| 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 some |
| 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 which 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,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 (Fig. \ref{thermalDemo}). The transport |
| 81 |
< |
coefficients (shear viscosity and thermal conductivity) are easily |
| 82 |
< |
obtained by assuming 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_z & = & \lambda \frac{\partial T}{\partial z} |
| 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} |
| 90 |
> |
(e.g. ionic 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} |
| 91 |
|
|
| 92 |
|
\begin{figure} |
| 93 |
|
\includegraphics[width=\linewidth]{thermalDemo} |
| 94 |
< |
\caption{Demostration of thermal gradient estalished by RNEMD |
| 95 |
< |
method. Physical thermal flow directs from high temperature region |
| 96 |
< |
to low temperature region. Unphysical thermal transfer counteracts |
| 97 |
< |
it and maintains a steady thermal gradient.} |
| 94 |
> |
\caption{RNEMD methods impose an unphysical transfer of momentum or |
| 95 |
> |
kinetic energy between a ``hot'' slab and a ``cold'' slab in the |
| 96 |
> |
simulation box. The molecular system responds to this imposed flux |
| 97 |
> |
by generating a momentum or temperature gradient. The slope of the |
| 98 |
> |
gradient can then be used to compute transport properties (e.g. |
| 99 |
> |
shear viscosity and thermal conductivity).} |
| 100 |
|
\label{thermalDemo} |
| 101 |
|
\end{figure} |
| 102 |
|
|
| 103 |
< |
RNEMD is preferable in many ways to the forward NEMD methods because |
| 104 |
< |
it imposes what is typically difficult to measure (a flux or stress) |
| 105 |
< |
and it is typically much easier to compute momentum gradients or |
| 106 |
< |
strains (the response). For similar reasons, RNEMD is also preferable |
| 107 |
< |
to slowly-converging equilibrium methods for measuring thermal |
| 108 |
< |
conductivity and shear viscosity (using Green-Kubo relations or the |
| 109 |
< |
Helfand moment approach of Viscardy {\it et |
| 103 |
> |
RNEMD is preferable in many ways to the forward NEMD methods |
| 104 |
> |
[CITATIONS NEEDED] because it imposes what is typically difficult to measure |
| 105 |
> |
(a flux or stress) and it is typically much easier to compute momentum |
| 106 |
> |
gradients or strains (the response). For similar reasons, RNEMD is |
| 107 |
> |
also preferable to slowly-converging equilibrium methods for measuring |
| 108 |
> |
thermal conductivity and shear viscosity (using Green-Kubo relations |
| 109 |
> |
[CITATIONS NEEDED] or the Helfand moment approach of Viscardy {\it et |
| 110 |
|
al.}\cite{Viscardy:2007bh,Viscardy:2007lq}) because these rely on |
| 111 |
|
computing difficult to measure quantities. |
| 112 |
|
|
| 130 |
|
(conservation of linear momentum and total energy, compatibility with |
| 131 |
|
periodic boundary conditions) while establishing true thermal |
| 132 |
|
distributions in each of the two slabs. In the next section, we |
| 133 |
< |
develop the method for determining the scaling constraints. We then |
| 133 |
> |
present the method for determining the scaling constraints. We then |
| 134 |
|
test the method on both single component, multi-component, and |
| 135 |
|
non-isotropic mixtures and show that it is capable of providing |
| 136 |
|
reasonable estimates of the thermal conductivity and shear viscosity |
| 137 |
|
in these cases. |
| 138 |
|
|
| 139 |
|
\section{Methodology} |
| 140 |
< |
We retain the basic idea of Muller-Plathe's RNEMD method; the periodic |
| 141 |
< |
system is partitioned into a series of thin slabs along a particular |
| 140 |
> |
