38 |
|
\begin{doublespace} |
39 |
|
|
40 |
|
\begin{abstract} |
41 |
< |
|
41 |
> |
We present a new method for introducing stable non-equilibrium |
42 |
> |
velocity and temperature distributions in molecular dynamics |
43 |
> |
simulations of heterogeneous systems. This method extends |
44 |
> |
earlier Reverse Non-Equilibrium Molecular Dynamics (RNEMD) methods |
45 |
> |
which use momentum exchange swapping moves that can create |
46 |
> |
non-thermal velocity distributions, and are difficult to use |
47 |
> |
for interfacial calculations. By using non-isotropic velocity |
48 |
> |
scaling (NIVS) on the molecules in specific regions of a system, it |
49 |
> |
is possible to impose momentum or thermal flux between regions of a |
50 |
> |
simulation and stable thermal and momentum gradients can then be |
51 |
> |
established. The scaling method we have developed conserves the |
52 |
> |
total linear momentum and total energy of the system. To test the |
53 |
> |
methods, we have computed the thermal conductivity of model liquid |
54 |
> |
and solid systems as well as the interfacial thermal conductivity of |
55 |
> |
a metal-water interface. We find that the NIVS-RNEMD improves the |
56 |
> |
problematic velocity distributions that develop in other RNEMD |
57 |
> |
methods. |
58 |
|
\end{abstract} |
59 |
|
|
60 |
|
\newpage |
65 |
|
% BODY OF TEXT |
66 |
|
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
67 |
|
|
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,Muller-Plathe:1999ek} 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, |
73 |
> |
the simulation cell.\cite{MullerPlathe:1997xw,ISI:000080382700030} The |
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} |
84 |
> |
j_z(p_x) & = & -\eta \frac{\partial v_x}{\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 have discovered some problems with the |
123 |
< |
original RNEMD swap technique. Notably, large momentum fluxes |
124 |
< |
(equivalent to frequent momentum swaps between the slabs) can result |
125 |
< |
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. |
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 |
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}{c} |
155 |
< |
x \\ |
156 |
< |
y \\ |
157 |
< |
z \\ |
154 |
> |
\vec{v}_i \leftarrow \left( \begin{array}{ccc} |
155 |
> |
x & 0 & 0 \\ |
156 |
> |
0 & y & 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}{c} |
166 |
< |
x^\prime \\ |
167 |
< |
y^\prime \\ |
168 |
< |
z^\prime \\ |
165 |
> |
\vec{v}_j \leftarrow \left( \begin{array}{ccc} |
166 |
> |
x^\prime & 0 & 0 \\ |
167 |
> |
0 & y^\prime & 0 \\ |
168 |
> |
0 & 0 & z^\prime \\ |
169 |
|
\end{array} \right) \cdot \vec{v}_j |
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 |
177 |
|
\end{equation} |
178 |
|
where |
179 |
< |
\begin{equation} |
151 |
< |
\begin{array}{rcl} |
179 |
> |
\begin{eqnarray} |
180 |
|
P_c^\alpha & = & \sum_{i = 1}^{N_c} m_i \left[\vec{v}_i\right]_\alpha \\ |
181 |
< |
P_h^\alpha & = & \sum_{j = 1}^{N_h} m_j \left[\vec{v}_j\right]_\alpha \\ |
154 |
< |
\end{array} |
181 |
> |
P_h^\alpha & = & \sum_{j = 1}^{N_h} m_j \left[\vec{v}_j\right]_\alpha |
182 |
|
\label{eq:momentumdef} |
183 |
< |
\end{equation} |
183 |
> |
\end{eqnarray} |
184 |
|
Therefore, for each of the three directions, the hot scaling |
185 |
|
parameters are a simple function of the cold scaling parameters and |
186 |
|
the instantaneous linear momentum in each of the two slabs. |
197 |
|
Conservation of total energy also places constraints on the scaling: |
198 |
|
\begin{equation} |
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. |
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 |
214 |
|
linear momenta and kinetic energies for the hot and cold slabs, |
215 |
< |
\begin{equation} |
187 |
< |
\begin{array}{rcl} |
215 |
> |
\begin{eqnarray} |
216 |
|
a_\alpha & = &\left(K_c^\alpha + K_h^\alpha |
217 |
|
\left(p_\alpha\right)^2\right) \\ |
218 |
|
b_\alpha & = & -2 K_h^\alpha p_\alpha \left( 1 + p_\alpha\right) \\ |
219 |
< |
c_\alpha & = & K_h^\alpha p_\alpha^2 + 2 K_h^\alpha p_\alpha - K_c^\alpha \\ |
192 |
< |
\end{array} |
219 |
> |
c_\alpha & = & K_h^\alpha p_\alpha^2 + 2 K_h^\alpha p_\alpha - K_c^\alpha |
220 |
|
\label{eq:constraintEllipsoidConsts} |
221 |
< |
\end{equation} |
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 translational kinetic energy. |
221 |
> |
\end{eqnarray} |
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 |
> |
linear momentum or kinetic energy from the cold slab to the hot slab. |
229 |
> |
If the hot and cold slabs are separated along the z-axis, the energy |
230 |
> |
flux is given simply by the decrease in kinetic energy of the cold |
231 |
> |
bin: |
232 |
|
\begin{equation} |
233 |
< |
(1-x^2) K_c^x + (1-y^2) K_c^y + (1-z^2) K_c^z = J_z |
233 |
> |
(1-x^2) K_c^x + (1-y^2) K_c^y + (1-z^2) K_c^z = J_z\Delta t |
234 |
|
\end{equation} |
235 |
|
The expression for the energy flux can be re-written as another |
236 |
|
ellipsoid centered on $(x,y,z) = 0$: |
237 |
|
\begin{equation} |
238 |
< |
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) |
238 |
> |
