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\usepackage{caption} |
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%\usepackage{tabularx} |
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\usepackage{graphicx} |
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\usepackage{multirow} |
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%\usepackage{booktabs} |
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%\usepackage{bibentry} |
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%\usepackage{mathrsfs} |
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\begin{doublespace} |
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|
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|
\begin{abstract} |
42 |
< |
|
42 |
> |
We present a new method for introducing stable non-equilibrium |
43 |
> |
velocity and temperature distributions in molecular dynamics |
44 |
> |
simulations of heterogeneous systems. This method extends earlier |
45 |
> |
Reverse Non-Equilibrium Molecular Dynamics (RNEMD) methods which use |
46 |
> |
momentum exchange swapping moves that can create non-thermal |
47 |
> |
velocity distributions and are difficult to use for interfacial |
48 |
> |
calculations. By using non-isotropic velocity scaling (NIVS) on the |
49 |
> |
molecules in specific regions of a system, it is possible to impose |
50 |
> |
momentum or thermal flux between regions of a simulation and stable |
51 |
> |
thermal and momentum gradients can then be established. The scaling |
52 |
> |
method we have developed conserves the total linear momentum and |
53 |
> |
total energy of the system. To test the methods, we have computed |
54 |
> |
the thermal conductivity of model liquid and solid systems as well |
55 |
> |
as the interfacial thermal conductivity of a metal-water interface. |
56 |
> |
We find that the NIVS-RNEMD improves the problematic velocity |
57 |
> |
distributions that develop in other RNEMD methods. |
58 |
|
\end{abstract} |
59 |
|
|
60 |
|
\newpage |
65 |
|
% BODY OF TEXT |
66 |
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
67 |
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|
52 |
– |
|
53 |
– |
|
68 |
|
\section{Introduction} |
69 |
|
The original formulation of Reverse Non-equilibrium Molecular Dynamics |
70 |
|
(RNEMD) obtains transport coefficients (thermal conductivity and shear |
71 |
|
viscosity) in a fluid by imposing an artificial momentum flux between |
72 |
|
two thin parallel slabs of material that are spatially separated in |
73 |
|
the simulation cell.\cite{MullerPlathe:1997xw,ISI:000080382700030} The |
74 |
< |
artificial flux is typically created by periodically ``swapping'' either |
75 |
< |
the entire momentum vector $\vec{p}$ or single components of this |
76 |
< |
vector ($p_x$) between molecules in each of the two slabs. If the two |
77 |
< |
slabs are separated along the z coordinate, the imposed flux is either |
78 |
< |
directional ($j_z(p_x)$) or isotropic ($J_z$), and the response of a |
79 |
< |
simulated system to the imposed momentum flux will typically be a |
80 |
< |
velocity or thermal gradient. The transport coefficients (shear |
81 |
< |
viscosity and thermal conductivity) are easily obtained by assuming |
82 |
< |
linear response of the system, |
74 |
> |
artificial flux is typically created by periodically ``swapping'' |
75 |
> |
either the entire momentum vector $\vec{p}$ or single components of |
76 |
> |
this vector ($p_x$) between molecules in each of the two slabs. If |
77 |
> |
the two slabs are separated along the $z$ coordinate, the imposed flux |
78 |
> |
is either directional ($j_z(p_x)$) or isotropic ($J_z$), and the |
79 |
> |
response of a simulated system to the imposed momentum flux will |
80 |
> |
typically be a velocity or thermal gradient (Fig. \ref{thermalDemo}). |
81 |
> |
The shear viscosity ($\eta$) and thermal conductivity ($\lambda$) are |
82 |
> |
easily obtained by assuming linear response of the system, |
83 |
|
\begin{eqnarray} |
84 |
|
j_z(p_x) & = & -\eta \frac{\partial v_x}{\partial z}\\ |
85 |
< |
J & = & \lambda \frac{\partial T}{\partial z} |
85 |
> |
J_z & = & \lambda \frac{\partial T}{\partial z} |
86 |
|
\end{eqnarray} |
87 |
< |
RNEMD has been widely used to provide computational estimates of thermal |
88 |
< |
conductivities and shear viscosities in a wide range of materials, |
89 |
< |
from liquid copper to monatomic liquids to molecular fluids |
90 |
< |
(e.g. ionic liquids).\cite{ISI:000246190100032} |
87 |
> |
RNEMD has been widely used to provide computational estimates of |
88 |
> |
thermal conductivities and shear viscosities in a wide range of |
89 |
> |
materials, from liquid copper to both monatomic and molecular fluids |
90 |
> |
(e.g. ionic |
91 |
> |
liquids).\cite{Bedrov:2000-1,Bedrov:2000,Muller-Plathe:2002,ISI:000184808400018,ISI:000231042800044,Maginn:2007,Muller-Plathe:2008,ISI:000258460400020,ISI:000258840700015,ISI:000261835100054} |
92 |
|
|
93 |
< |
RNEMD is preferable in many ways to the forward NEMD methods because |
94 |
< |
it imposes what is typically difficult to measure (a flux or stress) |
95 |
< |
and it is typically much easier to compute momentum gradients or |
96 |
< |
strains (the response). For similar reasons, RNEMD is also preferable |
97 |
< |
to slowly-converging equilibrium methods for measuring thermal |
98 |
< |
conductivity and shear viscosity (using Green-Kubo relations or the |
99 |
< |
Helfand moment approach of Viscardy {\it et |
93 |
> |
\begin{figure} |
94 |
> |
\includegraphics[width=\linewidth]{thermalDemo} |
95 |
> |
\caption{RNEMD methods impose an unphysical transfer of momentum or |
96 |
> |
kinetic energy between a ``hot'' slab and a ``cold'' slab in the |
97 |
> |
simulation box. The molecular system responds to this imposed flux |
98 |
> |
by generating a momentum or temperature gradient. The slope of the |
99 |
> |
gradient can then be used to compute transport properties (e.g. |
100 |
> |
shear viscosity and thermal conductivity).} |
101 |
> |
\label{thermalDemo} |
102 |
> |
\end{figure} |
103 |
> |
|
104 |
> |
RNEMD is preferable in many ways to the forward NEMD |
105 |
> |
methods\cite{ISI:A1988Q205300014,hess:209,Vasquez:2004fk,backer:154503,ISI:000266247600008} |
106 |
> |
because it imposes what is typically difficult to measure (a flux or |
107 |
> |
stress) and it is typically much easier to compute the response |
108 |
> |
(momentum gradients or strains). For similar reasons, RNEMD is also |
109 |
> |
preferable to slowly-converging equilibrium methods for measuring |
110 |
> |
thermal conductivity and shear viscosity (using Green-Kubo |
111 |
> |
relations\cite{daivis:541,mondello:9327} or the Helfand moment |
112 |
> |
approach of Viscardy {\it et |
113 |
|
al.}\cite{Viscardy:2007bh,Viscardy:2007lq}) because these rely on |
114 |
|
computing difficult to measure quantities. |
115 |
|
|
119 |
|
typically samples from the same manifold of states in the |
120 |
|
microcanonical ensemble. |
121 |
|
|
122 |
< |
Recently, Tenney and Maginn\cite{ISI:000273472300004} have discovered |
122 |
> |
Recently, Tenney and Maginn\cite{Maginn:2010} have discovered |
123 |
|
some problems with the original RNEMD swap technique. Notably, large |
124 |
|
momentum fluxes (equivalent to frequent momentum swaps between the |
125 |
< |
slabs) can result in ``notched'', ``peaked'' and generally non-thermal momentum |
126 |
< |
distributions in the two slabs, as well as non-linear thermal and |
127 |
< |
velocity distributions along the direction of the imposed flux ($z$). |
128 |
< |
Tenney and Maginn obtained reasonable limits on imposed flux and |
129 |
< |
self-adjusting metrics for retaining the usability of the method. |
125 |
> |
slabs) can result in ``notched'', ``peaked'' and generally non-thermal |
126 |
> |
momentum distributions in the two slabs, as well as non-linear thermal |
127 |
> |
and velocity distributions along the direction of the imposed flux |
128 |
> |
($z$). Tenney and Maginn obtained reasonable limits on imposed flux |
129 |
> |
and self-adjusting metrics for retaining the usability of the method. |
130 |
|
|
131 |
|
In this paper, we develop and test a method for non-isotropic velocity |
132 |
< |
scaling (NIVS-RNEMD) which retains the desirable features of RNEMD |
132 |
> |
scaling (NIVS) which retains the desirable features of RNEMD |
133 |
|
(conservation of linear momentum and total energy, compatibility with |
134 |
|