We retain the basic idea of M\"{u}ller-Plathe's RNEMD method; the |
| 141 |
> |
periodic system is partitioned into a series of thin slabs along one |
| 142 |
|
axis ($z$). One of the slabs at the end of the periodic box is |
| 143 |
|
designated the ``hot'' slab, while the slab in the center of the box |
| 144 |
|
is designated the ``cold'' slab. The artificial momentum flux will be |
| 146 |
|
hot slab. |
| 147 |
|
|
| 148 |
|
Rather than using momentum swaps, we use a series of velocity scaling |
| 149 |
< |
moves. For molecules $\{i\}$ located within the cold slab, |
| 149 |
> |
moves. For molecules $\{i\}$ located within the cold slab, |
| 150 |
|
\begin{equation} |
| 151 |
|
\vec{v}_i \leftarrow \left( \begin{array}{ccc} |
| 152 |
|
x & 0 & 0 \\ |
| 166 |
|
\end{equation} |
| 167 |
|
|
| 168 |
|
Conservation of linear momentum in each of the three directions |
| 169 |
< |
($\alpha = x,y,z$) ties the values of the hot and cold bin scaling |
| 169 |
> |
($\alpha = x,y,z$) ties the values of the hot and cold scaling |
| 170 |
|
parameters together: |
| 171 |
|
\begin{equation} |
| 172 |
|
P_h^\alpha + P_c^\alpha = \alpha^\prime P_h^\alpha + \alpha P_c^\alpha |
| 200 |
|
similar manner to the linear momenta (Eq. \ref{eq:momentumdef}). |
| 201 |
|
Substituting in the expressions for the hot scaling parameters |
| 202 |
|
($\alpha^\prime$) from Eq. (\ref{eq:hotcoldscaling}), we obtain the |
| 203 |
< |
{\it constraint ellipsoid equation}: |
| 203 |
> |
{\it constraint ellipsoid}: |
| 204 |
|
\begin{equation} |
| 205 |
|
\sum_{\alpha = x,y,z} a_\alpha \alpha^2 + b_\alpha \alpha + c_\alpha = 0 |
| 206 |
|
\label{eq:constraintEllipsoid} |
| 214 |
|
c_\alpha & = & K_h^\alpha p_\alpha^2 + 2 K_h^\alpha p_\alpha - K_c^\alpha |
| 215 |
|
\label{eq:constraintEllipsoidConsts} |
| 216 |
|
\end{eqnarray} |
| 217 |
< |
This ellipsoid equation defines the set of cold slab scaling |
| 218 |
< |
parameters which can be applied while preserving both linear momentum |
| 219 |
< |
in all three directions as well as kinetic energy. |
| 217 |
> |
This ellipsoid (Eq. \ref{eq:constraintEllipsoid}) defines the set of |
| 218 |
> |
cold slab scaling parameters which can be applied while preserving |
| 219 |
> |
both linear momentum in all three directions as well as total kinetic |
| 220 |
> |
energy. |
| 221 |
|
|
| 222 |
|
The goal of using velocity scaling variables is to transfer linear |
| 223 |
|
momentum or kinetic energy from the cold slab to the hot slab. If the |
| 232 |
|
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 |
| 233 |
|
\label{eq:fluxEllipsoid} |
| 234 |
|
\end{equation} |
| 235 |
< |
The spatial extent of the {\it thermal flux ellipsoid equation} is |
| 236 |
< |
governed both by a targetted value, $J_z$ as well as the instantaneous |
| 237 |
< |
values of the kinetic energy components in the cold bin. |
| 235 |
> |
The spatial extent of the {\it thermal flux ellipsoid} is governed |
| 236 |
> |
both by a targetted value, $J_z$ as well as the instantaneous values |
| 237 |
> |
of the kinetic energy components in the cold bin. |
| 238 |
|
|
| 239 |
|
To satisfy an energetic flux as well as the conservation constraints, |
| 240 |
< |
it is sufficient to determine the points ${x,y,z}$ which lie on both |
| 241 |
< |
the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the |
| 242 |
< |
flux ellipsoid (Eq. \ref{eq:fluxEllipsoid}), i.e. the intersection of |
| 243 |
< |
the two ellipsoids in 3-dimensional space. |
| 240 |
> |
we must determine the points ${x,y,z}$ which lie on both the |
| 241 |
> |
constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the flux |
| 242 |
> |
ellipsoid (Eq. \ref{eq:fluxEllipsoid}), i.e. the intersection of the |
| 243 |
> |