\sum_{\alpha = x,y,z} K_c^\alpha \alpha^2 = \sum_{\alpha = x,y,z} |
239 |
> |
K_c^\alpha -J_z \Delta t |
240 |
|
\label{eq:fluxEllipsoid} |
241 |
|
\end{equation} |
242 |
< |
The spatial extent of the {\it flux ellipsoid} is governed both by a |
243 |
< |
targetted value, $J_z$ as well as the instantaneous values of the |
244 |
< |
kinetic energy components in the hot bin. |
242 |
> |
The spatial extent of the {\it thermal flux ellipsoid} is governed |
243 |
> |
both by the target flux, $J_z$ as well as the instantaneous values of |
244 |
> |
the kinetic energy components in the cold bin. |
245 |
|
|
246 |
|
To satisfy an energetic flux as well as the conservation constraints, |
247 |
< |
it is sufficient to determine the points ${x,y,z}$ which lie on both |
247 |
> |
we must determine the points ${x,y,z}$ that lie on both the constraint |
248 |
> |
ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the flux ellipsoid |
249 |
> |
(Eq. \ref{eq:fluxEllipsoid}), i.e. the intersection of the two |
250 |
> |
ellipsoids in 3-dimensional space. |
251 |
> |
|
252 |
> |
\begin{figure} |
253 |
> |
\includegraphics[width=\linewidth]{ellipsoids} |
254 |
> |
\caption{Velocity scaling coefficients which maintain both constant |
255 |
> |
energy and constant linear momentum of the system lie on the surface |
256 |
> |
of the {\it constraint ellipsoid} while points which generate the |
257 |
> |
target momentum flux lie on the surface of the {\it flux ellipsoid}. |
258 |
> |
The velocity distributions in the cold bin are scaled by only those |
259 |
> |
points which lie on both ellipsoids.} |
260 |
> |
\label{ellipsoids} |
261 |
> |
\end{figure} |
262 |
> |
|
263 |
> |
Since ellipsoids can be expressed as polynomials up to second order in |
264 |
> |
each of the three coordinates, finding the the intersection points of |
265 |
> |
two ellipsoids is isomorphic to finding the roots a polynomial of |
266 |
> |
degree 16. There are a number of polynomial root-finding methods in |
267 |
> |
the literature, [CITATIONS NEEDED] but numerically finding the roots |
268 |
> |
of high-degree polynomials is generally an ill-conditioned |
269 |
> |
problem.[CITATION NEEDED] One way around this is to try to maintain |
270 |
> |
velocity scalings that are {\it as isotropic as possible}. To do |
271 |
> |
this, we impose $x=y$, and to treat both the constraint and flux |
272 |
> |
ellipsoids as 2-dimensional ellipses. In reduced dimensionality, the |
273 |
> |
intersecting-ellipse problem reduces to finding the roots of |
274 |
> |
polynomials of degree 4. |
275 |
> |
|
276 |
> |
Depending on the target flux and current velocity distributions, the |
277 |
> |
ellipsoids can have between 0 and 4 intersection points. If there are |
278 |
> |
no intersection points, it is not possible to satisfy the constraints |
279 |
> |
while performing a non-equilibrium scaling move, and no change is made |
280 |
> |
to the dynamics. |
281 |
> |
|
282 |
> |
With multiple intersection points, any of the scaling points will |
283 |
> |
conserve the linear momentum and kinetic energy of the system and will |
284 |
> |
generate the correct target flux. Although this method is inherently |
285 |
> |
non-isotropic, the goal is still to maintain the system as close to an |
286 |
> |
isotropic fluid as possible. With this in mind, we would like the |
287 |
> |
kinetic energies in the three different directions could become as |
288 |
> |
close as each other as possible after each scaling. Simultaneously, |
289 |
> |
one would also like each scaling as gentle as possible, i.e. ${x,y,z |
290 |
> |
\rightarrow 1}$, in order to avoid large perturbation to the system. |
291 |
> |
To do this, we pick the intersection point which maintains the scaling |
292 |
> |
variables ${x=y, z}$ as well as the ratio of kinetic energies |
293 |
> |
${K_c^z/K_c^x, K_c^z/K_c^y}$ as close as possible to 1. |
294 |
> |
|
295 |
> |
After the valid scaling parameters are arrived at by solving geometric |
296 |
> |
intersection problems in $x, y, z$ space in order to obtain cold slab |
297 |
> |
scaling parameters, Eq. (\ref{eq:hotcoldscaling}) is used to |
298 |
> |
determine the conjugate hot slab scaling variables. |
299 |
> |
|
300 |
> |
\subsection{Introducing shear stress via velocity scaling} |
301 |
> |
Rather than using this method to induce a thermal flux, it is possible |
302 |
> |
to use the random fluctuations of the average momentum in each of the |
303 |
> |
bins to induce a momentum flux. Doing this repeatedly will create a |
304 |
> |
shear stress on the system which will respond with an easily-measured |
305 |
> |
strain. The momentum flux (say along the $x$-direction) may be |
306 |
> |
defined as: |
307 |
> |
\begin{equation} |
308 |
> |
(1-x) P_c^x = j_z(p_x)\Delta t |
309 |
> |
\label{eq:fluxPlane} |
310 |
> |
\end{equation} |
311 |
> |
This {\it momentum flux plane} is perpendicular to the $x$-axis, with |
312 |
> |
its position governed both by a target value, $j_z(p_x)$ as well as |
313 |
> |
the instantaneous value of the momentum along the $x$-direction. |
314 |
> |
|
315 |
