6 |
|
Closely related to Classical Mechanics, Molecular Dynamics |
7 |
|
simulations are carried out by integrating the equations of motion |
8 |
|
for a given system of particles. There are three fundamental ideas |
9 |
< |
behind classical mechanics. Firstly, One can determine the state of |
9 |
> |
behind classical mechanics. Firstly, one can determine the state of |
10 |
|
a mechanical system at any time of interest; Secondly, all the |
11 |
|
mechanical properties of the system at that time can be determined |
12 |
|
by combining the knowledge of the properties of the system with the |
17 |
|
\subsection{\label{introSection:newtonian}Newtonian Mechanics} |
18 |
|
The discovery of Newton's three laws of mechanics which govern the |
19 |
|
motion of particles is the foundation of the classical mechanics. |
20 |
< |
Newton¡¯s first law defines a class of inertial frames. Inertial |
20 |
> |
Newton's first law defines a class of inertial frames. Inertial |
21 |
|
frames are reference frames where a particle not interacting with |
22 |
|
other bodies will move with constant speed in the same direction. |
23 |
< |
With respect to inertial frames Newton¡¯s second law has the form |
23 |
> |
With respect to inertial frames, Newton's second law has the form |
24 |
|
\begin{equation} |
25 |
< |
F = \frac {dp}{dt} = \frac {mv}{dt} |
25 |
> |
F = \frac {dp}{dt} = \frac {mdv}{dt} |
26 |
|
\label{introEquation:newtonSecondLaw} |
27 |
|
\end{equation} |
28 |
|
A point mass interacting with other bodies moves with the |
29 |
|
acceleration along the direction of the force acting on it. Let |
30 |
|
$F_{ij}$ be the force that particle $i$ exerts on particle $j$, and |
31 |
|
$F_{ji}$ be the force that particle $j$ exerts on particle $i$. |
32 |
< |
Newton¡¯s third law states that |
32 |
> |
Newton's third law states that |
33 |
|
\begin{equation} |
34 |
|
F_{ij} = -F_{ji} |
35 |
|
\label{introEquation:newtonThirdLaw} |
46 |
|
\end{equation} |
47 |
|
The torque $\tau$ with respect to the same origin is defined to be |
48 |
|
\begin{equation} |
49 |
< |
N \equiv r \times F \label{introEquation:torqueDefinition} |
49 |
> |
\tau \equiv r \times F \label{introEquation:torqueDefinition} |
50 |
|
\end{equation} |
51 |
|
Differentiating Eq.~\ref{introEquation:angularMomentumDefinition}, |
52 |
|
\[ |
59 |
|
\] |
60 |
|
thus, |
61 |
|
\begin{equation} |
62 |
< |
\dot L = r \times \dot p = N |
62 |
> |
\dot L = r \times \dot p = \tau |
63 |
|
\end{equation} |
64 |
|
If there are no external torques acting on a body, the angular |
65 |
|
momentum of it is conserved. The last conservation theorem state |
68 |
|
\end{equation} |
69 |
|
is conserved. All of these conserved quantities are |
70 |
|
important factors to determine the quality of numerical integration |
71 |
< |
scheme for rigid body \cite{Dullweber1997}. |
71 |
> |
schemes for rigid bodies \cite{Dullweber1997}. |
72 |
|
|
73 |
|
\subsection{\label{introSection:lagrangian}Lagrangian Mechanics} |
74 |
|
|
75 |
< |
Newtonian Mechanics suffers from two important limitations: it |
76 |
< |
describes their motion in special cartesian coordinate systems. |
77 |
< |
Another limitation of Newtonian mechanics becomes obvious when we |
78 |
< |
try to describe systems with large numbers of particles. It becomes |
79 |
< |
very difficult to predict the properties of the system by carrying |
80 |
< |
out calculations involving the each individual interaction between |
81 |
< |
all the particles, even if we know all of the details of the |
82 |
< |
interaction. In order to overcome some of the practical difficulties |
83 |
< |
which arise in attempts to apply Newton's equation to complex |
84 |
< |
system, alternative procedures may be developed. |
75 |
> |
Newtonian Mechanics suffers from two important limitations: motions |
76 |
> |
can only be described in cartesian coordinate systems. Moreover, It |
77 |
> |
become impossible to predict analytically the properties of the |
78 |
> |
system even if we know all of the details of the interaction. In |
79 |
> |
order to overcome some of the practical difficulties which arise in |
80 |
> |
attempts to apply Newton's equation to complex system, approximate |
81 |
> |
numerical procedures may be developed. |
82 |
|
|
83 |
< |
\subsubsection{\label{introSection:halmiltonPrinciple}Hamilton's |
84 |
< |
Principle} |
83 |
> |
\subsubsection{\label{introSection:halmiltonPrinciple}\textbf{Hamilton's |
84 |
> |
Principle}} |
85 |
|
|
86 |
|
Hamilton introduced the dynamical principle upon which it is |
87 |
< |
possible to base all of mechanics and, indeed, most of classical |
88 |
< |
physics. Hamilton's Principle may be stated as follow, |
87 |
> |
possible to base all of mechanics and most of classical physics. |
88 |
> |
Hamilton's Principle may be stated as follows, |
89 |
|
|
90 |
|
The actual trajectory, along which a dynamical system may move from |
91 |
|
one point to another within a specified time, is derived by finding |
92 |
|
the path which minimizes the time integral of the difference between |
93 |
< |
the kinetic, $K$, and potential energies, $U$ \cite{Tolman1979}. |
93 |
> |
the kinetic, $K$, and potential energies, $U$. |
94 |
|
\begin{equation} |
95 |
|
\delta \int_{t_1 }^{t_2 } {(K - U)dt = 0} , |
96 |
|
\label{introEquation:halmitonianPrinciple1} |
97 |
|
\end{equation} |
98 |
|
|
99 |
|
For simple mechanical systems, where the forces acting on the |
100 |
< |
different part are derivable from a potential and the velocities are |
101 |
< |
small compared with that of light, the Lagrangian function $L$ can |
102 |
< |
be define as the difference between the kinetic energy of the system |
106 |
< |
and its potential energy, |
100 |
> |
different parts are derivable from a potential, the Lagrangian |
101 |
> |
function $L$ can be defined as the difference between the kinetic |
102 |
> |
energy of the system and its potential energy, |
103 |
|
\begin{equation} |
104 |
|
L \equiv K - U = L(q_i ,\dot q_i ) , |
105 |
|
\label{introEquation:lagrangianDef} |
110 |
|
\label{introEquation:halmitonianPrinciple2} |
111 |
|
\end{equation} |
112 |
|
|
113 |
< |
\subsubsection{\label{introSection:equationOfMotionLagrangian}The |
114 |
< |
Equations of Motion in Lagrangian Mechanics} |
113 |
> |
\subsubsection{\label{introSection:equationOfMotionLagrangian}\textbf{The |
114 |
> |
Equations of Motion in Lagrangian Mechanics}} |
115 |
|
|
116 |
< |
For a holonomic system of $f$ degrees of freedom, the equations of |
117 |
< |
motion in the Lagrangian form is |
116 |
> |
For a system of $f$ degrees of freedom, the equations of motion in |
117 |
> |
the Lagrangian form is |
118 |
|
\begin{equation} |
119 |
|
\frac{d}{{dt}}\frac{{\partial L}}{{\partial \dot q_i }} - |
120 |
|
\frac{{\partial L}}{{\partial q_i }} = 0,{\rm{ }}i = 1, \ldots,f |
128 |
|
Arising from Lagrangian Mechanics, Hamiltonian Mechanics was |
129 |
|
introduced by William Rowan Hamilton in 1833 as a re-formulation of |
130 |
|
classical mechanics. If the potential energy of a system is |
131 |
< |
independent of generalized velocities, the generalized momenta can |
136 |
< |
be defined as |
131 |
> |
independent of velocities, the momenta can be defined as |
132 |
|
\begin{equation} |
133 |
|
p_i = \frac{\partial L}{\partial \dot q_i} |
134 |
|
\label{introEquation:generalizedMomenta} |
167 |
|
By identifying the coefficients of $dq_k$, $dp_k$ and dt, we can |
168 |
|
find |
169 |
|
\begin{equation} |
170 |
< |
\frac{{\partial H}}{{\partial p_k }} = q_k |
170 |
> |
\frac{{\partial H}}{{\partial p_k }} = \dot {q_k} |
171 |
|
\label{introEquation:motionHamiltonianCoordinate} |
172 |
|
\end{equation} |
173 |
|
\begin{equation} |
174 |
< |
\frac{{\partial H}}{{\partial q_k }} = - p_k |
174 |
> |
\frac{{\partial H}}{{\partial q_k }} = - \dot {p_k} |
175 |
|