two ellipsoids in 3-dimensional space. |
| 244 |
|
|
| 245 |
|
\begin{figure} |
| 246 |
|
\includegraphics[width=\linewidth]{ellipsoids} |
| 253 |
|
\label{ellipsoids} |
| 254 |
|
\end{figure} |
| 255 |
|
|
| 256 |
< |
One may also define momentum flux (say along the $x$-direction) as: |
| 256 |
> |
One may also define {\it momentum} flux (say along the $x$-direction) as: |
| 257 |
|
\begin{equation} |
| 258 |
|
(1-x) P_c^x = j_z(p_x)\Delta t |
| 259 |
|
\label{eq:fluxPlane} |
| 260 |
|
\end{equation} |
| 261 |
< |
The above {\it momentum flux equation} is essentially a plane which is |
| 262 |
< |
perpendicular to the $x$-axis, with its position governed both by a |
| 263 |
< |
target value, $j_z(p_x)$ as well as the instantaneous value of the |
| 247 |
< |
momentum along the $x$-direction. |
| 261 |
> |
The above {\it momentum flux plane} is perpendicular to the $x$-axis, |
| 262 |
> |
with its position governed both by a target value, $j_z(p_x)$ as well |
| 263 |
> |
as the instantaneous value of the momentum along the $x$-direction. |
| 264 |
|
|
| 265 |
< |
Similarly, to satisfy a momentum flux as well as the conservation |
| 266 |
< |
constraints, it is sufficient to determine the points ${x,y,z}$ which |
| 267 |
< |
lie on both the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) |
| 268 |
< |
and the flux plane (Eq. \ref{eq:fluxPlane}), i.e. the intersection of |
| 269 |
< |
an ellipsoid and a plane in 3-dimensional space. |
| 265 |
> |
In order to satisfy a momentum flux as well as the conservation |
| 266 |
> |
constraints, we must determine the points ${x,y,z}$ which lie on both |
| 267 |
> |
the constraint ellipsoid (Eq. \ref{eq:constraintEllipsoid}) and the |
| 268 |
> |
flux plane (Eq. \ref{eq:fluxPlane}), i.e. the intersection of an |
| 269 |
> |
ellipsoid and a plane in 3-dimensional space. |
| 270 |
|
|
| 271 |
< |
To summarize, by solving respective equation sets, one can determine |
| 272 |
< |
possible sets of scaling variables for cold slab. And corresponding |
| 273 |
< |
sets of scaling variables for hot slab can be determine as well. |
| 271 |
> |
In both the momentum and energy flux scenarios, valid scaling |
| 272 |
> |
parameters are arrived at by solving geometric intersection problems |
| 273 |
> |
in $x, y, z$ space in order to obtain cold slab scaling parameters. |
| 274 |
> |
Once the scaling variables for the cold slab are known, the hot slab |
| 275 |
> |
scaling has also been determined. |
| 276 |
|
|
| 277 |
+ |
|
| 278 |
|
The following problem will be choosing an optimal set of scaling |
| 279 |
|
variables among the possible sets. Although this method is inherently |
| 280 |
|
non-isotropic, the goal is still to maintain the system as isotropic |
| 282 |
|
energies in different directions could become as close as each other |
| 283 |
|
after each scaling. Simultaneously, one would also like each scaling |
| 284 |
|
as gentle as possible, i.e. ${x,y,z \rightarrow 1}$, in order to avoid |
| 285 |
< |
large perturbation to the system. Therefore, one approach to obtain the |
| 286 |
< |
scaling variables would be constructing an criteria function, with |
| 285 |
> |
large perturbation to the system. Therefore, one approach to obtain |
| 286 |
> |
the scaling variables would be constructing an criteria function, with |
| 287 |
|
constraints as above equation sets, and solving the function's minimum |
| 288 |
|
by method like Lagrange multipliers. |
| 289 |
|
|
| 393 |
|
\subsection{ Water / Metal Thermal Conductivity} |
| 394 |
|
Another series of our simulation is the calculation of interfacial |
| 395 |
|
thermal conductivity of a Au/H$_2$O system. Respective calculations of |
| 396 |
< |