> |
In order to satisfy a momentum flux as well as the conservation |
316 |
> |
constraints, we must determine the points ${x,y,z}$ which lie on both |
317 |
|
the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the |
318 |
< |
flux ellipsoid (Eq. \ref{eq:fluxEllipsoid}), i.e. the intersection of |
319 |
< |
the two ellipsoids in 3-dimensional space. |
318 |
> |
flux plane (Eq. \ref{eq:fluxPlane}), i.e. the intersection of an |
319 |
> |
ellipsoid and a plane in 3-dimensional space. |
320 |
|
|
321 |
< |
One may also define momentum flux (say along the x-direction) as: |
321 |
> |
In the case of momentum flux transfer, we also impose another |
322 |
> |
constraint to set the kinetic energy transfer as zero. In another |
323 |
> |
word, we apply Eq. \ref{eq:fluxEllipsoid} and let ${J_z = 0}$. With |
324 |
> |
one variable fixed by Eq. \ref{eq:fluxPlane}, one now have a similar |
325 |
> |
set of quartic equations to the above kinetic energy transfer problem. |
326 |
> |
|
327 |
> |
\section{Computational Details} |
328 |
> |
|
329 |
> |
We have implemented this methodology in our molecular dynamics |
330 |
> |
code,\cite{Meineke:2005gd} by performing the NIVS scaling moves after |
331 |
> |
each MD step. We have tested it for a variety of different |
332 |
> |
situations, including homogeneous fluids (Lennard-Jones and SPC/E |
333 |
> |
water), crystalline solids (EAM and Sutton-Chen models for Gold), and |
334 |
> |
heterogeneous interfaces (QSC gold - SPC/E water). The last of these |
335 |
> |
systems would have been very difficult to study using previous RNEMD |
336 |
> |
methods, but using velocity scaling moves, we can even obtain |
337 |
> |
estimates of the interfacial thermal conductivity. |
338 |
> |
|
339 |
> |
\subsection{Lennard-Jones Fluid} |
340 |
> |
|
341 |
> |
2592 Lennard-Jones atoms were placed in an orthorhombic cell |
342 |
> |
${10.06\sigma \times 10.06\sigma \times 30.18\sigma}$ on a side. The |
343 |
> |
reduced density ${\rho^* = \rho\sigma^3}$ was thus 0.85, which enabled |
344 |
> |
direct comparison between our results and previous methods. These |
345 |
> |
simulations were carried out with a reduced timestep ${\tau^* = |
346 |
> |
4.6\times10^{-4}}$. For the shear viscosity calculation, the mean |
347 |
> |
temperature was ${T^* = k_B T/\varepsilon = 0.72}$. Simulations were |
348 |
> |
first thermalized in canonical ensemble (NVT), then equilibrated in |
349 |
> |
microcanonical ensemble (NVE) before introducing any non-equilibrium |
350 |
> |
method. |
351 |
> |
|
352 |
> |
We have compared the momentum gradients established using our method |
353 |
> |
to those obtained using the original M\"{u}ller-Plathe swapping |
354 |
> |
algorithm.\cite{ISI:000080382700030} In both cases, the simulation box |
355 |
> |
was divided into ${N = 20}$ slabs. In the swapping algorithm, the top |
356 |
> |
slab $(n = 1)$ exchanges the most negative $x$ momentum with the most |
357 |
> |
positive $x$ momentum in the center slab $(n = N/2 + 1)$. The rate at |
358 |
> |
which the swapping moves are carried out defines the momentum or |
359 |
> |
thermal flux between the two slabs. In their work, Tenney {\it et |
360 |
> |
al.}\cite{Maginn:2010} found problematic behavior with large swap |
361 |
> |
frequencies. |
362 |
> |
|
363 |
> |
According to each result from swapping RNEMD, scaling RNEMD |
364 |
> |
simulations were run with the target momentum flux set to produce a |
365 |
> |
similar momentum flux, and consequently shear rate. Furthermore, |
366 |
> |
various scaling frequencies can be tested for one single swapping |
367 |
> |
rate. To test the temperature homogeneity in our system of swapping |
368 |
> |
and scaling methods, temperatures of different dimensions in all the |
369 |
> |
slabs were observed. Most of the simulations include $10^5$ steps of |
370 |
> |
equilibration without imposing momentum flux, $10^5$ steps of |
371 |
> |
stablization with imposing unphysical momentum transfer, and $10^6$ |
372 |
> |
steps of data collection under RNEMD. For relatively high momentum |
373 |
> |
flux simulations, ${5\times10^5}$ step data collection is sufficient. |
374 |
> |
For some low momentum flux simulations, ${2\times10^6}$ steps were |
375 |
> |
necessary. |
376 |
> |
|
377 |
> |
After each simulation, the shear viscosity was calculated in reduced |
378 |
> |
unit. The momentum flux was calculated with total unphysical |
379 |
> |
transferred momentum ${P_x}$ and data collection time $t$: |
380 |
|
\begin{equation} |
381 |
< |
(1-x) P_c^x = j_z(p_x) |
381 |
> |
j_z(p_x) = \frac{P_x}{2 t L_x L_y} |
382 |
> |
\end{equation} |
383 |
> |
where $L_x$ and $L_y$ denotes $x$ and $y$ lengths of the simulation |
384 |
> |
box, and physical momentum transfer occurs in two ways due to our |
385 |
> |
periodic boundary condition settings. And the velocity gradient |
386 |
> |
${\langle \partial v_x /\partial z \rangle}$ can be obtained by a |
387 |
> |
linear regression of the velocity profile. From the shear viscosity |
388 |
> |
$\eta$ calculated with the above parameters, one can further convert |