periodic boundary conditions) while establishing true thermal |
135 |
< |
distributions in each of the two slabs. In the next section, we |
136 |
< |
develop the method for determining the scaling constraints. We then |
137 |
< |
test the method on both single component, multi-component, and |
138 |
< |
non-isotropic mixtures and show that it is capable of providing |
135 |
> |
distributions in each of the two slabs. In the next section, we |
136 |
> |
present the method for determining the scaling constraints. We then |
137 |
> |
test the method on both liquids and solids as well as a non-isotropic |
138 |
> |
liquid-solid interface and show that it is capable of providing |
139 |
|
reasonable estimates of the thermal conductivity and shear viscosity |
140 |
< |
in these cases. |
140 |
> |
in all of these cases. |
141 |
|
|
142 |
|
\section{Methodology} |
143 |
< |
We retain the basic idea of Muller-Plathe's RNEMD method; the periodic |
144 |
< |
system is partitioned into a series of thin slabs along a particular |
143 |
> |
We retain the basic idea of M\"{u}ller-Plathe's RNEMD method; the |
144 |
> |
periodic system is partitioned into a series of thin slabs along one |
145 |
|
axis ($z$). One of the slabs at the end of the periodic box is |
146 |
|
designated the ``hot'' slab, while the slab in the center of the box |
147 |
|
is designated the ``cold'' slab. The artificial momentum flux will be |
149 |
|
hot slab. |
150 |
|
|
151 |
|
Rather than using momentum swaps, we use a series of velocity scaling |
152 |
< |
moves. For molecules $\{i\}$ located within the cold slab, |
152 |
> |
moves. For molecules $\{i\}$ located within the cold slab, |
153 |
|
\begin{equation} |
154 |
|
\vec{v}_i \leftarrow \left( \begin{array}{ccc} |
155 |
|
x & 0 & 0 \\ |
157 |
|
0 & 0 & z \\ |
158 |
|
\end{array} \right) \cdot \vec{v}_i |
159 |
|
\end{equation} |
160 |
< |
where ${x, y, z}$ are a set of 3 scaling variables for each of the |
161 |
< |
three directions in the system. Likewise, the molecules $\{j\}$ |
162 |
< |
located in the hot slab will see a concomitant scaling of velocities, |
160 |
> |
where ${x, y, z}$ are a set of 3 velocity-scaling variables for each |
161 |
> |
of the three directions in the system. Likewise, the molecules |
162 |
> |
$\{j\}$ located in the hot slab will see a concomitant scaling of |
163 |
> |
velocities, |
164 |
|
\begin{equation} |
165 |
|
\vec{v}_j \leftarrow \left( \begin{array}{ccc} |
166 |
|
x^\prime & 0 & 0 \\ |
170 |
|
\end{equation} |
171 |
|
|
172 |
|
Conservation of linear momentum in each of the three directions |
173 |
< |
($\alpha = x,y,z$) ties the values of the hot and cold bin scaling |
173 |
> |
($\alpha = x,y,z$) ties the values of the hot and cold scaling |
174 |
|
parameters together: |
175 |
|
\begin{equation} |
176 |
|
P_h^\alpha + P_c^\alpha = \alpha^\prime P_h^\alpha + \alpha P_c^\alpha |
199 |
|
\sum_{\alpha = x,y,z} K_h^\alpha + K_c^\alpha = \sum_{\alpha = x,y,z} |
200 |
|
\left(\alpha^\prime\right)^2 K_h^\alpha + \alpha^2 K_c^\alpha |
201 |
|
\end{equation} |
202 |
< |
where the kinetic energies, $K_h^\alpha$ and $K_c^\alpha$, are computed |
203 |
< |
for each of the three directions in a similar manner to the linear momenta |
204 |
< |
(Eq. \ref{eq:momentumdef}). Substituting in the expressions for the |
205 |
< |
hot scaling parameters ($\alpha^\prime$) from Eq. (\ref{eq:hotcoldscaling}), |
206 |
< |
we obtain the {\it constraint ellipsoid equation}: |
202 |
> |
where the translational kinetic energies, $K_h^\alpha$ and |
203 |
> |
$K_c^\alpha$, are computed for each of the three directions in a |
204 |
> |
similar manner to the linear momenta (Eq. \ref{eq:momentumdef}). |
205 |
> |
Substituting in the expressions for the hot scaling parameters |
206 |
> |
($\alpha^\prime$) from Eq. (\ref{eq:hotcoldscaling}), we obtain the |
207 |
> |
{\it constraint ellipsoid}: |
208 |
|
\begin{equation} |
209 |
< |
\sum_{\alpha = x,y,z} a_\alpha \alpha^2 + b_\alpha \alpha + c_\alpha = 0 |
209 |
> |
\sum_{\alpha = x,y,z} \left( a_\alpha \alpha^2 + b_\alpha \alpha + |
210 |
> |
c_\alpha \right) = 0 |
211 |
|
\label{eq:constraintEllipsoid} |
212 |
|
\end{equation} |
213 |
|
where the constants are obtained from the instantaneous values of the |
219 |
|
c_\alpha & = & K_h^\alpha p_\alpha^2 + 2 K_h^\alpha p_\alpha - K_c^\alpha |
220 |
|
\label{eq:constraintEllipsoidConsts} |
221 |
|
\end{eqnarray} |
222 |
< |
This ellipsoid equation defines the set of cold slab scaling |
223 |
< |
parameters which can be applied while preserving both linear momentum |
224 |
< |
in all three directions as well as kinetic energy. |
222 |
> |
This ellipsoid (Eq. \ref{eq:constraintEllipsoid}) defines the set of |
223 |
> |
cold slab scaling parameters which, when applied, preserve the linear |
224 |
> |
momentum of the system in all three directions as well as total |
225 |
> |
kinetic energy. |
226 |
|
|
227 |
< |
The goal of using velocity scaling variables is to transfer linear |
228 |
< |
momentum or kinetic energy from the cold slab to the hot slab. If the |
229 |
< |
hot and cold slabs are separated along the z-axis, the energy flux is |
230 |
< |
given simply by the decrease in kinetic energy of the cold bin: |
227 |
> |
The goal of using these velocity scaling variables is to transfer |
228 |
> |
kinetic energy from the cold slab to the hot slab. If the hot and |
229 |
> |
cold slabs are separated along the z-axis, the energy flux is given |
230 |
> |
simply by the decrease in kinetic energy of the cold bin: |
231 |
|
\begin{equation} |
232 |
|
(1-x^2) K_c^x + (1-y^2) K_c^y + (1-z^2) K_c^z = J_z\Delta t |
233 |
|
\end{equation} |
234 |
|
The expression for the energy flux can be re-written as another |
235 |
|
ellipsoid centered on $(x,y,z) = 0$: |
236 |
|
\begin{equation} |
237 |
< |
x^2 K_c^x + y^2 K_c^y + z^2 K_c^z = K_c^x + K_c^y + K_c^z - J_z\Delta t |
237 |
> |
\sum_{\alpha = x,y,z} K_c^\alpha \alpha^2 = \sum_{\alpha = x,y,z} |
238 |
> |
K_c^\alpha -J_z \Delta t |
239 |
|
\label{eq:fluxEllipsoid} |
240 |
|
\end{equation} |
241 |
< |
The spatial extent of the {\it flux ellipsoid equation} is governed |
242 |
< |
both by a targetted value, $J_z$ as well as the instantaneous values of the |
243 |
< |
kinetic energy components in the cold bin. |
241 |
> |
The spatial extent of the {\it thermal flux ellipsoid} is governed |
242 |
> |
both by the target flux, $J_z$ as well as the instantaneous values of |
243 |
> |
the kinetic energy components in the cold bin. |
244 |
|
|
245 |
|
To satisfy an energetic flux as well as the conservation constraints, |
246 |
< |
it is sufficient to determine the points ${x,y,z}$ which lie on both |
247 |
< |
the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the |
248 |
< |
flux ellipsoid (Eq. \ref{eq:fluxEllipsoid}), i.e. the intersection of |
249 |
< |
the two ellipsoids in 3-dimensional space. |
246 |
> |
we must determine the points ${x,y,z}$ that lie on both the constraint |
247 |
> |
ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the flux ellipsoid |
248 |
> |
(Eq. \ref{eq:fluxEllipsoid}), i.e. the intersection of the two |
249 |
> |
ellipsoids in 3-dimensional space. |
250 |
|
|
251 |
|
\begin{figure} |
252 |
|
\includegraphics[width=\linewidth]{ellipsoids} |
253 |
< |
\caption{Scaling points which maintain both constant energy and |
254 |
< |
constant linear momentum of the system lie on the surface of the |
255 |
< |
{\it constraint ellipsoid} while points which generate the target |
256 |
< |
momentum flux lie on the surface of the {\it flux ellipsoid}. The |
257 |
< |
velocity distributions in the hot bin are scaled by only those |
253 |
> |
\caption{Velocity scaling coefficients which maintain both constant |
254 |
> |
energy and constant linear momentum of the system lie on the surface |
255 |
> |
of the {\it constraint ellipsoid} while points which generate the |
256 |
> |
target momentum flux lie on the surface of the {\it flux ellipsoid}. |
257 |
> |
The velocity distributions in the cold bin are scaled by only those |
258 |
|
points which lie on both ellipsoids.} |
259 |
|
\label{ellipsoids} |
260 |
|
\end{figure} |
261 |
|
|
262 |
< |
One may also define momentum flux (say along the x-direction) as: |
262 |
> |
Since ellipsoids can be expressed as polynomials up to second order in |
263 |
> |
each of the three coordinates, finding the the intersection points of |
264 |
> |
two ellipsoids is isomorphic to finding the roots a polynomial of |
265 |
> |