liquid water (SPC/E) and crystal gold (QSC) thermal conductivity were |
| 397 |
< |
performed and compared with current results to ensure the validity of |
| 398 |
< |
NIVS-RNEMD. After that, a mixture system was simulated. |
| 396 |
> |
liquid water (Extended Simple Point Charge model) and crystal gold |
| 397 |
> |
thermal conductivity were performed and compared with current results |
| 398 |
> |
to ensure the validity of NIVS-RNEMD. After that, a mixture system was |
| 399 |
> |
simulated. |
| 400 |
|
|
| 401 |
|
For thermal conductivity calculation of bulk water, a simulation box |
| 402 |
|
consisting of 1000 molecules were first equilibrated under ambient |
| 408 |
|
process was similar to Lennard-Jones fluid system. |
| 409 |
|
|
| 410 |
|
Thermal conductivity calculation of bulk crystal gold used a similar |
| 411 |
< |
protocol. The face centered cubic crystal simulation box consists of |
| 411 |
> |
protocol. Two types of force field parameters, Embedded Atom Method |
| 412 |
> |
(EAM) and Quantum Sutten-Chen (QSC) force field were used |
| 413 |
> |
respectively. The face-centered cubic crystal simulation box consists of |
| 414 |
|
2880 Au atoms. The lattice was first allowed volume change to relax |
| 415 |
|
under ambient temperature and pressure. Equilibrations in canonical and |
| 416 |
|
microcanonical ensemble were followed in order. With the simulation |
| 485 |
|
\end{figure} |
| 486 |
|
|
| 487 |
|
During these simulations, molecule velocities were recorded in 1000 of |
| 488 |
< |
all the snapshots. These velocity data were used to produce histograms |
| 489 |
< |
of velocity and speed distribution in different slabs. From these |
| 490 |
< |
histograms, it is observed that with increasing unphysical kinetic |
| 491 |
< |
energy flux, speed and velocity distribution of molecules in slabs |
| 492 |
< |
where swapping occured could deviate from Maxwell-Boltzmann |
| 493 |
< |
distribution. Figure \ref{histSwap} indicates how these distributions |
| 494 |
< |
deviate from ideal condition. In high temperature slabs, probability |
| 495 |
< |
density in low speed is confidently smaller than ideal distribution; |
| 496 |
< |
in low temperature slabs, probability density in high speed is smaller |
| 497 |
< |
than ideal. This phenomenon is observable even in our relatively low |
| 498 |
< |
swapping rate simulations. And this deviation could also leads to |
| 499 |
< |
deviation of distribution of velocity in various dimensions. One |
| 500 |
< |
feature of these deviated distribution is that in high temperature |
| 501 |
< |
slab, the ideal Gaussian peak was changed into a relatively flat |
| 502 |
< |
plateau; while in low temperature slab, that peak appears sharper. |
| 488 |
> |
all the snapshots of one single data collection process. These |
| 489 |
> |
velocity data were used to produce histograms of velocity and speed |
| 490 |
> |
distribution in different slabs. From these histograms, it is observed |
| 491 |
> |
that under relatively high unphysical kinetic energy flux, speed and |
| 492 |
> |
velocity distribution of molecules in slabs where swapping occured |
| 493 |
> |
could deviate from Maxwell-Boltzmann distribution. Figure |
| 494 |
> |
\ref{histSwap} illustrates how these distributions deviate from an |
| 495 |
> |
ideal distribution. In high temperature slab, probability density in |
| 496 |
> |
low speed is confidently smaller than ideal curve fit; in low |
| 497 |
> |
temperature slab, probability density in high speed is smaller than |
| 498 |
> |
ideal, while larger than ideal in low speed. This phenomenon is more |
| 499 |
> |
obvious in our high swapping rate simulations. And this deviation |
| 500 |
> |