389 |
> |
it into reduced unit ${\eta^* = \eta \sigma^2 (\varepsilon m)^{-1/2}}$. |
390 |
> |
|
391 |
> |
For thermal conductivity calculations, simulations were first run under |
392 |
> |
reduced temperature ${\langle T^*\rangle = 0.72}$ in NVE |
393 |
> |
ensemble. Muller-Plathe's algorithm was adopted in the swapping |
394 |
> |
method. Under identical simulation box parameters with our shear |
395 |
> |
viscosity calculations, in each swap, the top slab exchanges all three |
396 |
> |
translational momentum components of the molecule with least kinetic |
397 |
> |
energy with the same components of the molecule in the center slab |
398 |
> |
with most kinetic energy, unless this ``coldest'' molecule in the |
399 |
> |
``hot'' slab is still ``hotter'' than the ``hottest'' molecule in the |
400 |
> |
``cold'' slab. According to swapping RNEMD results, target energy flux |
401 |
> |
for scaling RNEMD simulations can be set. Also, various scaling |
402 |
> |
frequencies can be tested for one target energy flux. To compare the |
403 |
> |
performance between swapping and scaling method, distributions of |
404 |
> |
velocity and speed in different slabs were observed. |
405 |
> |
|
406 |
> |
For each swapping rate, thermal conductivity was calculated in reduced |
407 |
> |
unit. The energy flux was calculated similarly to the momentum flux, |
408 |
> |
with total unphysical transferred energy ${E_{total}}$ and data collection |
409 |
> |
time $t$: |
410 |
> |
\begin{equation} |
411 |
> |
J_z = \frac{E_{total}}{2 t L_x L_y} |
412 |
> |
\end{equation} |
413 |
> |
And the temperature gradient ${\langle\partial T/\partial z\rangle}$ |
414 |
> |
can be obtained by a linear regression of the temperature |
415 |
> |
profile. From the thermal conductivity $\lambda$ calculated, one can |
416 |
> |
further convert it into reduced unit ${\lambda^*=\lambda \sigma^2 |
417 |
> |
m^{1/2} k_B^{-1}\varepsilon^{-1/2}}$. |
418 |
> |
|
419 |
> |
\subsection{ Water / Metal Thermal Conductivity} |
420 |
> |
Another series of our simulation is the calculation of interfacial |
421 |
> |
thermal conductivity of a Au/H$_2$O system. Respective calculations of |
422 |
> |
liquid water (Extended Simple Point Charge model) and crystal gold |
423 |
> |
thermal conductivity were performed and compared with current results |
424 |
> |
to ensure the validity of NIVS-RNEMD. After that, a mixture system was |
425 |
> |
simulated. |
426 |
> |
|
427 |
> |
For thermal conductivity calculation of bulk water, a simulation box |
428 |
> |
consisting of 1000 molecules were first equilibrated under ambient |
429 |
> |
pressure and temperature conditions using NPT ensemble, followed by |
430 |
> |
equilibration in fixed volume (NVT). The system was then equilibrated in |
431 |
> |
microcanonical ensemble (NVE). Also in NVE ensemble, establishing a |
432 |
> |
stable thermal gradient was followed. The simulation box was under |
433 |
> |
periodic boundary condition and devided into 10 slabs. Data collection |
434 |
> |
process was similar to Lennard-Jones fluid system. |
435 |
> |
|
436 |
> |
Thermal conductivity calculation of bulk crystal gold used a similar |
437 |
> |
protocol. Two types of force field parameters, Embedded Atom Method |
438 |
> |
(EAM) and Quantum Sutten-Chen (QSC) force field were used |
439 |
> |
respectively. The face-centered cubic crystal simulation box consists of |
440 |
> |
2880 Au atoms. The lattice was first allowed volume change to relax |
441 |
> |
under ambient temperature and pressure. Equilibrations in canonical and |
442 |
> |
microcanonical ensemble were followed in order. With the simulation |
443 |
> |
lattice devided evenly into 10 slabs, different thermal gradients were |
444 |
> |
established by applying a set of target thermal transfer flux. Data of |
445 |
> |
the series of thermal gradients was collected for calculation. |
446 |
> |
|
447 |
> |
After simulations of bulk water and crystal gold, a mixture system was |
448 |
> |
constructed, consisting of 1188 Au atoms and 1862 H$_2$O |
449 |
> |
molecules. Spohr potential was adopted in depicting the interaction |
450 |
> |
between metal atom and water molecule.\cite{ISI:000167766600035} A |
451 |
> |
similar protocol of equilibration was followed. Several thermal |
452 |
> |
gradients was built under different target thermal flux. It was found |
453 |
> |
out that compared to our previous simulation systems, the two phases |
454 |
> |
could have large temperature difference even under a relatively low |
455 |
> |
thermal flux. Therefore, under our low flux conditions, it is assumed |
456 |
> |
that the metal and water phases have respectively homogeneous |
457 |
> |
temperature, excluding the surface regions. In calculating the |
458 |
> |
interfacial thermal conductivity $G$, this assumptioin was applied and |
459 |
> |
thus our formula becomes: |
460 |
> |
|
461 |
> |
\begin{equation} |
462 |
> |
G = \frac{E_{total}}{2 t L_x L_y \left( \langle T_{gold}\rangle - |
463 |
> |
\langle T_{water}\rangle \right)} |
464 |
> |
\label{interfaceCalc} |
465 |
|
\end{equation} |
466 |
