such as prism and other surfaces) on |
846 |
|
|
interfacial friction and the corresponding liquid viscosity. |
847 |
gezelter |
3769 |
|
848 |
skuang |
3776 |
\section{Conclusions} |
849 |
skuang |
3779 |
Our simulations demonstrate the validity of our method in RNEMD |
850 |
|
|
computations of thermal conductivity and shear viscosity in atomic and |
851 |
|
|
molecular liquids. Our method maintains thermal velocity distributions |
852 |
|
|
and avoids thermal anisotropy in previous NIVS shear stress |
853 |
|
|
simulations, as well as retains attractive features of previous RNEMD |
854 |
|
|
methods. There is no {\it a priori} restrictions to the method to be |
855 |
|
|
applied in various ensembles, so prospective applications to |
856 |
|
|
extended-system methods are possible. |
857 |
gezelter |
3769 |
|
858 |
skuang |
3785 |
Our method is capable of effectively imposing thermal and/or momentum |
859 |
|
|
flux accross an interface. This facilitates studies that relates |
860 |
|
|
dynamic property measurements to the chemical details of an |
861 |
|
|
interface. Therefore, investigations can be carried out to |
862 |
|
|
characterize interfacial interactions using the method. |
863 |
gezelter |
3769 |
|
864 |
skuang |
3779 |
Another attractive feature of our method is the ability of |
865 |
skuang |
3785 |
simultaneously introducing thermal and momentum gradients in a |
866 |
|
|
system. This facilitates us to effectively map out the shear viscosity |
867 |
|
|
with respect to a range of temperature in single trajectory of |
868 |
|
|
simulation with satisafactory accuracy. Complex systems that involve |
869 |
|
|
thermal and momentum gradients might potentially benefit from |
870 |
|
|
this. For example, the Soret effects under a velocity gradient might |
871 |
|
|
be models of interest to purification and separation researches. |
872 |
gezelter |
3769 |
|
873 |
|
|
\section{Acknowledgments} |
874 |
|
|
Support for this project was provided by the National Science |
875 |
|
|
Foundation under grant CHE-0848243. Computational time was provided by |
876 |
|
|
the Center for Research Computing (CRC) at the University of Notre |
877 |
|
|
Dame. |
878 |
|
|
|
879 |
|
|
\newpage |
880 |
|
|
|
881 |
skuang |
3770 |
\bibliography{stokes} |
882 |
gezelter |
3769 |
|
883 |
|
|
\end{doublespace} |
884 |
|
|
\end{document} |