could also leads to deviation of distribution of velocity in various |
| 501 |
> |
dimensions. One feature of these deviated distribution is that in high |
| 502 |
> |
temperature slab, the ideal Gaussian peak was changed into a |
| 503 |
> |
relatively flat plateau; while in low temperature slab, that peak |
| 504 |
> |
appears sharper. This problem is rooted in the mechanism of the |
| 505 |
> |
swapping method. Continually depleting low (high) speed particles in |
| 506 |
> |
the high (low) temperature slab could not be complemented by |
| 507 |
> |
diffusions of low (high) speed particles from neighbor slabs, unless |
| 508 |
> |
in suffciently low swapping rate. Simutaneously, surplus low speed |
| 509 |
> |
particles in the low temperature slab do not have sufficient time to |
| 510 |
> |
diffuse to neighbor slabs. However, thermal exchange rate should reach |
| 511 |
> |
a minimum level to produce an observable thermal gradient under noise |
| 512 |
> |
interference. Consequently, swapping RNEMD has a relatively narrow |
| 513 |
> |
choice of swapping rate to satisfy these above restrictions. |
| 514 |
|
|
| 515 |
|
\begin{figure} |
| 516 |
|
\includegraphics[width=\linewidth]{histSwap} |
| 517 |
< |
\caption{Speed distribution for thermal conductivity using swapping RNEMD.} |
| 517 |
> |
\caption{Speed distribution for thermal conductivity using swapping |
| 518 |
> |
RNEMD. Shown is from the simulation with 250 fs exchange interval.} |
| 519 |
|
\label{histSwap} |
| 520 |
|
\end{figure} |
| 521 |
|
|
| 522 |
+ |
Comparatively, NIVS-RNEMD has a speed distribution closer to the ideal |
| 523 |
+ |
curve fit (Figure \ref{histScale}). Essentially, after scaling, a |
| 524 |
+ |
Gaussian distribution function would remain Gaussian. Although a |
| 525 |
+ |
single scaling is non-isotropic in all three dimensions, our scaling |
| 526 |
+ |
coefficient criteria could help maintian the scaling region as |
| 527 |
+ |
isotropic as possible. On the other hand, scaling coefficients are |
| 528 |
+ |
preferred to be as close to 1 as possible, which also helps minimize |
| 529 |
+ |
the difference among different dimensions. This is possible if scaling |
| 530 |
+ |
interval and one-time thermal transfer energy are well |
| 531 |
+ |
chosen. Consequently, NIVS-RNEMD is able to impose an unphysical |
| 532 |
+ |
thermal flux as the previous RNEMD method without large perturbation |
| 533 |
+ |
to the distribution of velocity and speed in the exchange regions. |
| 534 |
+ |
|
| 535 |
|
\begin{figure} |
| 536 |
|
\includegraphics[width=\linewidth]{histScale} |
| 537 |
< |
\caption{Speed distribution for thermal conductivity using scaling RNEMD.} |
| 537 |
> |
\caption{Speed distribution for thermal conductivity using scaling |
| 538 |
> |
RNEMD. Shown is from the simulation with an equilvalent thermal flux |
| 539 |
> |
as an 250 fs exchange interval swapping simulation.} |
| 540 |
|
\label{histScale} |
| 541 |
|
\end{figure} |
| 542 |
|
|
| 543 |
|
\subsubsection{SPC/E Water} |
| 544 |
|
Our results of SPC/E water thermal conductivity are comparable to |
| 545 |
|
Bedrov {\it et al.}\cite{ISI:000090151400044}, which employed the |
| 546 |
< |
previous swapping RNEMD method for their calculation. Our simulations |
| 547 |
< |
were able to produce a similar temperature gradient to their |
| 548 |
< |
system. However, the average temperature of our system is 300K, while |
| 549 |
< |
theirs is 318K, which would be attributed for part of the difference |
| 550 |
< |
between the two series of results. Both methods yields values in |
| 551 |
< |
agreement with experiment. And this shows the applicability of our |
| 552 |
< |
method to multi-atom molecular system. |
| 546 |
> |