degree 16. There are a number of polynomial root-finding methods in |
266 |
> |
the literature, [CITATIONS NEEDED] but numerically finding the roots |
267 |
> |
of high-degree polynomials is generally an ill-conditioned |
268 |
> |
problem.[CITATION NEEDED] One simplification is to maintain velocity |
269 |
> |
scalings that are {\it as isotropic as possible}. To do this, we |
270 |
> |
impose $x=y$, and to treat both the constraint and flux ellipsoids as |
271 |
> |
2-dimensional ellipses. In reduced dimensionality, the |
272 |
> |
intersecting-ellipse problem reduces to finding the roots of |
273 |
> |
polynomials of degree 4. |
274 |
> |
|
275 |
> |
Depending on the target flux and current velocity distributions, the |
276 |
> |
ellipsoids can have between 0 and 4 intersection points. If there are |
277 |
> |
no intersection points, it is not possible to satisfy the constraints |
278 |
> |
while performing a non-equilibrium scaling move, and no change is made |
279 |
> |
to the dynamics. |
280 |
> |
|
281 |
> |
With multiple intersection points, any of the scaling points will |
282 |
> |
conserve the linear momentum and kinetic energy of the system and will |
283 |
> |
generate the correct target flux. Although this method is inherently |
284 |
> |
non-isotropic, the goal is still to maintain the system as close to an |
285 |
> |
isotropic fluid as possible. With this in mind, we would like the |
286 |
> |
kinetic energies in the three different directions could become as |
287 |
> |
close as each other as possible after each scaling. Simultaneously, |
288 |
> |
one would also like each scaling as gentle as possible, i.e. ${x,y,z |
289 |
> |
\rightarrow 1}$, in order to avoid large perturbation to the system. |
290 |
> |
To do this, we pick the intersection point which maintains the three |
291 |
> |
scaling variables ${x, y, z}$ as well as the ratio of kinetic energies |
292 |
> |
${K_c^z/K_c^x, K_c^z/K_c^y}$ as close as possible to 1. |
293 |
> |
|
294 |
> |
After the valid scaling parameters are arrived at by solving geometric |
295 |
> |
intersection problems in $x, y, z$ space in order to obtain cold slab |
296 |
> |
scaling parameters, Eq. (\ref{eq:hotcoldscaling}) is used to |
297 |
> |
determine the conjugate hot slab scaling variables. |
298 |
> |
|
299 |
> |
\subsection{Introducing shear stress via velocity scaling} |
300 |
> |
It is also possible to use this method to magnify the random |
301 |
> |
fluctuations of the average momentum in each of the bins to induce a |
302 |
> |
momentum flux. Doing this repeatedly will create a shear stress on |
303 |
> |
the system which will respond with an easily-measured strain. The |
304 |
> |
momentum flux (say along the $x$-direction) may be defined as: |
305 |
|
\begin{equation} |
306 |
|
(1-x) P_c^x = j_z(p_x)\Delta t |
307 |
|
\label{eq:fluxPlane} |
308 |
|
\end{equation} |
309 |
< |
The above {\it flux equation} is essentially a plane which is |
310 |
< |
perpendicular to the x-axis, with its position governed both by a |
311 |
< |
targetted value, $j_z(p_x)$ as well as the instantaneous value of the |
237 |
< |
momentum along the x-direction. |
309 |
> |
This {\it momentum flux plane} is perpendicular to the $x$-axis, with |
310 |
> |
its position governed both by a target value, $j_z(p_x)$ as well as |
311 |
> |
the instantaneous value of the momentum along the $x$-direction. |
312 |
|
|
313 |
< |
Similarly, to satisfy a momentum flux as well as the conservation |
314 |
< |
constraints, it is sufficient to determine the points ${x,y,z}$ which |
315 |
< |
lie on both the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) |
316 |
< |
and the flux plane (Eq. \ref{eq:fluxPlane}), i.e. the intersection of |
317 |
< |
an ellipsoid and a plane in 3-dimensional space. |
313 |
> |
In order to satisfy a momentum flux as well as the conservation |
314 |
> |
constraints, we must determine the points ${x,y,z}$ which lie on both |
315 |
> |
the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the |
316 |
> |
flux plane (Eq. \ref{eq:fluxPlane}), i.e. the intersection of an |
317 |
> |
ellipsoid and a plane in 3-dimensional space. |
318 |
|
|
319 |
< |
To summarize, by solving respective equation sets, one can determine |
320 |
< |
possible sets of scaling variables for cold slab. And corresponding |
321 |
< |
sets of scaling variables for hot slab can be determine as well. |
319 |
> |
In the case of momentum flux transfer, we also impose another |
320 |
> |
constraint to set the kinetic energy transfer as zero. In other |
321 |
> |
words, we apply Eq. \ref{eq:fluxEllipsoid} and let ${J_z = 0}$. With |
322 |
> |
one variable fixed by Eq. \ref{eq:fluxPlane}, one now have a similar |
323 |
> |
set of quartic equations to the above kinetic energy transfer problem. |
324 |
|
|
325 |
< |
The following problem will be choosing an optimal set of scaling |
250 |
< |
variables among the possible sets. Although this method is inherently |
251 |
< |
non-isotropic, the goal is still to maintain the system as isotropic |
252 |
< |
as possible. Under this consideration, one would like the kinetic |
253 |
< |
energies in different directions could become as close as each other |
254 |
< |
after each scaling. Simultaneously, one would also like each scaling |
255 |
< |
as gentle as possible, i.e. ${x,y,z \rightarrow 1}$, in order to avoid |
256 |
< |
large perturbation to the system. Therefore, one approach to obtain the |
257 |
< |
scaling variables would be constructing an criteria function, with |
258 |
< |
constraints as above equation sets, and solving the function's minimum |
259 |
< |
by method like Lagrange multipliers. |
325 |
> |
\section{Computational Details} |
326 |
|
|
327 |
< |
In order to save computation time, we have a different approach to a |
328 |
< |
relatively good set of scaling variables with much less calculation |
329 |
< |
than above. Here is the detail of our simplification of the problem. |
327 |
> |
We have implemented this methodology in our molecular dynamics code, |
328 |
> |
OpenMD,\cite{Meineke:2005gd,openmd} performing the NIVS scaling moves |
329 |
> |
after each MD step. We have tested the method in a variety of |
330 |
> |
different systems, including homogeneous fluids (Lennard-Jones and |
331 |
> |
SPC/E water), crystalline solids ({\sc eam}~\cite{PhysRevB.33.7983} and |
332 |
> |
quantum Sutton-Chen ({\sc q-sc})~\cite{PhysRevB.59.3527} |
333 |
> |
models for Gold), and heterogeneous interfaces (QSC gold - SPC/E |
334 |
> |
water). The last of these systems would have been difficult to study |
335 |
> |
using previous RNEMD methods, but using velocity scaling moves, we can |
336 |
> |
even obtain estimates of the interfacial thermal conductivities ($G$). |
337 |
|
|
338 |
< |
In the case of kinetic energy transfer, we impose another constraint |
266 |
< |
${x = y}$, into the equation sets. Consequently, there are two |
267 |
< |
variables left. And now one only needs to solve a set of two {\it |
268 |
< |
ellipses equations}. This problem would be transformed into solving |
269 |
< |
one quartic equation for one of the two variables. There are known |
270 |
< |
generic methods that solve real roots of quartic equations. Then one |
271 |
< |
can determine the other variable and obtain sets of scaling |
272 |
< |
variables. Among these sets, one can apply the above criteria to |
273 |
< |
choose the best set, while much faster with only a few sets to choose. |
338 |
> |
\subsection{Simulation Cells} |
339 |
|
|
340 |
< |
In the case of momentum flux transfer, we impose another constraint to |
341 |
< |
set the kinetic energy transfer as zero. In another word, we apply |
342 |
< |
Eq. \ref{eq:fluxEllipsoid} and let ${J_z = 0}$. After that, with one |
343 |
< |
variable fixed by Eq. \ref{eq:fluxPlane}, one now have a similar set |
344 |
< |
of equations on the above kinetic energy transfer problem. Therefore, |
345 |
< |
an approach similar to the above would be sufficient for this as well. |
340 |
> |
In each of the systems studied, the dynamics was carried out in a |
341 |
> |
rectangular simulation cell using periodic boundary conditions in all |
342 |
> |
three dimensions. The cells were longer along the $z$ axis and the |
343 |
> |
space was divided into $N$ slabs along this axis (typically $N=20$). |
344 |
> |
The top slab ($n=1$) was designated the ``cold'' slab, while the |
345 |
> |
central slab ($n= N/2 + 1$) was designated the ``hot'' slab. In all |
346 |
> |