+ |
where ${E_{total}}$ is the imposed unphysical kinetic energy transfer |
467 |
+ |
and ${\langle T_{gold}\rangle}$ and ${\langle T_{water}\rangle}$ are the |
468 |
+ |
average observed temperature of gold and water phases respectively. |
469 |
|
|
470 |
+ |
\section{Results And Discussions} |
471 |
+ |
\subsection{Thermal Conductivity} |
472 |
+ |
\subsubsection{Lennard-Jones Fluid} |
473 |
+ |
Our thermal conductivity calculations show that scaling method results |
474 |
+ |
agree with swapping method. Four different exchange intervals were |
475 |
+ |
tested (Table \ref{thermalLJRes}) using swapping method. With a fixed |
476 |
+ |
10fs exchange interval, target exchange kinetic energy was set to |
477 |
+ |
produce equivalent kinetic energy flux as in swapping method. And |
478 |
+ |
similar thermal gradients were observed with similar thermal flux in |
479 |
+ |
two simulation methods (Figure \ref{thermalGrad}). |
480 |
|
|
481 |
+ |
\begin{table*} |
482 |
+ |
\begin{minipage}{\linewidth} |
483 |
+ |
\begin{center} |
484 |
|
|
485 |
+ |
\caption{Calculation results for thermal conductivity of Lennard-Jones |
486 |
+ |
fluid at ${\langle T^* \rangle = 0.72}$ and ${\rho^* = 0.85}$, with |
487 |
+ |
swap and scale methods at various kinetic energy exchange rates. Results |
488 |
+ |
in reduced unit. Errors of calculations in parentheses.} |
489 |
|
|
490 |
+ |
\begin{tabular}{ccccc} |
491 |
+ |
Swapping method & & & NIVS-RNEMD\\ |
492 |
+ |
\hline |
493 |
+ |
Swap Interval (fs) & $\lambda^*_{swap}$ & & Equilvalent $J_z^*$ & |
494 |
+ |
$\lambda^*_{scale}$\\ |
495 |
+ |
\hline |
496 |
+ |
250 & 7.03(0.34) & & 0.16 & 7.30(0.10)\\ |
497 |
+ |
500 & 7.03(0.14) & & 0.09 & 6.95(0.09)\\ |
498 |
+ |
1000 & 6.91(0.42) & & 0.047 & 7.19(0.07)\\ |
499 |
+ |
2000 & 7.52(0.15) & & 0.024 & 7.19(0.28)\\ |
500 |
+ |
\hline |
501 |
+ |
\end{tabular} |
502 |
+ |
\label{thermalLJRes} |
503 |
+ |
\end{center} |
504 |
+ |
\end{minipage} |
505 |
+ |
\end{table*} |
506 |
|
|
507 |
+ |
\begin{figure} |
508 |
+ |
\includegraphics[width=\linewidth]{thermalGrad} |
509 |
+ |
\caption{NIVS-RNEMD method introduced similar temperature gradients |
510 |
+ |
compared to ``swapping'' method under various kinetic energy flux in |
511 |
+ |
thermal conductivity simulations.} |
512 |
+ |
\label{thermalGrad} |
513 |
+ |
\end{figure} |
514 |
+ |
|
515 |
+ |
During these simulations, molecule velocities were recorded in 1000 of |
516 |
+ |
all the snapshots of one single data collection process. These |
517 |
+ |
velocity data were used to produce histograms of velocity and speed |
518 |
+ |
distribution in different slabs. From these histograms, it is observed |
519 |
+ |
that under relatively high unphysical kinetic energy flux, speed and |
520 |
+ |
velocity distribution of molecules in slabs where swapping occured |
521 |
+ |
could deviate from Maxwell-Boltzmann distribution. Figure |
522 |
+ |
\ref{thermalHist} a) illustrates how these distributions deviate from an |
523 |
+ |
ideal distribution. In high temperature slab, probability density in |
524 |
+ |
low speed is confidently smaller than ideal curve fit; in low |
525 |
+ |
temperature slab, probability density in high speed is smaller than |
526 |
+ |
ideal, while larger than ideal in low speed. This phenomenon is more |
527 |
+ |
obvious in our high swapping rate simulations. And this deviation |
528 |
+ |
could also leads to deviation of distribution of velocity in various |
529 |
+ |
dimensions. One feature of these deviated distribution is that in high |
530 |
+ |
temperature slab, the ideal Gaussian peak was changed into a |
531 |
+ |
relatively flat plateau; while in low temperature slab, that peak |
532 |
+ |
appears sharper. This problem is rooted in the mechanism of the |
533 |
+ |
swapping method. Continually depleting low (high) speed particles in |
534 |
+ |
the high (low) temperature slab could not be complemented by |
535 |
+ |
diffusions of low (high) speed particles from neighbor slabs, unless |
536 |
+ |
in suffciently low swapping rate. Simutaneously, surplus low speed |
537 |
+ |
particles in the low temperature slab do not have sufficient time to |
538 |
+ |
diffuse to neighbor slabs. However, thermal exchange rate should reach |
539 |
+ |
a minimum level to produce an observable thermal gradient under noise |
540 |
+ |
interference. Consequently, swapping RNEMD has a relatively narrow |
541 |
+ |
choice of swapping rate to satisfy these above restrictions. |
542 |
+ |
|
543 |
+ |
Comparatively, NIVS-RNEMD has a speed distribution closer to the ideal |
544 |
+ |
curve fit (Figure \ref{thermalHist} b). Essentially, after scaling, a |
545 |
+ |
Gaussian distribution function would remain Gaussian. Although a |
546 |
+ |
single scaling is non-isotropic in all three dimensions, our scaling |
547 |
+ |
coefficient criteria could help maintian the scaling region as |
548 |
+ |
isotropic as possible. On the other hand, scaling coefficients are |
549 |
+ |
preferred to be as close to 1 as possible, which also helps minimize |
550 |
+ |
the difference among different dimensions. This is possible if scaling |