\label{introEquation:motionHamiltonianMomentum} |
176 |
|
\end{equation} |
177 |
|
and |
188 |
|
|
189 |
|
An important difference between Lagrangian approach and the |
190 |
|
Hamiltonian approach is that the Lagrangian is considered to be a |
191 |
< |
function of the generalized velocities $\dot q_i$ and the |
192 |
< |
generalized coordinates $q_i$, while the Hamiltonian is considered |
193 |
< |
to be a function of the generalized momenta $p_i$ and the conjugate |
194 |
< |
generalized coordinate $q_i$. Hamiltonian Mechanics is more |
195 |
< |
appropriate for application to statistical mechanics and quantum |
196 |
< |
mechanics, since it treats the coordinate and its time derivative as |
197 |
< |
independent variables and it only works with 1st-order differential |
203 |
< |
equations\cite{Marion1990}. |
191 |
> |
function of the generalized velocities $\dot q_i$ and coordinates |
192 |
> |
$q_i$, while the Hamiltonian is considered to be a function of the |
193 |
> |
generalized momenta $p_i$ and the conjugate coordinates $q_i$. |
194 |
> |
Hamiltonian Mechanics is more appropriate for application to |
195 |
> |
statistical mechanics and quantum mechanics, since it treats the |
196 |
> |
coordinate and its time derivative as independent variables and it |
197 |
> |
only works with 1st-order differential equations\cite{Marion1990}. |
198 |
|
|
199 |
|
In Newtonian Mechanics, a system described by conservative forces |
200 |
|
conserves the total energy \ref{introEquation:energyConservation}. |
224 |
|
possible states. Each possible state of the system corresponds to |
225 |
|
one unique point in the phase space. For mechanical systems, the |
226 |
|
phase space usually consists of all possible values of position and |
227 |
< |
momentum variables. Consider a dynamic system in a cartesian space, |
228 |
< |
where each of the $6f$ coordinates and momenta is assigned to one of |
229 |
< |
$6f$ mutually orthogonal axes, the phase space of this system is a |
230 |
< |
$6f$ dimensional space. A point, $x = (q_1 , \ldots ,q_f ,p_1 , |
231 |
< |
\ldots ,p_f )$, with a unique set of values of $6f$ coordinates and |
232 |
< |
momenta is a phase space vector. |
227 |
> |
momentum variables. Consider a dynamic system of $f$ particles in a |
228 |
> |
cartesian space, where each of the $6f$ coordinates and momenta is |
229 |
> |
assigned to one of $6f$ mutually orthogonal axes, the phase space of |
230 |
> |
this system is a $6f$ dimensional space. A point, $x = (\rightarrow |
231 |
> |
q_1 , \ldots ,\rightarrow q_f ,\rightarrow p_1 , \ldots ,\rightarrow |
232 |
> |
p_f )$, with a unique set of values of $6f$ coordinates and momenta |
233 |
> |
is a phase space vector. |
234 |
> |
%%%fix me |
235 |
|
|
236 |
< |
A microscopic state or microstate of a classical system is |
241 |
< |
specification of the complete phase space vector of a system at any |
242 |
< |
instant in time. An ensemble is defined as a collection of systems |
243 |
< |
sharing one or more macroscopic characteristics but each being in a |
244 |
< |
unique microstate. The complete ensemble is specified by giving all |
245 |
< |
systems or microstates consistent with the common macroscopic |
246 |
< |
characteristics of the ensemble. Although the state of each |
247 |
< |
individual system in the ensemble could be precisely described at |
248 |
< |
any instance in time by a suitable phase space vector, when using |
249 |
< |
ensembles for statistical purposes, there is no need to maintain |
250 |
< |
distinctions between individual systems, since the numbers of |
251 |
< |
systems at any time in the different states which correspond to |
252 |
< |
different regions of the phase space are more interesting. Moreover, |
253 |
< |
in the point of view of statistical mechanics, one would prefer to |
254 |
< |
use ensembles containing a large enough population of separate |
255 |
< |
members so that the numbers of systems in such different states can |
256 |
< |
be regarded as changing continuously as we traverse different |
257 |
< |
regions of the phase space. The condition of an ensemble at any time |
236 |
> |
In statistical mechanics, the condition of an ensemble at any time |
237 |
|
can be regarded as appropriately specified by the density $\rho$ |
238 |
|
with which representative points are distributed over the phase |
239 |
< |
space. The density of distribution for an ensemble with $f$ degrees |
240 |
< |
of freedom is defined as, |
239 |
> |
space. The density distribution for an ensemble with $f$ degrees of |
240 |
> |
freedom is defined as, |
241 |
|
\begin{equation} |
242 |
|
\rho = \rho (q_1 , \ldots ,q_f ,p_1 , \ldots ,p_f ,t). |
243 |
|
\label{introEquation:densityDistribution} |
244 |
|
\end{equation} |
245 |
|
Governed by the principles of mechanics, the phase points change |
246 |
< |
their value which would change the density at any time at phase |
247 |
< |
space. Hence, the density of distribution is also to be taken as a |
246 |
> |
their locations which would change the density at any time at phase |
247 |
> |
space. Hence, the density distribution is also to be taken as a |
248 |
|
function of the time. |
249 |
|
|
250 |
|
The number of systems $\delta N$ at time $t$ can be determined by, |
252 |
|
\delta N = \rho (q,p,t)dq_1 \ldots dq_f dp_1 \ldots dp_f. |
253 |
|
\label{introEquation:deltaN} |
254 |
|
\end{equation} |
255 |
< |
Assuming a large enough population of systems are exploited, we can |
256 |
< |
sufficiently approximate $\delta N$ without introducing |
257 |
< |
discontinuity when we go from one region in the phase space to |
258 |
< |
another. By integrating over the whole phase space, |
255 |
> |
Assuming a large enough population of systems, we can sufficiently |
256 |
> |
approximate $\delta N$ without introducing discontinuity when we go |
257 |
> |
from one region in the phase space to another. By integrating over |
258 |
> |
the whole phase space, |
259 |
|
\begin{equation} |
260 |
|
N = \int { \ldots \int {\rho (q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f |
261 |
|
\label{introEquation:totalNumberSystem} |
267 |
|
{\rho (q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}. |
268 |
|
\label{introEquation:unitProbability} |
269 |
|
\end{equation} |
270 |
< |
With the help of Equation(\ref{introEquation:unitProbability}) and |
271 |
< |
the knowledge of the system, it is possible to calculate the average |
270 |
> |
With the help of Eq.~\ref{introEquation:unitProbability} and the |
271 |
> |
knowledge of the system, it is possible to calculate the average |
272 |
|
value of any desired quantity which depends on the coordinates and |
273 |
|
momenta of the system. Even when the dynamics of the real system is |
274 |
|
complex, or stochastic, or even discontinuous, the average |
275 |
< |
properties of the ensemble of possibilities as a whole may still |
276 |
< |
remain well defined. For a classical system in thermal equilibrium |
277 |
< |
with its environment, the ensemble average of a mechanical quantity, |
278 |
< |
$\langle A(q , p) \rangle_t$, takes the form of an integral over the |
279 |
< |
phase space of the system, |
275 |
> |
properties of the ensemble of possibilities as a whole remaining |
276 |
> |
well defined. For a classical system in thermal equilibrium with its |
277 |
> |
environment, the ensemble average of a mechanical quantity, $\langle |
278 |
> |
A(q , p) \rangle_t$, takes the form of an integral over the phase |
279 |
> |
space of the system, |
280 |
|
\begin{equation} |
281 |
|
\langle A(q , p) \rangle_t = \frac{{\int { \ldots \int {A(q,p)\rho |
282 |
|
(q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}{{\int { \ldots \int {\rho |
286 |
|
|
287 |
|
There are several different types of ensembles with different |
288 |
|
statistical characteristics. As a function of macroscopic |