cases, simulations were first thermalized in canonical ensemble (NVT) |
347 |
> |
using a Nos\'{e}-Hoover thermostat.\cite{Hoover85} then equilibrated in |
348 |
> |
microcanonical ensemble (NVE) before introducing any non-equilibrium |
349 |
> |
method. |
350 |
|
|
351 |
< |
\section{Computational Details} |
283 |
< |
Our simulation consists of a series of systems. All of these |
284 |
< |
simulations were run with the OpenMD simulation software |
285 |
< |
package\cite{Meineke:2005gd} integrated with RNEMD methods. |
351 |
> |
\subsection{RNEMD with M\"{u}ller-Plathe swaps} |
352 |
|
|
353 |
< |
A Lennard-Jones fluid system was built and tested first. In order to |
354 |
< |
compare our method with swapping RNEMD, a series of simulations were |
355 |
< |
performed to calculate the shear viscosity and thermal conductivity of |
290 |
< |
argon. 2592 atoms were in a orthorhombic cell, which was ${10.06\sigma |
291 |
< |
\times 10.06\sigma \times 30.18\sigma}$ by size. The reduced density |
292 |
< |
${\rho^* = \rho\sigma^3}$ was thus 0.85, which enabled direct |
293 |
< |
comparison between our results and others. These simulations used |
294 |
< |
velocity Verlet algorithm with reduced timestep ${\tau^* = |
295 |
< |
4.6\times10^{-4}}$. |
353 |
> |
In order to compare our new methodology with the original |
354 |
> |
M\"{u}ller-Plathe swapping algorithm,\cite{ISI:000080382700030} we |
355 |
> |
first performed simulations using the original technique. |
356 |
|
|
357 |
< |
For shear viscosity calculation, the reduced temperature was ${T^* = |
298 |
< |
k_B T/\varepsilon = 0.72}$. Simulations were first equilibrated in canonical |
299 |
< |
ensemble (NVT), then equilibrated in microcanonical ensemble |
300 |
< |
(NVE). Establishing and stablizing momentum gradient were followed |
301 |
< |
also in NVE ensemble. For the swapping part, Muller-Plathe's algorithm was |
302 |
< |
adopted.\cite{ISI:000080382700030} The simulation box was under |
303 |
< |
periodic boundary condition, and devided into ${N = 20}$ slabs. In each swap, |
304 |
< |
the top slab ${(n = 1)}$ exchange the most negative $x$ momentum with the |
305 |
< |
most positive $x$ momentum in the center slab ${(n = N/2 + 1)}$. Referred |
306 |
< |
to Tenney {\it et al.}\cite{ISI:000273472300004}, a series of swapping |
307 |
< |
frequency were chosen. According to each result from swapping |
308 |
< |
RNEMD, scaling RNEMD simulations were run with the target momentum |
309 |
< |
flux set to produce a similar momentum flux and shear |
310 |
< |
rate. Furthermore, various scaling frequencies can be tested for one |
311 |
< |
single swapping rate. To compare the performance between swapping and |
312 |
< |
scaling method, temperatures of different dimensions in all the slabs |
313 |
< |
were observed. Most of the simulations include $10^5$ steps of |
314 |
< |
equilibration without imposing momentum flux, $10^5$ steps of |
315 |
< |
stablization with imposing momentum transfer, and $10^6$ steps of data |
316 |
< |
collection under RNEMD. For relatively high momentum flux simulations, |
317 |
< |
${5\times10^5}$ step data collection is sufficient. For some low momentum |
318 |
< |
flux simulations, ${2\times10^6}$ steps were necessary. |
357 |
> |
\subsection{RNEMD with NIVS scaling} |
358 |
|
|
359 |
< |
After each simulation, the shear viscosity was calculated in reduced |
360 |
< |
unit. The momentum flux was calculated with total unphysical |
361 |
< |
transferred momentum ${P_x}$ and data collection time $t$: |
359 |
> |
For each simulation utilizing the swapping method, a corresponding |
360 |
> |
NIVS-RNEMD simulation was carried out using a target momentum flux set |
361 |
> |
to produce a the same momentum or energy flux exhibited in the |
362 |
> |
swapping simulation. |
363 |
> |
|
364 |
> |
To test the temperature homogeneity (and to compute transport |
365 |
> |
coefficients), directional momentum and temperature distributions were |
366 |
> |
accumulated for molecules in each of the slabs. |
367 |
> |
|
368 |
> |
\subsection{Shear viscosities} |
369 |
> |
|
370 |
> |
The momentum flux was calculated using the total non-physical momentum |
371 |
> |
transferred (${P_x}$) and the data collection time ($t$): |
372 |
|
\begin{equation} |
373 |
|
j_z(p_x) = \frac{P_x}{2 t L_x L_y} |
374 |
|
\end{equation} |
375 |
< |
And the velocity gradient ${\langle \partial v_x /\partial z \rangle}$ |
376 |
< |
can be obtained by a linear regression of the velocity profile. From |
377 |
< |
the shear viscosity $\eta$ calculated with the above parameters, one |
378 |
< |
can further convert it into reduced unit ${\eta^* = \eta \sigma^2 |
379 |
< |
(\varepsilon m)^{-1/2}}$. |
375 |
> |
where $L_x$ and $L_y$ denote the $x$ and $y$ lengths of the simulation |
376 |
> |
box. The factor of two in the denominator is present because physical |
377 |
> |
momentum transfer occurs in two directions due to our periodic |
378 |
> |
boundary conditions. The velocity gradient ${\langle \partial v_x |
379 |
> |
/\partial z \rangle}$ was obtained using linear regression of the |
380 |
> |
velocity profiles in the bins. For Lennard-Jones simulations, shear |
381 |
> |
viscosities are reporte in reduced units (${\eta^* = \eta \sigma^2 |
382 |
> |
(\varepsilon m)^{-1/2}}$). |
383 |
|
|
384 |
< |
For thermal conductivity calculation, simulations were first run under |
333 |
< |
reduced temperature ${T^* = 0.72}$ in NVE ensemble. Muller-Plathe's |
334 |
< |
algorithm was adopted in the swapping method. Under identical |
335 |
< |
simulation box parameters, in each swap, the top slab exchange the |
336 |
< |
molecule with least kinetic energy with the molecule in the center |
337 |
< |
slab with most kinetic energy, unless this ``coldest'' molecule in the |
338 |
< |
``hot'' slab is still ``hotter'' than the ``hottest'' molecule in the ``cold'' |
339 |
< |
slab. According to swapping RNEMD results, target energy flux for |
340 |
< |
scaling RNEMD simulations can be set. Also, various scaling |
341 |
< |
frequencies can be tested for one target energy flux. To compare the |
342 |
< |
performance between swapping and scaling method, distributions of |
343 |
< |
velocity and speed in different slabs were observed. |
384 |
> |
\subsection{Thermal Conductivities} |
385 |
|
|
386 |
< |
For each swapping rate, thermal conductivity was calculated in reduced |
387 |
< |
unit. The energy flux was calculated similarly to the momentum flux, |
388 |
< |
with total unphysical transferred energy ${E_{total}}$ and data collection |
348 |
< |
time $t$: |
386 |
> |
The energy flux was calculated similarly to the momentum flux, using |
387 |
> |
the total non-physical energy transferred (${E_{total}}$) and the data |
388 |
> |
collection time $t$: |
389 |
|
\begin{equation} |
390 |
|
J_z = \frac{E_{total}}{2 t L_x L_y} |
391 |
|
\end{equation} |
392 |
< |
And the temperature gradient ${\langle\partial T/\partial z\rangle}$ |
393 |
< |
can be obtained by a linear regression of the temperature |
394 |
< |
profile. From the thermal conductivity $\lambda$ calculated, one can |
395 |
< |
further convert it into reduced unit ${\lambda^*=\lambda \sigma^2 |
396 |
< |
m^{1/2} k_B^{-1}\varepsilon^{-1/2}}$. |
392 |
> |
The temperature gradient ${\langle\partial T/\partial z\rangle}$ was |
393 |
> |
obtained by a linear regression of the temperature profile. For |
394 |
> |
Lennard-Jones simulations, thermal conductivities are reported in |
395 |
> |
reduced units (${\lambda^*=\lambda \sigma^2 m^{1/2} |
396 |
> |
k_B^{-1}\varepsilon^{-1/2}}$). |
397 |
|
|
398 |
< |
Another series of our simulation is to calculate the interfacial |
359 |
< |
thermal conductivity of a Au/H${_2}$O system. Respective calculations of |
360 |
< |
water (SPC/E) and gold (QSC) thermal conductivity were performed and |
361 |
< |
compared with current results to ensure the validity of |
362 |
< |
NIVS-RNEMD. After that, the mixture system was simulated. |
398 |
> |
\subsection{Interfacial Thermal Conductivities} |
399 |
|
|
400 |
< |
\section{Results And Discussion} |
400 |
> |
For materials with a relatively low interfacial conductance, and in |
401 |
> |
cases where the flux between the materials is small, the bulk regions |
402 |
> |
on either side of an interface rapidly come to a state in which the |
403 |
> |
two phases have relatively homogeneous (but distinct) temperatures. |
404 |
> |
In calculating the interfacial thermal conductivity $G$, this |
405 |
> |