551 |
+ |
interval and one-time thermal transfer energy are well |
552 |
+ |
chosen. Consequently, NIVS-RNEMD is able to impose an unphysical |
553 |
+ |
thermal flux as the previous RNEMD method without large perturbation |
554 |
+ |
to the distribution of velocity and speed in the exchange regions. |
555 |
+ |
|
556 |
+ |
\begin{figure} |
557 |
+ |
\includegraphics[width=\linewidth]{thermalHist} |
558 |
+ |
\caption{Speed distribution for thermal conductivity using a) |
559 |
+ |
``swapping'' and b) NIVS- RNEMD methods. Shown is from the |
560 |
+ |
simulations with an exchange or equilvalent exchange interval of 250 |
561 |
+ |
fs. In circled areas, distributions from ``swapping'' RNEMD |
562 |
+ |
simulation have deviation from ideal Maxwell-Boltzmann distribution |
563 |
+ |
(curves fit for each distribution).} |
564 |
+ |
\label{thermalHist} |
565 |
+ |
\end{figure} |
566 |
+ |
|
567 |
+ |
\subsubsection{SPC/E Water} |
568 |
+ |
Our results of SPC/E water thermal conductivity are comparable to |
569 |
+ |
Bedrov {\it et al.}\cite{Bedrov:2000}, which employed the |
570 |
+ |
previous swapping RNEMD method for their calculation. Bedrov {\it et |
571 |
+ |
al.}\cite{Bedrov:2000} argued that exchange of the molecule |
572 |
+ |
center-of-mass velocities instead of single atom velocities in a |
573 |
+ |
molecule conserves the total kinetic energy and linear momentum. This |
574 |
+ |
principle is adopted in our simulations. Scaling is applied to the |
575 |
+ |
velocities of the rigid bodies of SPC/E model water molecules, instead |
576 |
+ |
of each hydrogen and oxygen atoms in relevant water molecules. As |
577 |
+ |
shown in Figure \ref{spceGrad}, temperature gradients were established |
578 |
+ |
similar to their system. However, the average temperature of our |
579 |
+ |
system is 300K, while theirs is 318K, which would be attributed for |
580 |
+ |
part of the difference between the final calculation results (Table |
581 |
+ |
\ref{spceThermal}). Both methods yields values in agreement with |
582 |
+ |
experiment. And this shows the applicability of our method to |
583 |
+ |
multi-atom molecular system. |
584 |
+ |
|
585 |
+ |
\begin{figure} |
586 |
+ |
\includegraphics[width=\linewidth]{spceGrad} |
587 |
+ |
\caption{Temperature gradients in SPC/E water thermal conductivity |
588 |
+ |
simulations.} |
589 |
+ |
\label{spceGrad} |
590 |
+ |
\end{figure} |
591 |
+ |
|
592 |
+ |
\begin{table*} |
593 |
+ |
\begin{minipage}{\linewidth} |
594 |
+ |
\begin{center} |
595 |
+ |
|
596 |
+ |
\caption{Calculation results for thermal conductivity of SPC/E water |
597 |
+ |
at ${\langle T\rangle}$ = 300K at various thermal exchange rates. Errors of |
598 |
+ |
calculations in parentheses. } |
599 |
+ |
|
600 |
+ |
\begin{tabular}{cccc} |
601 |
+ |
\hline |
602 |
+ |
$\langle dT/dz\rangle$(K/\AA) & & $\lambda$(W/m/K) & \\ |
603 |
+ |
& This work & Previous simulations\cite{Bedrov:2000} & |
604 |
+ |
Experiment[CiteWagnerKruseBook]\\ |
605 |
+ |
\hline |
606 |
+ |
0.38 & 0.816(0.044) & & 0.64\\ |
607 |
+ |
0.81 & 0.770(0.008) & 0.784\\ |
608 |
+ |
1.54 & 0.813(0.007) & 0.730\\ |
609 |
+ |
\hline |
610 |
+ |
\end{tabular} |
611 |
+ |
\label{spceThermal} |
612 |
+ |
\end{center} |
613 |
+ |
\end{minipage} |
614 |
+ |
\end{table*} |
615 |
+ |
|
616 |
+ |
\subsubsection{Crystal Gold} |
617 |
+ |
Our results of gold thermal conductivity using two force fields are |
618 |
+ |
shown in Table \ref{AuThermal}. In these calculations,the end and |
619 |
+ |
middle slabs were excluded in thermal gradient regession and only used |
620 |
+ |
as heat source and drain in the systems. Our yielded values using EAM |
621 |
+ |
force field are slightly larger than those using QSC force |
622 |
+ |
field. However, both series are significantly smaller than |
623 |
+ |
experimental value by a factor of more than 200. It has been verified |
624 |
+ |
that this difference is mainly attributed to the lack of electron |
625 |
+ |
interaction representation in these force field parameters. Richardson |
626 |
+ |
{\it et al.}\cite{Clancy:1992} used EAM force field parameters in |
627 |
+ |
their metal thermal conductivity calculations. The Non-Equilibrium MD |
628 |
+ |
method they employed in their simulations produced comparable results |
629 |
+ |
to ours. As Zhang {\it et al.}\cite{ISI:000231042800044} stated, |
630 |
+ |
thermal conductivity values are influenced mainly by force |
631 |
+ |
field. Another factor that affects the calculation results could be |
632 |
+ |
the density of the metal. Simulations were run with and without |
633 |
+ |
isobaric-isothermal (NPT) equilibration. When equilibrated under NPT |
634 |
+ |
conditions, our crystall simulation cell expanded by the order of 1\% |
635 |
+ |
(Table \ref{AuThermal}). Under larger lattice constant than default, |
636 |
+ |
lower thermal conductance were expected and observed. Furthermore, |
637 |
+ |
from our simulations, a decrease of thermal conductance at higher |
638 |
+ |
temperature is observed, and this trend agrees with experimental |
639 |
+ |