289 |
< |
parameters, such as temperature \textit{etc}, partition function can |
290 |
< |
be used to describe the statistical properties of a system in |
289 |
> |
parameters, such as temperature \textit{etc}, the partition function |
290 |
> |
can be used to describe the statistical properties of a system in |
291 |
|
thermodynamic equilibrium. |
292 |
|
|
293 |
|
As an ensemble of systems, each of which is known to be thermally |
294 |
< |
isolated and conserve energy, Microcanonical ensemble(NVE) has a |
295 |
< |
partition function like, |
294 |
> |
isolated and conserve energy, the Microcanonical ensemble (NVE) has |
295 |
> |
a partition function like, |
296 |
|
\begin{equation} |
297 |
|
\Omega (N,V,E) = e^{\beta TS} \label{introEquation:NVEPartition}. |
298 |
|
\end{equation} |
299 |
< |
A canonical ensemble(NVT)is an ensemble of systems, each of which |
299 |
> |
A canonical ensemble (NVT)is an ensemble of systems, each of which |
300 |
|
can share its energy with a large heat reservoir. The distribution |
301 |
|
of the total energy amongst the possible dynamical states is given |
302 |
|
by the partition function, |
305 |
|
\label{introEquation:NVTPartition} |
306 |
|
\end{equation} |
307 |
|
Here, $A$ is the Helmholtz free energy which is defined as $ A = U - |
308 |
< |
TS$. Since most experiment are carried out under constant pressure |
309 |
< |
condition, isothermal-isobaric ensemble(NPT) play a very important |
310 |
< |
role in molecular simulation. The isothermal-isobaric ensemble allow |
311 |
< |
the system to exchange energy with a heat bath of temperature $T$ |
312 |
< |
and to change the volume as well. Its partition function is given as |
308 |
> |
TS$. Since most experiments are carried out under constant pressure |
309 |
> |
condition, the isothermal-isobaric ensemble (NPT) plays a very |
310 |
> |
important role in molecular simulations. The isothermal-isobaric |
311 |
> |
ensemble allow the system to exchange energy with a heat bath of |
312 |
> |
temperature $T$ and to change the volume as well. Its partition |
313 |
> |
function is given as |
314 |
|
\begin{equation} |
315 |
|
\Delta (N,P,T) = - e^{\beta G}. |
316 |
|
\label{introEquation:NPTPartition} |
319 |
|
|
320 |
|
\subsection{\label{introSection:liouville}Liouville's theorem} |
321 |
|
|
322 |
< |
The Liouville's theorem is the foundation on which statistical |
323 |
< |
mechanics rests. It describes the time evolution of phase space |
322 |
> |
Liouville's theorem is the foundation on which statistical mechanics |
323 |
> |
rests. It describes the time evolution of the phase space |
324 |
|
distribution function. In order to calculate the rate of change of |
325 |
< |
$\rho$, we begin from Equation(\ref{introEquation:deltaN}). If we |
326 |
< |
consider the two faces perpendicular to the $q_1$ axis, which are |
327 |
< |
located at $q_1$ and $q_1 + \delta q_1$, the number of phase points |
328 |
< |
leaving the opposite face is given by the expression, |
325 |
> |
$\rho$, we begin from Eq.~\ref{introEquation:deltaN}. If we consider |
326 |
> |
the two faces perpendicular to the $q_1$ axis, which are located at |
327 |
> |
$q_1$ and $q_1 + \delta q_1$, the number of phase points leaving the |
328 |
> |
opposite face is given by the expression, |
329 |
|
\begin{equation} |
330 |
|
\left( {\rho + \frac{{\partial \rho }}{{\partial q_1 }}\delta q_1 } |
331 |
|
\right)\left( {\dot q_1 + \frac{{\partial \dot q_1 }}{{\partial q_1 |
349 |
|
+ \frac{{\partial \dot p_i }}{{\partial p_i }}} \right)} = 0 , |
350 |
|
\end{equation} |
351 |
|
which cancels the first terms of the right hand side. Furthermore, |
352 |
< |
divining $ \delta q_1 \ldots \delta q_f \delta p_1 \ldots \delta |
352 |
> |
dividing $ \delta q_1 \ldots \delta q_f \delta p_1 \ldots \delta |
353 |
|
p_f $ in both sides, we can write out Liouville's theorem in a |
354 |
|
simple form, |
355 |
|
\begin{equation} |
361 |
|
|
362 |
|
Liouville's theorem states that the distribution function is |
363 |
|
constant along any trajectory in phase space. In classical |
364 |
< |
statistical mechanics, since the number of particles in the system |
365 |
< |
is huge, we may be able to believe the system is stationary, |
364 |
> |
statistical mechanics, since the number of members in an ensemble is |
365 |
> |
huge and constant, we can assume the local density has no reason |
366 |
> |
(other than classical mechanics) to change, |
367 |
|
\begin{equation} |
368 |
|
\frac{{\partial \rho }}{{\partial t}} = 0. |
369 |
|
\label{introEquation:stationary} |
376 |
|
\label{introEquation:densityAndHamiltonian} |
377 |
|
\end{equation} |
378 |
|
|
379 |
< |
\subsubsection{\label{introSection:phaseSpaceConservation}Conservation of Phase Space} |
379 |
> |
\subsubsection{\label{introSection:phaseSpaceConservation}\textbf{Conservation of Phase Space}} |
380 |
|
Lets consider a region in the phase space, |
381 |
|
\begin{equation} |
382 |
|
\delta v = \int { \ldots \int {dq_1 } ...dq_f dp_1 } ..dp_f . |
383 |
|
\end{equation} |
384 |
|
If this region is small enough, the density $\rho$ can be regarded |
385 |
< |
as uniform over the whole phase space. Thus, the number of phase |
386 |
< |
points inside this region is given by, |
385 |
> |
as uniform over the whole integral. Thus, the number of phase points |
386 |
> |
inside this region is given by, |
387 |
|
\begin{equation} |
388 |
|
\delta N = \rho \delta v = \rho \int { \ldots \int {dq_1 } ...dq_f |
389 |
|
dp_1 } ..dp_f. |
395 |
|
\end{equation} |
396 |
|
With the help of stationary assumption |
397 |
|
(\ref{introEquation:stationary}), we obtain the principle of the |
398 |
< |
\emph{conservation of extension in phase space}, |
398 |
> |
\emph{conservation of volume in phase space}, |
399 |
|
\begin{equation} |
400 |
|
\frac{d}{{dt}}(\delta v) = \frac{d}{{dt}}\int { \ldots \int {dq_1 } |
401 |
|
...dq_f dp_1 } ..dp_f = 0. |
402 |
|
\label{introEquation:volumePreserving} |
403 |
|
\end{equation} |
404 |
|
|
405 |
< |
\subsubsection{\label{introSection:liouvilleInOtherForms}Liouville's Theorem in Other Forms} |
405 |
> |
\subsubsection{\label{introSection:liouvilleInOtherForms}\textbf{Liouville's Theorem in Other Forms}} |
406 |
|
|
407 |
|
Liouville's theorem can be expresses in a variety of different forms |
408 |
|
which are convenient within different contexts. For any two function |
416 |
|
\label{introEquation:poissonBracket} |
417 |
|
\end{equation} |
418 |
|
Substituting equations of motion in Hamiltonian formalism( |
419 |
< |
\ref{introEquation:motionHamiltonianCoordinate} , |
420 |
< |
\ref{introEquation:motionHamiltonianMomentum} ) into |
421 |
< |
(\ref{introEquation:liouvilleTheorem}), we can rewrite Liouville's |
422 |
< |
theorem using Poisson bracket notion, |
419 |
> |
Eq.~\ref{introEquation:motionHamiltonianCoordinate} , |
420 |
> |
Eq.~\ref{introEquation:motionHamiltonianMomentum} ) into |
421 |
> |
(Eq.~\ref{introEquation:liouvilleTheorem}), we can rewrite |
422 |
> |
Liouville's theorem using Poisson bracket notion, |
423 |
|
\begin{equation} |
424 |
|
\left( {\frac{{\partial \rho }}{{\partial t}}} \right) = - \left\{ |
425 |
|
{\rho ,H} \right\}. |
444 |
|
Various thermodynamic properties can be calculated from Molecular |
445 |
|
Dynamics simulation. By comparing experimental values with the |
446 |
|
calculated properties, one can determine the accuracy of the |
447 |
< |
simulation and the quality of the underlying model. However, both of |
448 |
< |
experiment and computer simulation are usually performed during a |
447 |
> |
simulation and the quality of the underlying model. However, both |
448 |
> |
experiments and computer simulations are usually performed during a |
449 |
|
certain time interval and the measurements are averaged over a |
450 |
|
period of them which is different from the average behavior of |