previous swapping RNEMD method for their calculation. Bedrov {\it et |
| 547 |
> |
al.}\cite{ISI:000090151400044} argued that exchange of the molecule |
| 548 |
> |
center-of-mass velocities instead of single atom velocities in a |
| 549 |
> |
molecule conserves the total kinetic energy and linear momentum. This |
| 550 |
> |
principle is adopted in our simulations. Scaling is applied to the |
| 551 |
> |
velocities of the rigid bodies of SPC/E model water molecules, instead |
| 552 |
> |
of each hydrogen and oxygen atoms in relevant water molecules. As |
| 553 |
> |
shown in Figure \ref{spceGrad}, temperature gradients were established |
| 554 |
> |
similar to their system. However, the average temperature of our |
| 555 |
> |
system is 300K, while theirs is 318K, which would be attributed for |
| 556 |
> |
part of the difference between the final calculation results (Table |
| 557 |
> |
\ref{spceThermal}). Both methods yields values in agreement with |
| 558 |
> |
experiment. And this shows the applicability of our method to |
| 559 |
> |
multi-atom molecular system. |
| 560 |
|
|
| 561 |
|
\begin{figure} |
| 562 |
|
\includegraphics[width=\linewidth]{spceGrad} |
| 589 |
|
\end{table*} |
| 590 |
|
|
| 591 |
|
\subsubsection{Crystal Gold} |
| 592 |
< |
Our results of gold thermal conductivity used QSC force field are |
| 593 |
< |
shown in Table \ref{AuThermal}. Although our calculation is smaller |
| 594 |
< |
than experimental value by an order of more than 100, this difference |
| 595 |
< |
is mainly attributed to the lack of electron interaction |
| 596 |
< |
representation in our force field parameters. Richardson {\it et |
| 597 |
< |
al.}\cite{ISI:A1992HX37800010} used similar force field parameters |
| 598 |
< |
in their metal thermal conductivity calculations. The EMD method they |
| 599 |
< |
employed in their simulations produced comparable results to |
| 600 |
< |
ours. Therefore, it is confident to conclude that NIVS-RNEMD is |
| 601 |
< |
applicable to metal force field system. |
| 592 |
> |
Our results of gold thermal conductivity using two force fields are |
| 593 |
> |
shown separately in Table \ref{qscThermal} and \ref{eamThermal}. In |
| 594 |
> |
these calculations,the end and middle slabs were excluded in thermal |
| 595 |
> |
gradient regession and only used as heat source and drain in the |
| 596 |
> |
systems. Our yielded values using EAM force field are slightly larger |
| 597 |
> |
than those using QSC force field. However, both series are |
| 598 |
> |
significantly smaller than experimental value by an order of more than |
| 599 |
> |
100. It has been verified that this difference is mainly attributed to |
| 600 |
> |
the lack of electron interaction representation in these force field |
| 601 |
> |
parameters. Richardson {\it et al.}\cite{Clancy:1992} used EAM |
| 602 |
> |
force field parameters in their metal thermal conductivity |
| 603 |
> |
calculations. The Non-Equilibrium MD method they employed in their |
| 604 |
> |
simulations produced comparable results to ours. As Zhang {\it et |
| 605 |
> |
al.}\cite{ISI:000231042800044} stated, thermal conductivity values |
| 606 |
> |
are influenced mainly by force field. Therefore, it is confident to |
| 607 |
> |
conclude that NIVS-RNEMD is applicable to metal force field system. |
| 608 |
|
|
| 609 |
|
\begin{figure} |
| 610 |
|
\includegraphics[width=\linewidth]{AuGrad} |
| 611 |
< |
\caption{Temperature gradients for crystal gold thermal conductivity.} |
| 611 |
> |
\caption{Temperature gradients for thermal conductivity calculation of |
| 612 |
> |
crystal gold using QSC force field.} |
| 613 |
|
\label{AuGrad} |
| 614 |
|
\end{figure} |
| 615 |
|
|
| 618 |
|