assumption was made, and the conductance can be approximated as: |
406 |
> |
|
407 |
> |
\begin{equation} |
408 |
> |
G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_{gold}\rangle - |
409 |
> |
\langle T_{water}\rangle \right)} |
410 |
> |
\label{interfaceCalc} |
411 |
> |
\end{equation} |
412 |
> |
where ${E_{total}}$ is the imposed non-physical kinetic energy |
413 |
> |
transfer and ${\langle T_{gold}\rangle}$ and ${\langle |
414 |
> |
T_{water}\rangle}$ are the average observed temperature of gold and |
415 |
> |
water phases respectively. |
416 |
> |
|
417 |
> |
\section{Results} |
418 |
> |
|
419 |
> |
\subsection{Lennard-Jones Fluid} |
420 |
> |
2592 Lennard-Jones atoms were placed in an orthorhombic cell |
421 |
> |
${10.06\sigma \times 10.06\sigma \times 30.18\sigma}$ on a side. The |
422 |
> |
reduced density ${\rho^* = \rho\sigma^3}$ was thus 0.85, which enabled |
423 |
> |
direct comparison between our results and previous methods. These |
424 |
> |
simulations were carried out with a reduced timestep ${\tau^* = |
425 |
> |
4.6\times10^{-4}}$. For the shear viscosity calculations, the mean |
426 |
> |
temperature was ${T^* = k_B T/\varepsilon = 0.72}$. For thermal |
427 |
> |
conductivity calculations, simulations were first run under reduced |
428 |
> |
temperature ${\langle T^*\rangle = 0.72}$ in the microcanonical |
429 |
> |
ensemble, but other temperatures ([XXX, YYY, and ZZZ]) were also |
430 |
> |
sampled. The simulations included $10^5$ steps of equilibration |
431 |
> |
without any momentum flux, $10^5$ steps of stablization with an |
432 |
> |
imposed momentum transfer to create a gradient, and $10^6$ steps of |
433 |
> |
data collection under RNEMD. |
434 |
> |
|
435 |
> |
Our thermal conductivity calculations show that the NIVS method agrees |
436 |
> |
well with the swapping method. Four different swap intervals were |
437 |
> |
tested (Table \ref{thermalLJRes}). With a fixed 10 fs [WHY NOT REDUCED |
438 |
> |
UNITS???] scaling interval, the target exchange kinetic energy |
439 |
> |
produced equivalent kinetic energy flux as in the swapping method. |
440 |
> |
Similar thermal gradients were observed with similar thermal flux |
441 |
> |
under the two different methods (Figure \ref{thermalGrad}). |
442 |
> |
|
443 |
> |
\begin{table*} |
444 |
> |
\begin{minipage}{\linewidth} |
445 |
> |
\begin{center} |
446 |
> |
|
447 |
> |
\caption{Thermal conductivity (in reduced units) of a |
448 |
> |
Lennard-Jones fluid at ${\langle T^* \rangle = 0.72}$ and |
449 |
> |
${\rho^* = 0.85}$ for the swapping and scaling methods at |
450 |
> |
various kinetic energy exchange rates. Uncertainties are |
451 |
> |
indicated in parentheses.} |
452 |
> |
|
453 |
> |
\begin{tabular}{|cc|cc|} |
454 |
> |
\hline |
455 |
> |
\multicolumn{2}{|c|}{Swapping RNEMD} & |
456 |
> |
\multicolumn{2}{|c|}{NIVS-RNEMD} \\ |
457 |
> |
\hline |
458 |
> |
Swap Interval (fs) & $\lambda^*_{swap}$ & Equilvalent $J_z^*$ & $\lambda^*_{scale}$\\ |
459 |
> |
\hline |
460 |
> |
250 & 7.03(0.34) & 0.16 & 7.30(0.10)\\ |
461 |
> |
500 & 7.03(0.14) & 0.09 & 6.95(0.09)\\ |
462 |
> |
1000 & 6.91(0.42) & 0.047 & 7.19(0.07)\\ |
463 |
> |
2000 & 7.52(0.15) & 0.024 & 7.19(0.28)\\ |
464 |
> |
\hline |
465 |
> |
\end{tabular} |
466 |
> |
\label{thermalLJRes} |
467 |
> |
\end{center} |
468 |
> |
\end{minipage} |
469 |
> |
\end{table*} |
470 |
> |
|
471 |
> |
\begin{figure} |
472 |
> |
\includegraphics[width=\linewidth]{thermalGrad} |
473 |
> |
\caption{NIVS-RNEMD method creates similar temperature gradients |
474 |
> |
compared with the swapping method under a variety of imposed kinetic |
475 |
> |
energy flux values.} |
476 |
> |
\label{thermalGrad} |
477 |
> |
\end{figure} |
478 |
> |
|
479 |
> |
During these simulations, velocities were recorded every 1000 steps |
480 |
> |
and was used to produce distributions of both velocity and speed in |
481 |
> |
each of the slabs. From these distributions, we observed that under |
482 |
> |
relatively high non-physical kinetic energy flux, the spee of |
483 |
> |
molecules in slabs where swapping occured could deviate from the |
484 |
> |
Maxwell-Boltzmann distribution. This behavior was also noted by Tenney |
485 |
> |
and Maginn.\cite{Maginn:2010} Figure \ref{thermalHist} shows how these |
486 |
> |
distributions deviate from an ideal distribution. In the ``hot'' slab, |
487 |
> |
the probability density is notched at low speeds and has a substantial |
488 |
> |
shoulder at higher speeds relative to the ideal MB distribution. In |
489 |
> |
the cold slab, the opposite notching and shouldering occurs. This |
490 |
> |
phenomenon is more obvious at higher swapping rates. |
491 |
> |
|
492 |
> |
In the velocity distributions, the ideal Gaussian peak is |
493 |
> |
substantially flattened in the hot slab, and is overly sharp (with |
494 |
> |
truncated wings) in the cold slab. This problem is rooted in the |
495 |
> |
mechanism of the swapping method. Continually depleting low (high) |
496 |
> |
speed particles in the high (low) temperature slab is not complemented |
497 |
> |
by diffusions of low (high) speed particles from neighboring slabs, |
498 |
> |
unless the swapping rate is sufficiently small. Simutaneously, surplus |
499 |
> |
low speed particles in the low temperature slab do not have sufficient |
500 |
> |
time to diffuse to neighboring slabs. Since the thermal exchange rate |
501 |
> |
must reach a minimum level to produce an observable thermal gradient, |
502 |
> |
the swapping-method RNEMD has a relatively narrow choice of exchange |
503 |
> |
times that can be utilized. |
504 |
> |
|
505 |
> |
For comparison, NIVS-RNEMD produces a speed distribution closer to the |
506 |
> |
Maxwell-Boltzmann curve (Figure \ref{thermalHist}). The reason for |
507 |
> |
this is simple; upon velocity scaling, a Gaussian distribution remains |
508 |
> |
Gaussian. Although a single scaling move is non-isotropic in three |
509 |
> |
dimensions, our criteria for choosing a set of scaling coefficients |
510 |
> |
helps maintain the distributions as close to isotropic as possible. |
511 |
> |
Consequently, NIVS-RNEMD is able to impose an unphysical thermal flux |
512 |
> |
as the previous RNEMD methods but without large perturbations to the |
513 |
> |
velocity distributions in the two slabs. |
514 |
> |
|
515 |
> |
\begin{figure} |
516 |
> |
\includegraphics[width=\linewidth]{thermalHist} |
517 |
> |
\caption{Speed distribution for thermal conductivity using a) |
518 |
> |
``swapping'' and b) NIVS- RNEMD methods. Shown is from the |
519 |
> |
simulations with an exchange or equilvalent exchange interval of 250 |
520 |
> |
fs. In circled areas, distributions from ``swapping'' RNEMD |
521 |
> |
simulation have deviation from ideal Maxwell-Boltzmann distribution |
522 |
> |
(curves fit for each distribution).} |
523 |
> |
\label{thermalHist} |
524 |
> |
\end{figure} |
525 |
> |
|
526 |
> |
\subsection{Bulk SPC/E water} |
527 |
> |
|
528 |
> |
We compared the thermal conductivity of SPC/E water using NIVS-RNEMD |
529 |
> |
to the work of Bedrov {\it et al.}\cite{Bedrov:2000}, which employed |
530 |
> |
the original swapping RNEMD method. Bedrov {\it et |
531 |
> |
al.}\cite{Bedrov:2000} argued that exchange of the molecule |
532 |
> |
center-of-mass velocities instead of single atom velocities in a |
533 |
> |
molecule conserves the total kinetic energy and linear momentum. This |
534 |
> |
principle is also adopted in our simulations. Scaling was applied to |
535 |
> |
the center-of-mass velocities of the rigid bodies of SPC/E model water |
536 |
> |
molecules. |
537 |
> |
|
538 |
> |
To construct the simulations, a simulation box consisting of 1000 |
539 |
> |
molecules were first equilibrated under ambient pressure and |
540 |
> |
temperature conditions using the isobaric-isothermal (NPT) |
541 |
> |
ensemble.\cite{melchionna93} A fixed volume was chosen to match the |
542 |
> |
average volume observed in the NPT simulations, and this was followed |
543 |
> |
by equilibration, first in the canonical (NVT) ensemble, followed by a |
544 |
> |
[XXX ps] period under constant-NVE conditions without any momentum |
545 |
> |
flux. [YYY ps] was allowed to stabilize the system with an imposed |
546 |
> |
momentum transfer to create a gradient, and [ZZZ ps] was alotted for |
547 |
> |
data collection under RNEMD. |
548 |
> |
|
549 |
> |
As shown in Figure \ref{spceGrad}, temperature gradients were |
550 |
> |
established similar to the previous work. However, the average |