451 |
< |
many-body system in Statistical Mechanics. Fortunately, Ergodic |
452 |
< |
Hypothesis is proposed to make a connection between time average and |
453 |
< |
ensemble average. It states that time average and average over the |
451 |
> |
many-body system in Statistical Mechanics. Fortunately, the Ergodic |
452 |
> |
Hypothesis makes a connection between time average and the ensemble |
453 |
> |
average. It states that the time average and average over the |
454 |
|
statistical ensemble are identical \cite{Frenkel1996, Leach2001}. |
455 |
|
\begin{equation} |
456 |
|
\langle A(q , p) \rangle_t = \mathop {\lim }\limits_{t \to \infty } |
472 |
|
choice\cite{Frenkel1996}. |
473 |
|
|
474 |
|
\section{\label{introSection:geometricIntegratos}Geometric Integrators} |
475 |
< |
A variety of numerical integrators were proposed to simulate the |
476 |
< |
motions. They usually begin with an initial conditionals and move |
477 |
< |
the objects in the direction governed by the differential equations. |
478 |
< |
However, most of them ignore the hidden physical law contained |
479 |
< |
within the equations. Since 1990, geometric integrators, which |
480 |
< |
preserve various phase-flow invariants such as symplectic structure, |
481 |
< |
volume and time reversal symmetry, are developed to address this |
482 |
< |
issue\cite{Dullweber1997, McLachlan1998, Leimkuhler1999}. The |
483 |
< |
velocity verlet method, which happens to be a simple example of |
484 |
< |
symplectic integrator, continues to gain its popularity in molecular |
485 |
< |
dynamics community. This fact can be partly explained by its |
486 |
< |
geometric nature. |
475 |
> |
A variety of numerical integrators have been proposed to simulate |
476 |
> |
the motions of atoms in MD simulation. They usually begin with |
477 |
> |
initial conditionals and move the objects in the direction governed |
478 |
> |
by the differential equations. However, most of them ignore the |
479 |
> |
hidden physical laws contained within the equations. Since 1990, |
480 |
> |
geometric integrators, which preserve various phase-flow invariants |
481 |
> |
such as symplectic structure, volume and time reversal symmetry, are |
482 |
> |
developed to address this issue\cite{Dullweber1997, McLachlan1998, |
483 |
> |
Leimkuhler1999}. The velocity Verlet method, which happens to be a |
484 |
> |
simple example of symplectic integrator, continues to gain |
485 |
> |
popularity in the molecular dynamics community. This fact can be |
486 |
> |
partly explained by its geometric nature. |
487 |
|
|
488 |
< |
\subsection{\label{introSection:symplecticManifold}Symplectic Manifold} |
489 |
< |
A \emph{manifold} is an abstract mathematical space. It locally |
490 |
< |
looks like Euclidean space, but when viewed globally, it may have |
491 |
< |
more complicate structure. A good example of manifold is the surface |
492 |
< |
of Earth. It seems to be flat locally, but it is round if viewed as |
493 |
< |
a whole. A \emph{differentiable manifold} (also known as |
494 |
< |
\emph{smooth manifold}) is a manifold with an open cover in which |
495 |
< |
the covering neighborhoods are all smoothly isomorphic to one |
496 |
< |
another. In other words,it is possible to apply calculus on |
516 |
< |
\emph{differentiable manifold}. A \emph{symplectic manifold} is |
517 |
< |
defined as a pair $(M, \omega)$ which consisting of a |
488 |
> |
\subsection{\label{introSection:symplecticManifold}Symplectic Manifolds} |
489 |
> |
A \emph{manifold} is an abstract mathematical space. It looks |
490 |
> |
locally like Euclidean space, but when viewed globally, it may have |
491 |
> |
more complicated structure. A good example of manifold is the |
492 |
> |
surface of Earth. It seems to be flat locally, but it is round if |
493 |
> |
viewed as a whole. A \emph{differentiable manifold} (also known as |
494 |
> |
\emph{smooth manifold}) is a manifold on which it is possible to |
495 |
> |
apply calculus on \emph{differentiable manifold}. A \emph{symplectic |
496 |
> |
manifold} is defined as a pair $(M, \omega)$ which consists of a |
497 |
|
\emph{differentiable manifold} $M$ and a close, non-degenerated, |
498 |
|
bilinear symplectic form, $\omega$. A symplectic form on a vector |
499 |
|
space $V$ is a function $\omega(x, y)$ which satisfies |
500 |
|
$\omega(\lambda_1x_1+\lambda_2x_2, y) = \lambda_1\omega(x_1, y)+ |
501 |
|
\lambda_2\omega(x_2, y)$, $\omega(x, y) = - \omega(y, x)$ and |
502 |
< |
$\omega(x, x) = 0$. Cross product operation in vector field is an |
503 |
< |
example of symplectic form. |
502 |
> |
$\omega(x, x) = 0$. The cross product operation in vector field is |
503 |
> |
an example of symplectic form. |
504 |
|
|
505 |
< |
One of the motivations to study \emph{symplectic manifold} in |
505 |
> |
One of the motivations to study \emph{symplectic manifolds} in |
506 |
|
Hamiltonian Mechanics is that a symplectic manifold can represent |
507 |
|
all possible configurations of the system and the phase space of the |
508 |
|
system can be described by it's cotangent bundle. Every symplectic |
509 |
|
manifold is even dimensional. For instance, in Hamilton equations, |
510 |
|
coordinate and momentum always appear in pairs. |
511 |
|
|
533 |
– |
Let $(M,\omega)$ and $(N, \eta)$ be symplectic manifolds. A map |
534 |
– |
\[ |
535 |
– |
f : M \rightarrow N |
536 |
– |
\] |
537 |
– |
is a \emph{symplectomorphism} if it is a \emph{diffeomorphims} and |
538 |
– |
the \emph{pullback} of $\eta$ under f is equal to $\omega$. |
539 |
– |
Canonical transformation is an example of symplectomorphism in |
540 |
– |
classical mechanics. |
541 |
– |
|
512 |
|
\subsection{\label{introSection:ODE}Ordinary Differential Equations} |
513 |
|
|
514 |
< |
For a ordinary differential system defined as |
514 |
> |
For an ordinary differential system defined as |
515 |
|
\begin{equation} |
516 |
|
\dot x = f(x) |
517 |
|
\end{equation} |
518 |
< |
where $x = x(q,p)^T$, this system is canonical Hamiltonian, if |
518 |
> |
where $x = x(q,p)^T$, this system is a canonical Hamiltonian, if |
519 |
|
\begin{equation} |
520 |
|
f(r) = J\nabla _x H(r). |
521 |
|
\end{equation} |
575 |
|
\end{equation} |
576 |
|
|
577 |
|
In most cases, it is not easy to find the exact flow $\varphi_\tau$. |
578 |
< |
Instead, we use a approximate map, $\psi_\tau$, which is usually |
578 |
> |
Instead, we use an approximate map, $\psi_\tau$, which is usually |
579 |
|
called integrator. The order of an integrator $\psi_\tau$ is $p$, if |
580 |
|
the Taylor series of $\psi_\tau$ agree to order $p$, |
581 |
|
\begin{equation} |
582 |
< |
\psi_tau(x) = x + \tau f(x) + O(\tau^{p+1}) |
582 |
> |
\psi_\tau(x) = x + \tau f(x) + O(\tau^{p+1}) |
583 |
|
\end{equation} |
584 |
|
|
585 |
|
\subsection{\label{introSection:geometricProperties}Geometric Properties} |
586 |
|
|
587 |
< |
The hidden geometric properties\cite{Budd1999, Marsden1998} of ODE |
588 |
< |
and its flow play important roles in numerical studies. Many of them |
589 |
< |
can be found in systems which occur naturally in applications. |
587 |
> |
The hidden geometric properties\cite{Budd1999, Marsden1998} of an |
588 |
> |
ODE and its flow play important roles in numerical studies. Many of |
589 |
> |
them can be found in systems which occur naturally in applications. |
590 |
|
|
591 |
|
Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is |
592 |
|
a \emph{symplectic} flow if it satisfies, |
601 |
|
\begin{equation} |
602 |
|
{\varphi '}^T J \varphi ' = J \circ \varphi |
603 |
|
\end{equation} |
604 |
< |
is the property must be preserved by the integrator. |
604 |
> |
is the property that must be preserved by the integrator. |
605 |
|
|
606 |
|
It is possible to construct a \emph{volume-preserving} flow for a |
607 |
< |
source free($ \nabla \cdot f = 0 $) ODE, if the flow satisfies $ |