\begin{center} |
| 619 |
|
|
| 620 |
|
\caption{Calculation results for thermal conductivity of crystal gold |
| 621 |
< |
at ${\langle T\rangle}$ = 300K at various thermal exchange rates. Errors of |
| 622 |
< |
calculations in parentheses. } |
| 621 |
> |
using QSC force field at ${\langle T\rangle}$ = 300K at various |
| 622 |
> |
thermal exchange rates. Errors of calculations in parentheses. } |
| 623 |
|
|
| 624 |
< |
\begin{tabular}{ccc} |
| 624 |
> |
\begin{tabular}{cc} |
| 625 |
|
\hline |
| 626 |
|
$\langle dT/dz\rangle$(K/\AA) & $\lambda$(W/m/K)\\ |
| 564 |
– |
& This work & Previous simulations\cite{ISI:A1992HX37800010} \\ |
| 627 |
|
\hline |
| 628 |
< |
1.44 & 1.10(0.01) & \\ |
| 629 |
< |
2.86 & 1.08(0.02) & \\ |
| 630 |
< |
5.14 & 1.15(0.01) & \\ |
| 628 |
> |
1.44 & 1.10(0.01)\\ |
| 629 |
> |
2.86 & 1.08(0.02)\\ |
| 630 |
> |
5.14 & 1.15(0.01)\\ |
| 631 |
|
\hline |
| 632 |
|
\end{tabular} |
| 633 |
< |
\label{AuThermal} |
| 633 |
> |
\label{qscThermal} |
| 634 |
> |
\end{center} |
| 635 |
> |
\end{minipage} |
| 636 |
> |
\end{table*} |
| 637 |
> |
|
| 638 |
> |
\begin{figure} |
| 639 |
> |
\includegraphics[width=\linewidth]{eamGrad} |
| 640 |
> |
\caption{Temperature gradients for thermal conductivity calculation of |
| 641 |
> |
crystal gold using EAM force field.} |
| 642 |
> |
\label{eamGrad} |
| 643 |
> |
\end{figure} |
| 644 |
> |
|
| 645 |
> |
\begin{table*} |
| 646 |
> |
\begin{minipage}{\linewidth} |
| 647 |
> |
\begin{center} |
| 648 |
> |
|
| 649 |
> |
\caption{Calculation results for thermal conductivity of crystal gold |
| 650 |
> |
using EAM force field at ${\langle T\rangle}$ = 300K at various |
| 651 |
> |
thermal exchange rates. Errors of calculations in parentheses. } |
| 652 |
> |
|
| 653 |
> |
\begin{tabular}{cc} |
| 654 |
> |
\hline |
| 655 |
> |
$\langle dT/dz\rangle$(K/\AA) & $\lambda$(W/m/K)\\ |
| 656 |
> |
\hline |
| 657 |
> |
1.24 & 1.24(0.06)\\ |
| 658 |
> |
2.06 & 1.37(0.04)\\ |
| 659 |
> |
2.55 & 1.41(0.03)\\ |
| 660 |
> |
\hline |
| 661 |
> |
\end{tabular} |
| 662 |
> |
\label{eamThermal} |
| 663 |
|
\end{center} |
| 664 |
|
\end{minipage} |
| 665 |
|
\end{table*} |
| 666 |
|
|
| 667 |
+ |
|
| 668 |
|
\subsection{Interfaciel Thermal Conductivity} |
| 669 |
< |
After valid simulations of homogeneous water and gold systems using |
| 670 |
< |
NIVS-RNEMD method, calculation of gold/water interfacial thermal |
| 671 |
< |
conductivity was followed. It is found out that the interfacial |
| 672 |
< |
conductance is low due to a hydrophobic surface in our system. Figure |
| 673 |
< |
\ref{interfaceDensity} demonstrates this observance. Consequently, our |
| 674 |
< |
reported results (Table \ref{interfaceRes}) are of two orders of |
| 675 |
< |
magnitude smaller than our calculations on homogeneous systems. |
| 669 |
> |
After simulations of homogeneous water and gold systems using |
| 670 |
> |
NIVS-RNEMD method were proved valid, calculation of gold/water |
| 671 |
> |
interfacial thermal conductivity was followed. It is found out that |
| 672 |
> |
the low interfacial conductance is probably due to the hydrophobic |
| 673 |
> |
surface in our system. Figure \ref{interfaceDensity} demonstrates mass |
| 674 |
> |
density change along $z$-axis, which is perpendicular to the |
| 675 |
> |
gold/water interface. It is observed that water density significantly |
| 676 |
> |
decreases when approaching the surface. Under this low thermal |
| 677 |
> |
conductance, both gold and water phase have sufficient time to |
| 678 |
> |
eliminate temperature difference inside respectively (Figure |
| 679 |
> |
\ref{interfaceGrad}). With indistinguishable temperature difference |
| 680 |
> |
within respective phase, it is valid to assume that the temperature |