measurements[CiteAshcroftMerminBook]. Therefore, it is confident to apply NIVS-RNEMD |
640 |
+ |
to metal force field systems. |
641 |
+ |
|
642 |
+ |
\begin{table*} |
643 |
+ |
\begin{minipage}{\linewidth} |
644 |
+ |
\begin{center} |
645 |
+ |
|
646 |
+ |
\caption{Calculation results for thermal conductivity of crystal gold |
647 |
+ |
using different force fields at different temperatures and various |
648 |
+ |
thermal exchange rates. Errors of calculations in parentheses.} |
649 |
+ |
|
650 |
+ |
\begin{tabular}{ccccc} |
651 |
+ |
\hline |
652 |
+ |
Force Field Used & $\rho$ (g/cm$^3$) & ${\langle T\rangle}$ (K) & |
653 |
+ |
$\langle dT/dz\rangle$ (K/\AA) & $\lambda$ (W/m/K)\\ |
654 |
+ |
\hline |
655 |
+ |
QSC & 19.188 & 300 & 1.44 & 1.10(0.06)\\ |
656 |
+ |
& & & 2.86 & 1.08(0.05)\\ |
657 |
+ |
& & & 5.14 & 1.15(0.07)\\ |
658 |
+ |
\\ |
659 |
+ |
& 19.263 & 300 & 2.31 & 1.25(0.06)\\ |
660 |
+ |
& & & 3.02 & 1.26(0.05)\\ |
661 |
+ |
& & 575 & 3.02 & 1.02(0.07)\\ |
662 |
+ |
& & & 4.84 & 0.92(0.05)\\ |
663 |
+ |
\\ |
664 |
+ |
\hline |
665 |
+ |
EAM & 19.045 & 300 & 1.24 & 1.24(0.16)\\ |
666 |
+ |
& & & 2.06 & 1.37(0.04)\\ |
667 |
+ |
& & & 2.55 & 1.41(0.07)\\ |
668 |
+ |
\\ |
669 |
+ |
& 19.263 & 300 & 1.06 & 1.45(0.13)\\ |
670 |
+ |
& & & 2.04 & 1.41(0.07)\\ |
671 |
+ |
& & & 2.41 & 1.53(0.10)\\ |
672 |
+ |
& & 575 & 2.82 & 1.08(0.03)\\ |
673 |
+ |
& & & 4.14 & 1.08(0.05)\\ |
674 |
+ |
\hline |
675 |
+ |
\end{tabular} |
676 |
+ |
\label{AuThermal} |
677 |
+ |
\end{center} |
678 |
+ |
\end{minipage} |
679 |
+ |
\end{table*} |
680 |
+ |
|
681 |
+ |
|
682 |
+ |
\subsection{Interfaciel Thermal Conductivity} |
683 |
+ |
After simulations of homogeneous water and gold systems using |
684 |
+ |
NIVS-RNEMD method were proved valid, calculation of gold/water |
685 |
+ |
interfacial thermal conductivity was followed. It is found out that |
686 |
+ |
the low interfacial conductance is probably due to the hydrophobic |
687 |
+ |
surface in our system. Figure \ref{interface} (a) demonstrates mass |
688 |
+ |
density change along $z$-axis, which is perpendicular to the |
689 |
+ |
gold/water interface. It is observed that water density significantly |
690 |
+ |
decreases when approaching the surface. Under this low thermal |
691 |
+ |
conductance, both gold and water phase have sufficient time to |
692 |
+ |
eliminate temperature difference inside respectively (Figure |
693 |
+ |
\ref{interface} b). With indistinguishable temperature difference |
694 |
+ |
within respective phase, it is valid to assume that the temperature |
695 |
+ |
difference between gold and water on surface would be approximately |
696 |
+ |
the same as the difference between the gold and water phase. This |
697 |
+ |
assumption enables convenient calculation of $G$ using |
698 |
+ |
Eq. \ref{interfaceCalc} instead of measuring temperatures of thin |
699 |
+ |
layer of water and gold close enough to surface, which would have |
700 |
+ |
greater fluctuation and lower accuracy. Reported results (Table |
701 |
+ |
\ref{interfaceRes}) are of two orders of magnitude smaller than our |
702 |
+ |
calculations on homogeneous systems, and thus have larger relative |
703 |
+ |
errors than our calculation results on homogeneous systems. |
704 |
+ |
|
705 |
+ |
\begin{figure} |
706 |
+ |
\includegraphics[width=\linewidth]{interface} |
707 |
+ |
\caption{Simulation results for Gold/Water interfacial thermal |
708 |
+ |
conductivity: (a) Significant water density decrease is observed on |
709 |
+ |
crystalline gold surface, which indicates low surface contact and |
710 |
+ |
leads to low thermal conductance. (b) Temperature profiles for a |
711 |
+ |
series of simulations. Temperatures of different slabs in the same |
712 |
+ |
phase show no significant differences.} |
713 |
+ |
\label{interface} |
714 |
+ |
\end{figure} |
715 |
+ |
|
716 |
+ |
\begin{table*} |
717 |
+ |
\begin{minipage}{\linewidth} |
718 |
+ |
\begin{center} |
719 |
+ |
|
720 |
+ |
\caption{Calculation results for interfacial thermal conductivity |
721 |
+ |
at ${\langle T\rangle \sim}$ 300K at various thermal exchange |
722 |
+ |
rates. Errors of calculations in parentheses. } |
723 |
+ |
|
724 |
+ |
\begin{tabular}{cccc} |
725 |
+ |
\hline |
726 |
+ |
$J_z$ (MW/m$^2$) & $T_{gold}$ (K) & $T_{water}$ (K) & $G$ |
727 |
+ |
(MW/m$^2$/K)\\ |
728 |
+ |
\hline |
729 |
+ |
98.0 & 355.2 & 295.8 & 1.65(0.21) \\ |
730 |
+ |
78.8 & 343.8 & 298.0 & 1.72(0.32) \\ |
731 |
+ |
73.6 & 344.3 & 298.0 & 1.59(0.24) \\ |
732 |
+ |
49.2 & 330.1 & 300.4 & 1.65(0.35) \\ |
733 |
+ |
\hline |
734 |
+ |
\end{tabular} |
735 |
+ |
\label{interfaceRes} |
736 |
+ |
\end{center} |
737 |
+ |
\end{minipage} |
738 |
+ |
\end{table*} |
739 |
+ |
|
740 |
+ |
\subsection{Shear Viscosity} |
741 |
+ |
Our calculations (Table \ref{shearRate}) shows that scale RNEMD method |
742 |
+ |
produced comparable shear viscosity to swap RNEMD method. In Table |
743 |
+ |
\ref{shearRate}, the names of the calculated samples are devided into |
744 |
+ |
two parts. The first number refers to total slabs in one simulation |
745 |
+ |