607 |
> |
source free ODE ($ \nabla \cdot f = 0 $), if the flow satisfies $ |
608 |
|
\det d\varphi = 1$. One can show easily that a symplectic flow will |
609 |
|
be volume-preserving. |
610 |
|
|
611 |
< |
Changing the variables $y = h(x)$ in a ODE\ref{introEquation:ODE} |
612 |
< |
will result in a new system, |
611 |
> |
Changing the variables $y = h(x)$ in an ODE |
612 |
> |
(Eq.~\ref{introEquation:ODE}) will result in a new system, |
613 |
|
\[ |
614 |
|
\dot y = \tilde f(y) = ((dh \cdot f)h^{ - 1} )(y). |
615 |
|
\] |
659 |
|
A lot of well established and very effective numerical methods have |
660 |
|
been successful precisely because of their symplecticities even |
661 |
|
though this fact was not recognized when they were first |
662 |
< |
constructed. The most famous example is leapfrog methods in |
663 |
< |
molecular dynamics. In general, symplectic integrators can be |
662 |
> |
constructed. The most famous example is the Verlet-leapfrog method |
663 |
> |
in molecular dynamics. In general, symplectic integrators can be |
664 |
|
constructed using one of four different methods. |
665 |
|
\begin{enumerate} |
666 |
|
\item Generating functions |
678 |
|
high-order explicit Runge-Kutta methods |
679 |
|
\cite{Owren1992,Chen2003}have been developed to overcome this |
680 |
|
instability. However, due to computational penalty involved in |
681 |
< |
implementing the Runge-Kutta methods, they do not attract too much |
682 |
< |
attention from Molecular Dynamics community. Instead, splitting have |
683 |
< |
been widely accepted since they exploit natural decompositions of |
684 |
< |
the system\cite{Tuckerman1992, McLachlan1998}. |
681 |
> |
implementing the Runge-Kutta methods, they have not attracted much |
682 |
> |
attention from the Molecular Dynamics community. Instead, splitting |
683 |
> |
methods have been widely accepted since they exploit natural |
684 |
> |
decompositions of the system\cite{Tuckerman1992, McLachlan1998}. |
685 |
|
|
686 |
< |
\subsubsection{\label{introSection:splittingMethod}Splitting Method} |
686 |
> |
\subsubsection{\label{introSection:splittingMethod}\textbf{Splitting Methods}} |
687 |
|
|
688 |
|
The main idea behind splitting methods is to decompose the discrete |
689 |
|
$\varphi_h$ as a composition of simpler flows, |
704 |
|
energy respectively, which is a natural decomposition of the |
705 |
|
problem. If $H_1$ and $H_2$ can be integrated using exact flows |
706 |
|
$\varphi_1(t)$ and $\varphi_2(t)$, respectively, a simple first |
707 |
< |
order is then given by the Lie-Trotter formula |
707 |
> |
order expression is then given by the Lie-Trotter formula |
708 |
|
\begin{equation} |
709 |
|
\varphi _h = \varphi _{1,h} \circ \varphi _{2,h}, |
710 |
|
\label{introEquation:firstOrderSplitting} |
730 |
|
\varphi _h = \varphi _{1,h/2} \circ \varphi _{2,h} \circ \varphi |
731 |
|
_{1,h/2} , \label{introEquation:secondOrderSplitting} |
732 |
|
\end{equation} |
733 |
< |
which has a local error proportional to $h^3$. Sprang splitting's |
734 |
< |
popularity in molecular simulation community attribute to its |
735 |
< |
symmetric property, |
733 |
> |
which has a local error proportional to $h^3$. The Sprang |
734 |
> |
splitting's popularity in molecular simulation community attribute |
735 |
> |
to its symmetric property, |
736 |
|
\begin{equation} |
737 |
|
\varphi _h^{ - 1} = \varphi _{ - h}. |
738 |
|
\label{introEquation:timeReversible} |
739 |
|
\end{equation} |
740 |
|
|
741 |
< |
\subsubsection{\label{introSection:exampleSplittingMethod}Example of Splitting Method} |
741 |
> |
\subsubsection{\label{introSection:exampleSplittingMethod}\textbf{Examples of the Splitting Method}} |
742 |
|
The classical equation for a system consisting of interacting |
743 |
|
particles can be written in Hamiltonian form, |
744 |
|
\[ |
745 |
|
H = T + V |
746 |
|
\] |
747 |
|
where $T$ is the kinetic energy and $V$ is the potential energy. |
748 |
< |
Setting $H_1 = T, H_2 = V$ and applying Strang splitting, one |
748 |
> |
Setting $H_1 = T, H_2 = V$ and applying the Strang splitting, one |
749 |
|
obtains the following: |
750 |
|
\begin{align} |
751 |
|
q(\Delta t) &= q(0) + \dot{q}(0)\Delta t + |
772 |
|
\label{introEquation:Lp9b}\\% |
773 |
|
% |
774 |
|
\dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) + |
775 |
< |
\frac{\Delta t}{2m}\, F[q(0)]. \label{introEquation:Lp9c} |
775 |
> |
\frac{\Delta t}{2m}\, F[q(t)]. \label{introEquation:Lp9c} |
776 |
|
\end{align} |
777 |
|
From the preceding splitting, one can see that the integration of |
778 |
|
the equations of motion would follow: |
781 |
|
|
782 |
|
\item Use the half step velocities to move positions one whole step, $\Delta t$. |
783 |
|
|
784 |
< |
\item Evaluate the forces at the new positions, $\mathbf{r}(\Delta t)$, and use the new forces to complete the velocity move. |
784 |
> |
\item Evaluate the forces at the new positions, $\mathbf{q}(\Delta t)$, and use the new forces to complete the velocity move. |
785 |
|
|
786 |
|
\item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values. |
787 |
|
\end{enumerate} |
788 |
|
|
789 |
< |
Simply switching the order of splitting and composing, a new |
790 |
< |
integrator, the \emph{position verlet} integrator, can be generated, |
789 |
> |
By simply switching the order of the propagators in the splitting |
790 |
> |
and composing a new integrator, the \emph{position verlet} |
791 |
> |
integrator, can be generated, |
792 |
|
\begin{align} |
793 |
|
\dot q(\Delta t) &= \dot q(0) + \Delta tF(q(0))\left[ {q(0) + |
794 |
|
\frac{{\Delta t}}{{2m}}\dot q(0)} \right], % |
799 |
|
\label{introEquation:positionVerlet2} |
800 |
|
\end{align} |
801 |
|
|
802 |
< |
\subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods} |
802 |
> |
\subsubsection{\label{introSection:errorAnalysis}\textbf{Error Analysis and Higher Order Methods}} |
803 |
|
|
804 |
< |
Baker-Campbell-Hausdorff formula can be used to determine the local |
805 |
< |
error of splitting method in terms of commutator of the |
804 |
> |
The Baker-Campbell-Hausdorff formula can be used to determine the |
805 |
> |
local error of splitting method in terms of the commutator of the |
806 |
|
operators(\ref{introEquation:exponentialOperator}) associated with |
807 |
< |
the sub-flow. For operators $hX$ and $hY$ which are associate to |
807 |
> |
the sub-flow. For operators $hX$ and $hY$ which are associated with |
808 |
|
$\varphi_1(t)$ and $\varphi_2(t)$ respectively , we have |
809 |
|
\begin{equation} |
810 |
|
\exp (hX + hY) = \exp (hZ) |
818 |
|
\[ |
819 |
|
[X,Y] = XY - YX . |
820 |
|
\] |
821 |
< |
Applying Baker-Campbell-Hausdorff formula\cite{Varadarajan1974} to |
822 |
< |
Sprang splitting, we can obtain |
821 |
> |
Applying the Baker-Campbell-Hausdorff formula\cite{Varadarajan1974} |
822 |
> |
to the Sprang splitting, we can obtain |
823 |
|
\begin{eqnarray*} |
824 |
|
\exp (h X/2)\exp (h Y)\exp (h X/2) & = & \exp (h X + h Y + h^2 [X,Y]/4 + h^2 [Y,X]/4 \\ |
825 |
|
& & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\ |
826 |
|
& & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3[X,[X,Y]]/24 + \ldots ) |
827 |
|
\end{eqnarray*} |
828 |
< |
Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0\], the dominant local |
828 |
> |
Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0,\] the dominant local |
829 |
|
error of Spring splitting is proportional to $h^3$. The same |
830 |
< |
procedure can be applied to general splitting, of the form |
830 |
> |
procedure can be applied to a general splitting, of the form |
831 |
|
\begin{equation} |
832 |
|
\varphi _{b_m h}^2 \circ \varphi _{a_m h}^1 \circ \varphi _{b_{m - |
833 |
|
1} h}^2 \circ \ldots \circ \varphi _{a_1 h}^1 . |
834 |
|
\end{equation} |
835 |
< |
Careful choice of coefficient $a_1 \ldots b_m$ will lead to higher |