| 681 |
> |
difference between gold and water on surface would be approximately |
| 682 |
> |
the same as the difference between the gold and water phase. This |
| 683 |
> |
assumption enables convenient calculation of $G$ using |
| 684 |
> |
Eq. \ref{interfaceCalc} instead of measuring temperatures of thin |
| 685 |
> |
layer of water and gold close enough to surface, which would have |
| 686 |
> |
greater fluctuation and lower accuracy. Reported results (Table |
| 687 |
> |
\ref{interfaceRes}) are of two orders of magnitude smaller than our |
| 688 |
> |
calculations on homogeneous systems, and thus have larger relative |
| 689 |
> |
errors than our calculation results on homogeneous systems. |
| 690 |
|
|
| 691 |
|
\begin{figure} |
| 692 |
|
\includegraphics[width=\linewidth]{interfaceDensity} |
| 693 |
|
\caption{Density profile for interfacial thermal conductivity |
| 694 |
< |
simulation box.} |
| 694 |
> |
simulation box. Significant water density decrease is observed on |
| 695 |
> |
gold surface.} |
| 696 |
|
\label{interfaceDensity} |
| 697 |
|
\end{figure} |
| 698 |
|
|
| 699 |
|
\begin{figure} |
| 700 |
|
\includegraphics[width=\linewidth]{interfaceGrad} |
| 701 |
|
\caption{Temperature profiles for interfacial thermal conductivity |
| 702 |
< |
simulation box.} |
| 702 |
> |
simulation box. Temperatures of different slabs in the same phase |
| 703 |
> |
show no significant difference.} |
| 704 |
|
\label{interfaceGrad} |
| 705 |
|
\end{figure} |
| 706 |
|
|
| 798 |
|
|
| 799 |
|
\section{Conclusions} |
| 800 |
|
NIVS-RNEMD simulation method is developed and tested on various |
| 801 |
< |
systems. Simulation results demonstrate its validity of thermal |
| 802 |
< |
conductivity calculations. NIVS-RNEMD improves non-Boltzmann-Maxwell |
| 803 |
< |
distributions existing in previous RNEMD methods, and extends its |
| 804 |
< |
applicability to interfacial systems. NIVS-RNEMD has also limited |
| 805 |
< |
application on shear viscosity calculations, but under high momentum |
| 806 |
< |
flux, it could cause temperature difference among different |
| 807 |
< |
dimensions. Modification is necessary to extend the applicability of |
| 808 |
< |
NIVS-RNEMD in shear viscosity calculations. |
| 801 |
> |
systems. Simulation results demonstrate its validity in thermal |
| 802 |
> |
conductivity calculations, from Lennard-Jones fluid to multi-atom |
| 803 |
> |
molecule like water and metal crystals. NIVS-RNEMD improves |
| 804 |
> |
non-Boltzmann-Maxwell distributions, which exist in previous RNEMD |
| 805 |
> |
methods. Furthermore, it develops a valid means for unphysical thermal |
| 806 |
> |
transfer between different species of molecules, and thus extends its |
| 807 |
> |
applicability to interfacial systems. Our calculation of gold/water |
| 808 |
> |
interfacial thermal conductivity demonstrates this advantage over |
| 809 |
> |
previous RNEMD methods. NIVS-RNEMD has also limited application on |
| 810 |
> |
shear viscosity calculations, but could cause temperature difference |
| 811 |
> |
among different dimensions under high momentum flux. Modification is |
| 812 |
> |
necessary to extend the applicability of NIVS-RNEMD in shear viscosity |
| 813 |
> |
calculations. |
| 814 |
|
|
| 815 |
|
\section{Acknowledgments} |
| 816 |
|
Support for this project was provided by the National Science |
| 818 |
|
the Center for Research Computing (CRC) at the University of Notre |
| 819 |
|
Dame. \newpage |
| 820 |
|
|
| 821 |
< |
\bibliographystyle{jcp2} |
| 821 |
> |
\bibliographystyle{aip} |
| 822 |
|
\bibliography{nivsRnemd} |
| 823 |
+ |
|
| 824 |
|
\end{doublespace} |
| 825 |
|
\end{document} |
| 826 |
|
|