551 |
> |
temperature of our system is 300K, while that in Bedrov {\it et al.} |
552 |
> |
is 318K, which would be attributed for part of the difference between |
553 |
> |
the final calculation results (Table \ref{spceThermal}). [WHY DIDN'T |
554 |
> |
WE DO 318 K?] Both methods yield values in reasonable agreement with |
555 |
> |
experiment [DONE AT WHAT TEMPERATURE?] |
556 |
> |
|
557 |
> |
\begin{figure} |
558 |
> |
\includegraphics[width=\linewidth]{spceGrad} |
559 |
> |
\caption{Temperature gradients in SPC/E water thermal conductivity |
560 |
> |
simulations.} |
561 |
> |
\label{spceGrad} |
562 |
> |
\end{figure} |
563 |
> |
|
564 |
> |
\begin{table*} |
565 |
> |
\begin{minipage}{\linewidth} |
566 |
> |
\begin{center} |
567 |
> |
|
568 |
> |
\caption{Thermal conductivity of SPC/E water under various |
569 |
> |
imposed thermal gradients. Uncertainties are indicated in |
570 |
> |
parentheses.} |
571 |
> |
|
572 |
> |
\begin{tabular}{|c|ccc|} |
573 |
> |
\hline |
574 |
> |
$\langle dT/dz\rangle$(K/\AA) & \multicolumn{3}{|c|}{$\lambda |
575 |
> |
(\mathrm{W m}^{-1} \mathrm{K}^{-1})$} \\ |
576 |
> |
& This work (300K) & Previous simulations (318K)\cite{Bedrov:2000} & |
577 |
> |
Experiment\cite{WagnerKruse}\\ |
578 |
> |
\hline |
579 |
> |
0.38 & 0.816(0.044) & & 0.64\\ |
580 |
> |
0.81 & 0.770(0.008) & 0.784 & \\ |
581 |
> |
1.54 & 0.813(0.007) & 0.730 & \\ |
582 |
> |
\hline |
583 |
> |
\end{tabular} |
584 |
> |
\label{spceThermal} |
585 |
> |
\end{center} |
586 |
> |
\end{minipage} |
587 |
> |
\end{table*} |
588 |
> |
|
589 |
> |
\subsection{Crystalline Gold} |
590 |
> |
|
591 |
> |
To see how the method performed in a solid, we calculated thermal |
592 |
> |
conductivities using two atomistic models for gold. Several different |
593 |
> |
potential models have been developed that reasonably describe |
594 |
> |
interactions in transition metals. In particular, the Embedded Atom |
595 |
> |
Model ({\sc eam})~\cite{PhysRevB.33.7983} and Sutton-Chen ({\sc |
596 |
> |
sc})~\cite{Chen90} potential have been used to study a wide range of |
597 |
> |
phenomena in both bulk materials and |
598 |
> |
nanoparticles.\cite{ISI:000207079300006,Clancy:1992,Vardeman:2008fk,Vardeman-II:2001jn,ShibataT._ja026764r,Sankaranarayanan:2005lr,Chui:2003fk,Wang:2005qy,Medasani:2007uq} |
599 |
> |
Both potentials are based on a model of a metal which treats the |
600 |
> |
nuclei and core electrons as pseudo-atoms embedded in the electron |
601 |
> |
density due to the valence electrons on all of the other atoms in the |
602 |
> |
system. The {\sc sc} potential has a simple form that closely |
603 |
> |
resembles the Lennard Jones potential, |
604 |
> |
\begin{equation} |
605 |
> |
\label{eq:SCP1} |
606 |
> |
U_{tot}=\sum _{i}\left[ \frac{1}{2}\sum _{j\neq i}D_{ij}V^{pair}_{ij}(r_{ij})-c_{i}D_{ii}\sqrt{\rho_{i}}\right] , |
607 |
> |
\end{equation} |
608 |
> |
where $V^{pair}_{ij}$ and $\rho_{i}$ are given by |
609 |
> |
\begin{equation} |
610 |
> |
\label{eq:SCP2} |
611 |
> |
V^{pair}_{ij}(r)=\left( \frac{\alpha_{ij}}{r_{ij}}\right)^{n_{ij}}, \rho_{i}=\sum_{j\neq i}\left( \frac{\alpha_{ij}}{r_{ij}}\right) ^{m_{ij}}. |
612 |
> |
\end{equation} |
613 |
> |
$V^{pair}_{ij}$ is a repulsive pairwise potential that accounts for |
614 |
> |
interactions between the pseudoatom cores. The $\sqrt{\rho_i}$ term in |
615 |
> |
Eq. (\ref{eq:SCP1}) is an attractive many-body potential that models |
616 |
> |
the interactions between the valence electrons and the cores of the |
617 |
> |
pseudo-atoms. $D_{ij}$, $D_{ii}$ set the appropriate overall energy |
618 |
> |
scale, $c_i$ scales the attractive portion of the potential relative |
619 |
> |
to the repulsive interaction and $\alpha_{ij}$ is a length parameter |
620 |
> |
that assures a dimensionless form for $\rho$. These parameters are |
621 |
> |
tuned to various experimental properties such as the density, cohesive |
622 |
> |
energy, and elastic moduli for FCC transition metals. The quantum |
623 |
> |
Sutton-Chen ({\sc q-sc}) formulation matches these properties while |
624 |
> |
including zero-point quantum corrections for different transition |
625 |
> |
metals.\cite{PhysRevB.59.3527} The {\sc eam} functional forms differ |
626 |
> |
slightly from {\sc sc} but the overall method is very similar. |
627 |
> |
|
628 |
> |
In this work, we have utilized both the {\sc eam} and the {\sc q-sc} |
629 |
> |
potentials to test the behavior of scaling RNEMD. |
630 |
> |
|
631 |
> |
A face-centered-cubic (FCC) lattice was prepared containing 2880 Au |
632 |
> |
atoms. [LxMxN UNIT CELLS]. Simulations were run both with and |
633 |
> |
without isobaric-isothermal (NPT)~\cite{melchionna93} |
634 |
> |
pre-equilibration at a target pressure of 1 atm. When equilibrated |
635 |
> |
under NPT conditions, our simulation box expanded by approximately 1\% |
636 |
> |
Following adjustment of the box volume, equilibrations in both the |
637 |
> |
canonical and microcanonical ensembles were carried out. With the |
638 |
> |
simulation cell divided evenly into 10 slabs, different thermal |
639 |
> |
gradients were established by applying a set of target thermal |
640 |
> |
transfer fluxes. |
641 |
> |
|
642 |
> |
The results for the thermal conductivity of gold are shown in Table |
643 |
> |
\ref{AuThermal}. In these calculations, the end and middle slabs were |
644 |
> |
excluded in thermal gradient linear regession. {\sc eam} predicts |
645 |
> |
slightly larger thermal conductivities than {\sc q-sc}. However, both |
646 |
> |
values are smaller than experimental value by a factor of more than |
647 |
> |
200. This behavior has been observed previously by Richardson and |
648 |
> |
Clancy, and has been attributed to the lack of electronic effects in |
649 |
> |
these force fields.\cite{Clancy:1992} The non-equilibrium MD method |
650 |
> |
they employed in their simulations produced comparable results to |
651 |
> |
ours. It should be noted that the density of the metal being |
652 |
> |
simulated also greatly affects the thermal conductivity. (Table |
653 |
> |
\ref{AuThermal}) [IN VOLUME OR LINEAR DIMENSIONS]. With an expanded |
654 |
> |
lattice, lower thermal conductance is expected (and observed). We also |
655 |
> |
observed a decrease in thermal conductance at higher temperatures, a |
656 |
> |
trend that agrees with experimental measurements [PAGE |
657 |
> |
NUMBERS?].\cite{AshcroftMermin} |
658 |
> |
|
659 |
> |
\begin{table*} |
660 |
> |
\begin{minipage}{\linewidth} |
661 |
> |
\begin{center} |
662 |
> |
|
663 |
> |
\caption{Calculated thermal conductivity of crystalline gold |
664 |
> |
using two related force fields. Calculations were done at both |
665 |
> |
experimental and equilibrated densities and at a range of |
666 |
> |
temperatures and thermal flux rates. Uncertainties are |
667 |
> |
indicated in parentheses. [CLANCY COMPARISON? SWAPPING |
668 |
> |
COMPARISON?]} |
669 |
> |
|
670 |
> |
\begin{tabular}{|c|c|c|cc|} |
671 |
> |
\hline |
672 |
> |
Force Field & $\rho$ (g/cm$^3$) & ${\langle T\rangle}$ (K) & |
673 |
> |
$\langle dT/dz\rangle$ (K/\AA) & $\lambda (\mathrm{W m}^{-1} \mathrm{K}^{-1})$\\ |
674 |
> |
\hline |
675 |
> |
\multirow{7}{*}{\sc q-sc} & \multirow{3}{*}{19.188} & \multirow{3}{*}{300} & 1.44 & 1.10(0.06)\\ |
676 |
> |
& & & 2.86 & 1.08(0.05)\\ |
677 |
> |
& & & 5.14 & 1.15(0.07)\\\cline{2-5} |
678 |
> |
& \multirow{4}{*}{19.263} & \multirow{2}{*}{300} & 2.31 & 1.25(0.06)\\ |
679 |
> |
& & & 3.02 & 1.26(0.05)\\\cline{3-5} |
680 |
> |
& & \multirow{2}{*}{575} & 3.02 & 1.02(0.07)\\ |
681 |
> |
& & & 4.84 & 0.92(0.05)\\ |
682 |
> |
\hline |
683 |
> |
\multirow{8}{*}{\sc eam} & \multirow{3}{*}{19.045} & \multirow{3}{*}{300} & 1.24 & 1.24(0.16)\\ |
684 |
> |
& & & 2.06 & 1.37(0.04)\\ |
685 |
> |
& & & 2.55 & 1.41(0.07)\\\cline{2-5} |
686 |
> |
& \multirow{5}{*}{19.263} & \multirow{3}{*}{300} & 1.06 & 1.45(0.13)\\ |
687 |
> |
& & & 2.04 & 1.41(0.07)\\ |
688 |
> |
& & & 2.41 & 1.53(0.10)\\\cline{3-5} |
689 |
> |
& & \multirow{2}{*}{575} & 2.82 & 1.08(0.03)\\ |
690 |
> |
& & & 4.14 & 1.08(0.05)\\ |
691 |
> |
\hline |
692 |
> |
\end{tabular} |
693 |
> |
\label{AuThermal} |
694 |
> |
\end{center} |
695 |
> |
\end{minipage} |
696 |
> |
\end{table*} |
697 |
> |
|
698 |
> |
\subsection{Thermal Conductance at the Au/H$_2$O interface} |
699 |
> |
The most attractive aspect of the scaling approach for RNEMD is the |
700 |
> |
ability to use the method in non-homogeneous systems, where molecules |
701 |
> |