836 |
< |
order method. Yoshida proposed an elegant way to compose higher |
835 |
> |
A careful choice of coefficient $a_1 \ldots b_m$ will lead to higher |
836 |
> |
order methods. Yoshida proposed an elegant way to compose higher |
837 |
|
order methods based on symmetric splitting\cite{Yoshida1990}. Given |
838 |
|
a symmetric second order base method $ \varphi _h^{(2)} $, a |
839 |
|
fourth-order symmetric method can be constructed by composing, |
846 |
|
integrator $ \varphi _h^{(2n + 2)}$ can be composed by |
847 |
|
\begin{equation} |
848 |
|
\varphi _h^{(2n + 2)} = \varphi _{\alpha h}^{(2n)} \circ \varphi |
849 |
< |
_{\beta h}^{(2n)} \circ \varphi _{\alpha h}^{(2n)} |
849 |
> |
_{\beta h}^{(2n)} \circ \varphi _{\alpha h}^{(2n)}, |
850 |
|
\end{equation} |
851 |
< |
, if the weights are chosen as |
851 |
> |
if the weights are chosen as |
852 |
|
\[ |
853 |
|
\alpha = - \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }},\beta = |
854 |
|
\frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }} . |
886 |
|
These three individual steps will be covered in the following |
887 |
|
sections. Sec.~\ref{introSec:initialSystemSettings} deals with the |
888 |
|
initialization of a simulation. Sec.~\ref{introSection:production} |
889 |
< |
will discusses issues in production run. |
889 |
> |
will discusse issues in production run. |
890 |
|
Sec.~\ref{introSection:Analysis} provides the theoretical tools for |
891 |
|
trajectory analysis. |
892 |
|
|
893 |
|
\subsection{\label{introSec:initialSystemSettings}Initialization} |
894 |
|
|
895 |
< |
\subsubsection{Preliminary preparation} |
895 |
> |
\subsubsection{\textbf{Preliminary preparation}} |
896 |
|
|
897 |
|
When selecting the starting structure of a molecule for molecular |
898 |
|
simulation, one may retrieve its Cartesian coordinates from public |
899 |
|
databases, such as RCSB Protein Data Bank \textit{etc}. Although |
900 |
|
thousands of crystal structures of molecules are discovered every |
901 |
|
year, many more remain unknown due to the difficulties of |
902 |
< |
purification and crystallization. Even for the molecule with known |
903 |
< |
structure, some important information is missing. For example, the |
902 |
> |
purification and crystallization. Even for molecules with known |
903 |
> |
structure, some important information is missing. For example, a |
904 |
|
missing hydrogen atom which acts as donor in hydrogen bonding must |
905 |
|
be added. Moreover, in order to include electrostatic interaction, |
906 |
|
one may need to specify the partial charges for individual atoms. |
907 |
|
Under some circumstances, we may even need to prepare the system in |
908 |
< |
a special setup. For instance, when studying transport phenomenon in |
909 |
< |
membrane system, we may prepare the lipids in bilayer structure |
910 |
< |
instead of placing lipids randomly in solvent, since we are not |
911 |
< |
interested in self-aggregation and it takes a long time to happen. |
908 |
> |
a special configuration. For instance, when studying transport |
909 |
> |
phenomenon in membrane systems, we may prepare the lipids in a |
910 |
> |
bilayer structure instead of placing lipids randomly in solvent, |
911 |
> |
since we are not interested in the slow self-aggregation process. |
912 |
|
|
913 |
< |
\subsubsection{Minimization} |
913 |
> |
\subsubsection{\textbf{Minimization}} |
914 |
|
|
915 |
|
It is quite possible that some of molecules in the system from |
916 |
< |
preliminary preparation may be overlapped with each other. This |
917 |
< |
close proximity leads to high potential energy which consequently |
918 |
< |
jeopardizes any molecular dynamics simulations. To remove these |
919 |
< |
steric overlaps, one typically performs energy minimization to find |
920 |
< |
a more reasonable conformation. Several energy minimization methods |
921 |
< |
have been developed to exploit the energy surface and to locate the |
922 |
< |
local minimum. While converging slowly near the minimum, steepest |
923 |
< |
descent method is extremely robust when systems are far from |
924 |
< |
harmonic. Thus, it is often used to refine structure from |
925 |
< |
crystallographic data. Relied on the gradient or hessian, advanced |
926 |
< |
methods like conjugate gradient and Newton-Raphson converge rapidly |
927 |
< |
to a local minimum, while become unstable if the energy surface is |
928 |
< |
far from quadratic. Another factor must be taken into account, when |
916 |
> |
preliminary preparation may be overlapping with each other. This |
917 |
> |
close proximity leads to high initial potential energy which |
918 |
> |
consequently jeopardizes any molecular dynamics simulations. To |
919 |
> |
remove these steric overlaps, one typically performs energy |
920 |
> |
minimization to find a more reasonable conformation. Several energy |
921 |
> |
minimization methods have been developed to exploit the energy |
922 |
> |
surface and to locate the local minimum. While converging slowly |
923 |
> |
near the minimum, steepest descent method is extremely robust when |
924 |
> |
systems are strongly anharmonic. Thus, it is often used to refine |
925 |
> |
structure from crystallographic data. Relied on the gradient or |
926 |
> |
hessian, advanced methods like Newton-Raphson converge rapidly to a |
927 |
> |
local minimum, but become unstable if the energy surface is far from |
928 |
> |
quadratic. Another factor that must be taken into account, when |
929 |
|
choosing energy minimization method, is the size of the system. |
930 |
|
Steepest descent and conjugate gradient can deal with models of any |
931 |
< |
size. Because of the limit of computation power to calculate hessian |
932 |
< |
matrix and insufficient storage capacity to store them, most |
933 |
< |
Newton-Raphson methods can not be used with very large models. |
931 |
> |
size. Because of the limits on computer memory to store the hessian |
932 |
> |
matrix and the computing power needed to diagonalized these |
933 |
> |
matrices, most Newton-Raphson methods can not be used with very |
934 |
> |
large systems. |
935 |
|
|
936 |
< |
\subsubsection{Heating} |
936 |
> |
\subsubsection{\textbf{Heating}} |
937 |
|
|
938 |
|
Typically, Heating is performed by assigning random velocities |
939 |
< |
according to a Gaussian distribution for a temperature. Beginning at |
940 |
< |
a lower temperature and gradually increasing the temperature by |
941 |
< |
assigning greater random velocities, we end up with setting the |
942 |
< |
temperature of the system to a final temperature at which the |
943 |
< |
simulation will be conducted. In heating phase, we should also keep |
944 |
< |
the system from drifting or rotating as a whole. Equivalently, the |
945 |
< |
net linear momentum and angular momentum of the system should be |
946 |
< |
shifted to zero. |
939 |
> |
according to a Maxwell-Boltzman distribution for a desired |
940 |
> |
temperature. Beginning at a lower temperature and gradually |
941 |
> |
increasing the temperature by assigning larger random velocities, we |
942 |
> |
end up with setting the temperature of the system to a final |
943 |
> |
temperature at which the simulation will be conducted. In heating |
944 |
> |
phase, we should also keep the system from drifting or rotating as a |
945 |
> |
whole. To do this, the net linear momentum and angular momentum of |
946 |
> |
the system is shifted to zero after each resampling from the Maxwell |
947 |
> |
-Boltzman distribution. |
948 |
|
|
949 |
< |
\subsubsection{Equilibration} |
949 |
> |
\subsubsection{\textbf{Equilibration}} |
950 |
|
|
951 |
|
The purpose of equilibration is to allow the system to evolve |
952 |
|
spontaneously for a period of time and reach equilibrium. The |
960 |
|
|
961 |
|
\subsection{\label{introSection:production}Production} |