box. The second number refers to the swapping interval in swap method, or |
746 |
+ |
in scale method the equilvalent swapping interval that the same |
747 |
+ |
momentum flux would theoretically result in swap method. All the scale |
748 |
+ |
method results were from simulations that had a scaling interval of 10 |
749 |
+ |
time steps. The average molecular momentum gradients of these samples |
750 |
+ |
are shown in Figure \ref{shear} (a) and (b). |
751 |
+ |
|
752 |
+ |
\begin{table*} |
753 |
+ |
\begin{minipage}{\linewidth} |
754 |
+ |
\begin{center} |
755 |
+ |
|
756 |
+ |
\caption{Calculation results for shear viscosity of Lennard-Jones |
757 |
+ |
fluid at ${T^* = 0.72}$ and ${\rho^* = 0.85}$, with swap and scale |
758 |
+ |
methods at various momentum exchange rates. Results in reduced |
759 |
+ |
unit. Errors of calculations in parentheses. } |
760 |
+ |
|
761 |
+ |
\begin{tabular}{ccccc} |
762 |
+ |
Swapping method & & & NIVS-RNEMD & \\ |
763 |
+ |
\hline |
764 |
+ |
Swap Interval (fs) & $\eta^*_{swap}$ & & Equilvalent $j_p^*(v_x)$ & |
765 |
+ |
$\eta^*_{scale}$\\ |
766 |
+ |
\hline |
767 |
+ |
500 & 3.64(0.05) & & 0.09 & 3.76(0.09)\\ |
768 |
+ |
1000 & 3.52(0.16) & & 0.046 & 3.66(0.06)\\ |
769 |
+ |
2000 & 3.72(0.05) & & 0.024 & 3.32(0.18)\\ |
770 |
+ |
2500 & 3.42(0.06) & & 0.019 & 3.43(0.08)\\ |
771 |
+ |
\hline |
772 |
+ |
\end{tabular} |
773 |
+ |
\label{shearRate} |
774 |
+ |
\end{center} |
775 |
+ |
\end{minipage} |
776 |
+ |
\end{table*} |
777 |
+ |
|
778 |
+ |
\begin{figure} |
779 |
+ |
\includegraphics[width=\linewidth]{shear} |
780 |
+ |
\caption{Average momentum gradients in shear viscosity simulations, |
781 |
+ |
using (a) ``swapping'' method and (b) NIVS-RNEMD method |
782 |
+ |
respectively. (c) Temperature difference among x and y, z dimensions |
783 |
+ |
observed when using NIVS-RNEMD with equivalent exchange interval of |
784 |
+ |
500 fs.} |
785 |
+ |
\label{shear} |
786 |
+ |
\end{figure} |
787 |
+ |
|
788 |
+ |
However, observations of temperatures along three dimensions show that |
789 |
+ |
inhomogeneity occurs in scaling RNEMD simulations, particularly in the |
790 |
+ |
two slabs which were scaled. Figure \ref{shear} (c) indicate that with |
791 |
+ |
relatively large imposed momentum flux, the temperature difference among $x$ |
792 |
+ |
and the other two dimensions was significant. This would result from the |
793 |
+ |
algorithm of scaling method. From Eq. \ref{eq:fluxPlane}, after |
794 |
+ |
momentum gradient is set up, $P_c^x$ would be roughly stable |
795 |
+ |
($<0$). Consequently, scaling factor $x$ would most probably larger |
796 |
+ |
than 1. Therefore, the kinetic energy in $x$-dimension $K_c^x$ would |
797 |
+ |
keep increase after most scaling steps. And if there is not enough time |
798 |
+ |
for the kinetic energy to exchange among different dimensions and |
799 |
+ |
different slabs, the system would finally build up temperature |
800 |
+ |
(kinetic energy) difference among the three dimensions. Also, between |
801 |
+ |
$y$ and $z$ dimensions in the scaled slabs, temperatures of $z$-axis |
802 |
+ |
are closer to neighbor slabs. This is due to momentum transfer along |
803 |
+ |
$z$ dimension between slabs. |
804 |
+ |
|
805 |
+ |
Although results between scaling and swapping methods are comparable, |
806 |
+ |
the inherent temperature inhomogeneity even in relatively low imposed |
807 |
+ |
exchange momentum flux simulations makes scaling RNEMD method less |
808 |
+ |
attractive than swapping RNEMD in shear viscosity calculation. |
809 |
+ |
|
810 |
+ |
\section{Conclusions} |
811 |
+ |
NIVS-RNEMD simulation method is developed and tested on various |
812 |
+ |
systems. Simulation results demonstrate its validity in thermal |
813 |
+ |
conductivity calculations, from Lennard-Jones fluid to multi-atom |
814 |
+ |
molecule like water and metal crystals. NIVS-RNEMD improves |
815 |
+ |
non-Boltzmann-Maxwell distributions, which exist in previous RNEMD |
816 |
+ |
methods. Furthermore, it develops a valid means for unphysical thermal |
817 |
+ |
transfer between different species of molecules, and thus extends its |
818 |
+ |
applicability to interfacial systems. Our calculation of gold/water |
819 |
+ |
interfacial thermal conductivity demonstrates this advantage over |
820 |
+ |
previous RNEMD methods. NIVS-RNEMD has also limited application on |
821 |
+ |
shear viscosity calculations, but could cause temperature difference |
822 |
+ |
among different dimensions under high momentum flux. Modification is |
823 |
+ |
necessary to extend the applicability of NIVS-RNEMD in shear viscosity |
824 |
+ |
calculations. |
825 |
+ |
|
826 |
|
\section{Acknowledgments} |
827 |
|
Support for this project was provided by the National Science |
828 |
|
Foundation under grant CHE-0848243. Computational time was provided by |
829 |
|
the Center for Research Computing (CRC) at the University of Notre |
830 |
|
Dame. \newpage |
831 |
|
|
832 |
< |
\bibliographystyle{jcp2} |
832 |
> |
\bibliographystyle{aip} |
833 |
|
\bibliography{nivsRnemd} |
834 |
+ |
|
835 |
|
\end{doublespace} |
836 |
|
\end{document} |
837 |
|
|