of different identities are segregated in different slabs. To test |
702 |
> |
this application, we simulated a Gold (111) / water interface. To |
703 |
> |
construct the interface, a box containing a lattice of 1188 Au atoms |
704 |
> |
(with the 111 surface in the +z and -z directions) was allowed to |
705 |
> |
relax under ambient temperature and pressure. A separate (but |
706 |
> |
identically sized) box of SPC/E water was also equilibrated at ambient |
707 |
> |
conditions. The two boxes were combined by removing all water |
708 |
> |
molecules withing 3 \AA radius of any gold atom. The final |
709 |
> |
configuration contained 1862 SPC/E water molecules. |
710 |
> |
|
711 |
> |
After simulations of bulk water and crystal gold, a mixture system was |
712 |
> |
constructed, consisting of 1188 Au atoms and 1862 H$_2$O |
713 |
> |
molecules. Spohr potential was adopted in depicting the interaction |
714 |
> |
between metal atom and water molecule.\cite{ISI:000167766600035} A |
715 |
> |
similar protocol of equilibration was followed. Several thermal |
716 |
> |
gradients was built under different target thermal flux. It was found |
717 |
> |
out that compared to our previous simulation systems, the two phases |
718 |
> |
could have large temperature difference even under a relatively low |
719 |
> |
thermal flux. |
720 |
> |
|
721 |
> |
|
722 |
> |
After simulations of homogeneous water and gold systems using |
723 |
> |
NIVS-RNEMD method were proved valid, calculation of gold/water |
724 |
> |
interfacial thermal conductivity was followed. It is found out that |
725 |
> |
the low interfacial conductance is probably due to the hydrophobic |
726 |
> |
surface in our system. Figure \ref{interface} (a) demonstrates mass |
727 |
> |
density change along $z$-axis, which is perpendicular to the |
728 |
> |
gold/water interface. It is observed that water density significantly |
729 |
> |
decreases when approaching the surface. Under this low thermal |
730 |
> |
conductance, both gold and water phase have sufficient time to |
731 |
> |
eliminate temperature difference inside respectively (Figure |
732 |
> |
\ref{interface} b). With indistinguishable temperature difference |
733 |
> |
within respective phase, it is valid to assume that the temperature |
734 |
> |
difference between gold and water on surface would be approximately |
735 |
> |
the same as the difference between the gold and water phase. This |
736 |
> |
assumption enables convenient calculation of $G$ using |
737 |
> |
Eq. \ref{interfaceCalc} instead of measuring temperatures of thin |
738 |
> |
layer of water and gold close enough to surface, which would have |
739 |
> |
greater fluctuation and lower accuracy. Reported results (Table |
740 |
> |
\ref{interfaceRes}) are of two orders of magnitude smaller than our |
741 |
> |
calculations on homogeneous systems, and thus have larger relative |
742 |
> |
errors than our calculation results on homogeneous systems. |
743 |
> |
|
744 |
> |
\begin{figure} |
745 |
> |
\includegraphics[width=\linewidth]{interface} |
746 |
> |
\caption{Simulation results for Gold/Water interfacial thermal |
747 |
> |
conductivity: (a) Significant water density decrease is observed on |
748 |
> |
crystalline gold surface, which indicates low surface contact and |
749 |
> |
leads to low thermal conductance. (b) Temperature profiles for a |
750 |
> |
series of simulations. Temperatures of different slabs in the same |
751 |
> |
phase show no significant differences.} |
752 |
> |
\label{interface} |
753 |
> |
\end{figure} |
754 |
> |
|
755 |
> |
\begin{table*} |
756 |
> |
\begin{minipage}{\linewidth} |
757 |
> |
\begin{center} |
758 |
> |
|
759 |
> |
\caption{Calculation results for interfacial thermal conductivity |
760 |
> |
at ${\langle T\rangle \sim}$ 300K at various thermal exchange |
761 |
> |
rates. Errors of calculations in parentheses. } |
762 |
> |
|
763 |
> |
\begin{tabular}{cccc} |
764 |
> |
\hline |
765 |
> |
$J_z$ (MW/m$^2$) & $T_{gold}$ (K) & $T_{water}$ (K) & $G$ |
766 |
> |
(MW/m$^2$/K)\\ |
767 |
> |
\hline |
768 |
> |
98.0 & 355.2 & 295.8 & 1.65(0.21) \\ |
769 |
> |
78.8 & 343.8 & 298.0 & 1.72(0.32) \\ |
770 |
> |
73.6 & 344.3 & 298.0 & 1.59(0.24) \\ |
771 |
> |
49.2 & 330.1 & 300.4 & 1.65(0.35) \\ |
772 |
> |
\hline |
773 |
> |
\end{tabular} |
774 |
> |
\label{interfaceRes} |
775 |
> |
\end{center} |
776 |
> |
\end{minipage} |
777 |
> |
\end{table*} |
778 |
> |
|
779 |
|
\subsection{Shear Viscosity} |
780 |
|
Our calculations (Table \ref{shearRate}) shows that scale RNEMD method |
781 |
|
produced comparable shear viscosity to swap RNEMD method. In Table |
786 |
|
momentum flux would theoretically result in swap method. All the scale |
787 |
|
method results were from simulations that had a scaling interval of 10 |
788 |
|
time steps. The average molecular momentum gradients of these samples |
789 |
< |
are shown in Figure \ref{shearGrad}. |
789 |
> |
are shown in Figure \ref{shear} (a) and (b). |
790 |
|
|
791 |
|
\begin{table*} |
792 |
|
\begin{minipage}{\linewidth} |
797 |
|
methods at various momentum exchange rates. Results in reduced |
798 |
|
unit. Errors of calculations in parentheses. } |
799 |
|
|
800 |
< |
\begin{tabular}{ccc} |
800 |
> |
\begin{tabular}{ccccc} |
801 |
> |
Swapping method & & & NIVS-RNEMD & \\ |
802 |
|
\hline |
803 |
< |
Series & $\eta^*_{swap}$ & $\eta^*_{scale}$\\ |
803 |
> |
Swap Interval (fs) & $\eta^*_{swap}$ & & Equilvalent $j_p^*(v_x)$ & |
804 |
> |
$\eta^*_{scale}$\\ |
805 |
|
\hline |
806 |
< |
20-500 & 3.64(0.05) & 3.76(0.09)\\ |
807 |
< |
20-1000 & 3.52(0.16) & 3.66(0.06)\\ |
808 |
< |
20-2000 & 3.72(0.05) & 3.32(0.18)\\ |
809 |
< |
20-2500 & 3.42(0.06) & 3.43(0.08)\\ |
806 |
> |
500 & 3.64(0.05) & & 0.09 & 3.76(0.09)\\ |
807 |
> |
1000 & 3.52(0.16) & & 0.046 & 3.66(0.06)\\ |
808 |
> |
2000 & 3.72(0.05) & & 0.024 & 3.32(0.18)\\ |
809 |
> |
2500 & 3.42(0.06) & & 0.019 & 3.43(0.08)\\ |
810 |
|
\hline |
811 |
|
\end{tabular} |
812 |
|
\label{shearRate} |
815 |
|
\end{table*} |
816 |
|
|
817 |
|
\begin{figure} |
818 |
< |
\includegraphics[width=\linewidth]{shearGrad} |
819 |
< |
\caption{Average momentum gradients of shear viscosity simulations} |
820 |
< |
\label{shearGrad} |
818 |
> |
\includegraphics[width=\linewidth]{shear} |
819 |
> |
\caption{Average momentum gradients in shear viscosity simulations, |
820 |
> |
using (a) ``swapping'' method and (b) NIVS-RNEMD method |
821 |
> |
respectively. (c) Temperature difference among x and y, z dimensions |
822 |
> |
observed when using NIVS-RNEMD with equivalent exchange interval of |
823 |
> |
500 fs.} |
824 |
> |
\label{shear} |
825 |
|
\end{figure} |
826 |
|
|
407 |
– |
\begin{figure} |
408 |
– |
\includegraphics[width=\linewidth]{shearTempScale} |
409 |
– |
\caption{Temperature profile for scaling RNEMD simulation.} |
410 |
– |
\label{shearTempScale} |
411 |
– |
\end{figure} |
827 |
|
However, observations of temperatures along three dimensions show that |
828 |
|
inhomogeneity occurs in scaling RNEMD simulations, particularly in the |
829 |
< |
two slabs which were scaled. Figure \ref{shearTempScale} indicate that with |
829 |
> |
two slabs which were scaled. Figure \ref{shear} (c) indicate that with |
830 |
|
relatively large imposed momentum flux, the temperature difference among $x$ |
831 |
|
and the other two dimensions was significant. This would result from the |
832 |
|
algorithm of scaling method. From Eq. \ref{eq:fluxPlane}, after |
846 |
|
exchange momentum flux simulations makes scaling RNEMD method less |
847 |
|
attractive than swapping RNEMD in shear viscosity calculation. |
848 |
|
|
849 |
< |
\subsection{Thermal Conductivity} |
849 |
> |
\section{Conclusions} |
850 |
> |
NIVS-RNEMD simulation method is developed and tested on various |
851 |
> |
systems. Simulation results demonstrate its validity in thermal |
852 |
> |
conductivity calculations, from Lennard-Jones fluid to multi-atom |
853 |
> |
molecule like water and metal crystals. NIVS-RNEMD improves |
854 |
> |
non-Boltzmann-Maxwell distributions, which exist in previous RNEMD |
855 |
> |
methods. Furthermore, it develops a valid means for unphysical thermal |
856 |
> |
transfer between different species of molecules, and thus extends its |
857 |
> |
applicability to interfacial systems. Our calculation of gold/water |
858 |
> |
interfacial thermal conductivity demonstrates this advantage over |
859 |
> |
previous RNEMD methods. NIVS-RNEMD has also limited application on |
860 |
> |