962 |
|
|
963 |
< |
Production run is the most important step of the simulation, in |
963 |
> |
The production run is the most important step of the simulation, in |
964 |
|
which the equilibrated structure is used as a starting point and the |
965 |
|
motions of the molecules are collected for later analysis. In order |
966 |
|
to capture the macroscopic properties of the system, the molecular |
967 |
< |
dynamics simulation must be performed in correct and efficient way. |
967 |
> |
dynamics simulation must be performed by sampling correctly and |
968 |
> |
efficiently from the relevant thermodynamic ensemble. |
969 |
|
|
970 |
|
The most expensive part of a molecular dynamics simulation is the |
971 |
|
calculation of non-bonded forces, such as van der Waals force and |
972 |
|
Coulombic forces \textit{etc}. For a system of $N$ particles, the |
973 |
|
complexity of the algorithm for pair-wise interactions is $O(N^2 )$, |
974 |
|
which making large simulations prohibitive in the absence of any |
975 |
< |
computation saving techniques. |
975 |
> |
algorithmic tricks. |
976 |
|
|
977 |
< |
A natural approach to avoid system size issue is to represent the |
977 |
> |
A natural approach to avoid system size issues is to represent the |
978 |
|
bulk behavior by a finite number of the particles. However, this |
979 |
< |
approach will suffer from the surface effect. To offset this, |
980 |
< |
\textit{Periodic boundary condition} (see Fig.~\ref{introFig:pbc}) |
981 |
< |
is developed to simulate bulk properties with a relatively small |
982 |
< |
number of particles. In this method, the simulation box is |
983 |
< |
replicated throughout space to form an infinite lattice. During the |
984 |
< |
simulation, when a particle moves in the primary cell, its image in |
985 |
< |
other cells move in exactly the same direction with exactly the same |
986 |
< |
orientation. Thus, as a particle leaves the primary cell, one of its |
987 |
< |
images will enter through the opposite face. |
979 |
> |
approach will suffer from the surface effect at the edges of the |
980 |
> |
simulation. To offset this, \textit{Periodic boundary conditions} |
981 |
> |
(see Fig.~\ref{introFig:pbc}) is developed to simulate bulk |
982 |
> |
properties with a relatively small number of particles. In this |
983 |
> |
method, the simulation box is replicated throughout space to form an |
984 |
> |
infinite lattice. During the simulation, when a particle moves in |
985 |
> |
the primary cell, its image in other cells move in exactly the same |
986 |
> |
direction with exactly the same orientation. Thus, as a particle |
987 |
> |
leaves the primary cell, one of its images will enter through the |
988 |
> |
opposite face. |
989 |
|
\begin{figure} |
990 |
|
\centering |
991 |
|
\includegraphics[width=\linewidth]{pbc.eps} |
997 |
|
|
998 |
|
%cutoff and minimum image convention |
999 |
|
Another important technique to improve the efficiency of force |
1000 |
< |
evaluation is to apply cutoff where particles farther than a |
1001 |
< |
predetermined distance, are not included in the calculation |
1000 |
> |
evaluation is to apply spherical cutoff where particles farther than |
1001 |
> |
a predetermined distance are not included in the calculation |
1002 |
|
\cite{Frenkel1996}. The use of a cutoff radius will cause a |
1003 |
|
discontinuity in the potential energy curve. Fortunately, one can |
1004 |
< |
shift the potential to ensure the potential curve go smoothly to |
1005 |
< |
zero at the cutoff radius. Cutoff strategy works pretty well for |
1006 |
< |
Lennard-Jones interaction because of its short range nature. |
1007 |
< |
However, simply truncating the electrostatic interaction with the |
1008 |
< |
use of cutoff has been shown to lead to severe artifacts in |
1009 |
< |
simulations. Ewald summation, in which the slowly conditionally |
1010 |
< |
convergent Coulomb potential is transformed into direct and |
1011 |
< |
reciprocal sums with rapid and absolute convergence, has proved to |
1012 |
< |
minimize the periodicity artifacts in liquid simulations. Taking the |
1013 |
< |
advantages of the fast Fourier transform (FFT) for calculating |
1014 |
< |
discrete Fourier transforms, the particle mesh-based |
1004 |
> |
shift simple radial potential to ensure the potential curve go |
1005 |
> |
smoothly to zero at the cutoff radius. The cutoff strategy works |
1006 |
> |
well for Lennard-Jones interaction because of its short range |
1007 |
> |
nature. However, simply truncating the electrostatic interaction |
1008 |
> |
with the use of cutoffs has been shown to lead to severe artifacts |
1009 |
> |
in simulations. The Ewald summation, in which the slowly decaying |
1010 |
> |
Coulomb potential is transformed into direct and reciprocal sums |
1011 |
> |
with rapid and absolute convergence, has proved to minimize the |
1012 |
> |
periodicity artifacts in liquid simulations. Taking the advantages |
1013 |
> |
of the fast Fourier transform (FFT) for calculating discrete Fourier |
1014 |
> |
transforms, the particle mesh-based |
1015 |
|
methods\cite{Hockney1981,Shimada1993, Luty1994} are accelerated from |
1016 |
< |
$O(N^{3/2})$ to $O(N logN)$. An alternative approach is \emph{fast |
1017 |
< |
multipole method}\cite{Greengard1987, Greengard1994}, which treats |
1018 |
< |
Coulombic interaction exactly at short range, and approximate the |
1019 |
< |
potential at long range through multipolar expansion. In spite of |
1020 |
< |
their wide acceptances at the molecular simulation community, these |
1021 |
< |
two methods are hard to be implemented correctly and efficiently. |
1022 |
< |
Instead, we use a damped and charge-neutralized Coulomb potential |
1023 |
< |
method developed by Wolf and his coworkers\cite{Wolf1999}. The |
1024 |
< |
shifted Coulomb potential for particle $i$ and particle $j$ at |
1025 |
< |
distance $r_{rj}$ is given by: |
1016 |
> |
$O(N^{3/2})$ to $O(N logN)$. An alternative approach is the |
1017 |
> |
\emph{fast multipole method}\cite{Greengard1987, Greengard1994}, |
1018 |
> |
which treats Coulombic interactions exactly at short range, and |
1019 |
> |
approximate the potential at long range through multipolar |
1020 |
> |
expansion. In spite of their wide acceptance at the molecular |
1021 |
> |
simulation community, these two methods are difficult to implement |
1022 |
> |
correctly and efficiently. Instead, we use a damped and |
1023 |
> |
charge-neutralized Coulomb potential method developed by Wolf and |
1024 |
> |
his coworkers\cite{Wolf1999}. The shifted Coulomb potential for |
1025 |
> |
particle $i$ and particle $j$ at distance $r_{rj}$ is given by: |
1026 |
|
\begin{equation} |
1027 |
|
V(r_{ij})= \frac{q_i q_j \textrm{erfc}(\alpha |
1028 |
|
r_{ij})}{r_{ij}}-\lim_{r_{ij}\rightarrow |
1044 |
|
|
1045 |
|
\subsection{\label{introSection:Analysis} Analysis} |
1046 |
|
|
1047 |
< |
Recently, advanced visualization technique are widely applied to |
1047 |
> |
Recently, advanced visualization technique have become applied to |
1048 |
|
monitor the motions of molecules. Although the dynamics of the |
1049 |
|
system can be described qualitatively from animation, quantitative |
1050 |
< |
trajectory analysis are more appreciable. According to the |
1051 |
< |
principles of Statistical Mechanics, |
1052 |
< |
Sec.~\ref{introSection:statisticalMechanics}, one can compute |
1053 |
< |
thermodynamics properties, analyze fluctuations of structural |
1054 |
< |
parameters, and investigate time-dependent processes of the molecule |
1080 |
< |
from the trajectories. |
1050 |
> |
trajectory analysis are more useful. According to the principles of |
1051 |
> |
Statistical Mechanics, Sec.~\ref{introSection:statisticalMechanics}, |
1052 |
> |
one can compute thermodynamic properties, analyze fluctuations of |
1053 |
> |