shear viscosity calculations, but could cause temperature difference |
861 |
> |
among different dimensions under high momentum flux. Modification is |
862 |
> |
necessary to extend the applicability of NIVS-RNEMD in shear viscosity |
863 |
> |
calculations. |
864 |
|
|
436 |
– |
Our thermal conductivity calculations also show that scaling method results |
437 |
– |
agree with swapping method. Table \ref{thermal} lists our simulation |
438 |
– |
results with similar manner we used in shear viscosity |
439 |
– |
calculation. All the data reported from scaling method were obtained |
440 |
– |
by simulations of 10-step exchange frequency, and the target exchange |
441 |
– |
kinetic energy were set to produce equivalent kinetic energy flux as |
442 |
– |
in swapping method. Figure \ref{thermalGrad} exhibits similar thermal |
443 |
– |
gradients of respective similar kinetic energy flux. |
444 |
– |
|
445 |
– |
\begin{table*} |
446 |
– |
\begin{minipage}{\linewidth} |
447 |
– |
\begin{center} |
448 |
– |
|
449 |
– |
\caption{Calculation results for thermal conductivity of Lennard-Jones |
450 |
– |
fluid at ${\langle T^* \rangle = 0.72}$ and ${\rho^* = 0.85}$, with |
451 |
– |
swap and scale methods at various kinetic energy exchange rates. Results |
452 |
– |
in reduced unit. Errors of calculations in parentheses.} |
453 |
– |
|
454 |
– |
\begin{tabular}{ccc} |
455 |
– |
\hline |
456 |
– |
Series & $\lambda^*_{swap}$ & $\lambda^*_{scale}$\\ |
457 |
– |
\hline |
458 |
– |
20-250 & 7.03(0.34) & 7.30(0.10)\\ |
459 |
– |
20-500 & 7.03(0.14) & 6.95(0.09)\\ |
460 |
– |
20-1000 & 6.91(0.42) & 7.19(0.07)\\ |
461 |
– |
20-2000 & 7.52(0.15) & 7.19(0.28)\\ |
462 |
– |
\hline |
463 |
– |
\end{tabular} |
464 |
– |
\label{thermal} |
465 |
– |
\end{center} |
466 |
– |
\end{minipage} |
467 |
– |
\end{table*} |
468 |
– |
|
469 |
– |
\begin{figure} |
470 |
– |
\includegraphics[width=\linewidth]{thermalGrad} |
471 |
– |
\caption{Temperature gradients of thermal conductivity simulations} |
472 |
– |
\label{thermalGrad} |
473 |
– |
\end{figure} |
474 |
– |
|
475 |
– |
During these simulations, molecule velocities were recorded in 1000 of |
476 |
– |
all the snapshots. These velocity data were used to produce histograms |
477 |
– |
of velocity and speed distribution in different slabs. From these |
478 |
– |
histograms, it is observed that with increasing unphysical kinetic |
479 |
– |
energy flux, speed and velocity distribution of molecules in slabs |
480 |
– |
where swapping occured could deviate from Maxwell-Boltzmann |
481 |
– |
distribution. Figure \ref{histSwap} indicates how these distributions |
482 |
– |
deviate from ideal condition. In high temperature slabs, probability |
483 |
– |
density in low speed is confidently smaller than ideal distribution; |
484 |
– |
in low temperature slabs, probability density in high speed is smaller |
485 |
– |
than ideal. This phenomenon is observable even in our relatively low |
486 |
– |
swapping rate simulations. And this deviation could also leads to |
487 |
– |
deviation of distribution of velocity in various dimensions. One |
488 |
– |
feature of these deviated distribution is that in high temperature |
489 |
– |
slab, the ideal Gaussian peak was changed into a relatively flat |
490 |
– |
plateau; while in low temperature slab, that peak appears sharper. |
491 |
– |
|
492 |
– |
\begin{figure} |
493 |
– |
\includegraphics[width=\linewidth]{histSwap} |
494 |
– |
\caption{Speed distribution for thermal conductivity using swapping RNEMD.} |
495 |
– |
\label{histSwap} |
496 |
– |
\end{figure} |
497 |
– |
|
498 |
– |
\begin{figure} |
499 |
– |
\includegraphics[width=\linewidth]{histScale} |
500 |
– |
\caption{Speed distribution for thermal conductivity using scaling RNEMD.} |
501 |
– |
\label{histScale} |
502 |
– |
\end{figure} |
503 |
– |
|
504 |
– |
\subsection{Interfaciel Thermal Conductivity} |
505 |
– |
|
506 |
– |
\begin{figure} |
507 |
– |
\includegraphics[width=\linewidth]{spceGrad} |
508 |
– |
\caption{Temperature gradients for SPC/E water thermal conductivity.} |
509 |
– |
\label{spceGrad} |
510 |
– |
\end{figure} |
511 |
– |
|
512 |
– |
\begin{table*} |
513 |
– |
\begin{minipage}{\linewidth} |
514 |
– |
\begin{center} |
515 |
– |
|
516 |
– |
\caption{Calculation results for thermal conductivity of SPC/E water |
517 |
– |
at ${\langle T\rangle}$ = 300K at various thermal exchange rates. Errors of |
518 |
– |
calculations in parentheses. } |
519 |
– |
|
520 |
– |
\begin{tabular}{cccc} |
521 |
– |
\hline |
522 |
– |
$\langle dT/dz\rangle$(K/\AA) & & $\lambda$(W/m/K) & \\ |
523 |
– |
& This work & Previous simulations$^a$ & Experiment$^b$\\ |
524 |
– |
\hline |
525 |
– |
0.38 & 0.816(0.044) & 0.784 & 0.64\\ |
526 |
– |
0.81 & 0.770(0.008) & 0.730\\ |
527 |
– |
1.54 & 0.813(0.007) & \\ |
528 |
– |
\hline |
529 |
– |
\end{tabular} |
530 |
– |
\label{spceThermal} |
531 |
– |
\end{center} |
532 |
– |
\end{minipage} |
533 |
– |
\end{table*} |
534 |
– |
|
535 |
– |
|
536 |
– |
\begin{figure} |
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– |
\includegraphics[width=\linewidth]{AuGrad} |
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– |
\caption{Temperature gradients for crystal gold thermal conductivity.} |
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– |
\label{AuGrad} |
540 |
– |
\end{figure} |
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|
542 |
– |
\begin{table*} |
543 |
– |
\begin{minipage}{\linewidth} |
544 |
– |
\begin{center} |
545 |
– |
|
546 |
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\caption{Calculation results for thermal conductivity of crystal gold |
547 |
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at ${\langle T\rangle}$ = 300K at various thermal exchange rates. Errors of |
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– |
calculations in parentheses. } |
549 |
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|
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\begin{tabular}{ccc} |
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– |
\hline |
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$\langle dT/dz\rangle$(K/\AA) & $\lambda$(W/m/K)\\ |
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& This work & Previous simulations$^a$ \\ |
554 |
– |
\hline |
555 |
– |
1.44 & 1.10(0.01) & \\ |
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2.86 & 1.08(0.02) & \\ |
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– |
5.14 & 1.15(0.01) & \\ |
558 |
– |
\hline |
559 |
– |
\end{tabular} |
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\label{AuThermal} |
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\end{center} |
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– |
\end{minipage} |
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\end{table*} |
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|
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|
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\begin{figure} |
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\includegraphics[width=\linewidth]{interfaceDensity} |
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\caption{Density profile for interfacial thermal conductivity |
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simulation box.} |
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\label{interfaceDensity} |
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– |
\end{figure} |
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|
573 |
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|
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\section{Acknowledgments} |
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Support for this project was provided by the National Science |
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Foundation under grant CHE-0848243. Computational time was provided by |
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the Center for Research Computing (CRC) at the University of Notre |
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Dame. \newpage |
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\bibliographystyle{jcp2} |
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\bibliographystyle{aip} |
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\bibliography{nivsRnemd} |
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\end{doublespace} |
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\end{document} |
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