structural parameters, and investigate time-dependent processes of |
1054 |
> |
the molecule from the trajectories. |
1055 |
|
|
1056 |
< |
\subsubsection{\label{introSection:thermodynamicsProperties}Thermodynamics Properties} |
1056 |
> |
\subsubsection{\label{introSection:thermodynamicsProperties}\textbf{Thermodynamic Properties}} |
1057 |
|
|
1058 |
< |
Thermodynamics properties, which can be expressed in terms of some |
1058 |
> |
Thermodynamic properties, which can be expressed in terms of some |
1059 |
|
function of the coordinates and momenta of all particles in the |
1060 |
|
system, can be directly computed from molecular dynamics. The usual |
1061 |
|
way to measure the pressure is based on virial theorem of Clausius |
1075 |
|
< j} {r{}_{ij} \cdot f_{ij} } } \right\rangle |
1076 |
|
\end{equation} |
1077 |
|
|
1078 |
< |
\subsubsection{\label{introSection:structuralProperties}Structural Properties} |
1078 |
> |
\subsubsection{\label{introSection:structuralProperties}\textbf{Structural Properties}} |
1079 |
|
|
1080 |
|
Structural Properties of a simple fluid can be described by a set of |
1081 |
< |
distribution functions. Among these functions,\emph{pair |
1081 |
> |
distribution functions. Among these functions,the \emph{pair |
1082 |
|
distribution function}, also known as \emph{radial distribution |
1083 |
< |
function}, is of most fundamental importance to liquid-state theory. |
1084 |
< |
Pair distribution function can be gathered by Fourier transforming |
1085 |
< |
raw data from a series of neutron diffraction experiments and |
1086 |
< |
integrating over the surface factor \cite{Powles1973}. The |
1087 |
< |
experiment result can serve as a criterion to justify the |
1088 |
< |
correctness of the theory. Moreover, various equilibrium |
1089 |
< |
thermodynamic and structural properties can also be expressed in |
1090 |
< |
terms of radial distribution function \cite{Allen1987}. |
1083 |
> |
function}, is of most fundamental importance to liquid theory. |
1084 |
> |
Experimentally, pair distribution function can be gathered by |
1085 |
> |
Fourier transforming raw data from a series of neutron diffraction |
1086 |
> |
experiments and integrating over the surface factor |
1087 |
> |
\cite{Powles1973}. The experimental results can serve as a criterion |
1088 |
> |
to justify the correctness of a liquid model. Moreover, various |
1089 |
> |
equilibrium thermodynamic and structural properties can also be |
1090 |
> |
expressed in terms of radial distribution function \cite{Allen1987}. |
1091 |
|
|
1092 |
< |
A pair distribution functions $g(r)$ gives the probability that a |
1092 |
> |
The pair distribution functions $g(r)$ gives the probability that a |
1093 |
|
particle $i$ will be located at a distance $r$ from a another |
1094 |
|
particle $j$ in the system |
1095 |
|
\[ |
1096 |
|
g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j |
1097 |
< |
\ne i} {\delta (r - r_{ij} )} } } \right\rangle. |
1097 |
> |
\ne i} {\delta (r - r_{ij} )} } } \right\rangle = \frac{\rho |
1098 |
> |
(r)}{\rho}. |
1099 |
|
\] |
1100 |
|
Note that the delta function can be replaced by a histogram in |
1101 |
< |
computer simulation. Figure |
1102 |
< |
\ref{introFigure:pairDistributionFunction} shows a typical pair |
1103 |
< |
distribution function for the liquid argon system. The occurrence of |
1129 |
< |
several peaks in the plot of $g(r)$ suggests that it is more likely |
1130 |
< |
to find particles at certain radial values than at others. This is a |
1131 |
< |
result of the attractive interaction at such distances. Because of |
1132 |
< |
the strong repulsive forces at short distance, the probability of |
1133 |
< |
locating particles at distances less than about 2.5{\AA} from each |
1134 |
< |
other is essentially zero. |
1101 |
> |
computer simulation. Peaks in $g(r)$ represent solvent shells, and |
1102 |
> |
the height of these peaks gradually decreases to 1 as the liquid of |
1103 |
> |
large distance approaches the bulk density. |
1104 |
|
|
1136 |
– |
%\begin{figure} |
1137 |
– |
%\centering |
1138 |
– |
%\includegraphics[width=\linewidth]{pdf.eps} |
1139 |
– |
%\caption[Pair distribution function for the liquid argon |
1140 |
– |
%]{Pair distribution function for the liquid argon} |
1141 |
– |
%\label{introFigure:pairDistributionFunction} |
1142 |
– |
%\end{figure} |
1105 |
|
|
1106 |
< |
\subsubsection{\label{introSection:timeDependentProperties}Time-dependent |
1107 |
< |
Properties} |
1106 |
> |
\subsubsection{\label{introSection:timeDependentProperties}\textbf{Time-dependent |
1107 |
> |
Properties}} |
1108 |
|
|
1109 |
|
Time-dependent properties are usually calculated using \emph{time |
1110 |
< |
correlation function}, which correlates random variables $A$ and $B$ |
1111 |
< |
at two different time |
1110 |
> |
correlation functions}, which correlate random variables $A$ and $B$ |
1111 |
> |
at two different times, |
1112 |
|
\begin{equation} |
1113 |
|
C_{AB} (t) = \left\langle {A(t)B(0)} \right\rangle. |
1114 |
|
\label{introEquation:timeCorrelationFunction} |
1115 |
|
\end{equation} |
1116 |
|
If $A$ and $B$ refer to same variable, this kind of correlation |
1117 |
< |
function is called \emph{auto correlation function}. One example of |
1118 |
< |
auto correlation function is velocity auto-correlation function |
1119 |
< |
which is directly related to transport properties of molecular |
1120 |
< |
liquids: |
1117 |
> |
function is called an \emph{autocorrelation function}. One example |
1118 |
> |
of an auto correlation function is the velocity auto-correlation |
1119 |
> |
function which is directly related to transport properties of |
1120 |
> |
molecular liquids: |
1121 |
|
\[ |
1122 |
|
D = \frac{1}{3}\int\limits_0^\infty {\left\langle {v(t) \cdot v(0)} |
1123 |
|
\right\rangle } dt |
1124 |
|
\] |
1125 |
< |
where $D$ is diffusion constant. Unlike velocity autocorrelation |
1126 |
< |
function which is averaging over time origins and over all the |
1127 |
< |
atoms, dipole autocorrelation are calculated for the entire system. |
1128 |
< |
The dipole autocorrelation function is given by: |
1125 |
> |
where $D$ is diffusion constant. Unlike the velocity autocorrelation |
1126 |
> |
function, which is averaging over time origins and over all the |
1127 |
> |
atoms, the dipole autocorrelation functions are calculated for the |
1128 |
> |
entire system. The dipole autocorrelation function is given by: |
1129 |
|
\[ |
1130 |
|
c_{dipole} = \left\langle {u_{tot} (t) \cdot u_{tot} (t)} |
1131 |
|
\right\rangle |
1151 |
|
areas, from engineering, physics, to chemistry. For example, |
1152 |
|
missiles and vehicle are usually modeled by rigid bodies. The |
1153 |
|
movement of the objects in 3D gaming engine or other physics |
1154 |
< |
simulator is governed by the rigid body dynamics. In molecular |
1155 |
< |
simulation, rigid body is used to simplify the model in |
1156 |
< |
protein-protein docking study\cite{Gray2003}. |
1154 |
> |
simulator is governed by rigid body dynamics. In molecular |
1155 |
> |
simulations, rigid bodies are used to simplify protein-protein |
1156 |
> |
docking studies\cite{Gray2003}. |
1157 |
|
|
1158 |
|
It is very important to develop stable and efficient methods to |
1159 |
< |
integrate the equations of motion of orientational degrees of |
1160 |
< |
freedom. Euler angles are the nature choice to describe the |
1161 |
< |
rotational degrees of freedom. However, due to its singularity, the |
1162 |
< |
numerical integration of corresponding equations of motion is very |
1163 |
< |
inefficient and inaccurate. Although an alternative integrator using |
1164 |
< |
different sets of Euler angles can overcome this |
1165 |
< |
difficulty\cite{Barojas1973}, the computational penalty and the lost |
1166 |
< |
of angular momentum conservation still remain. A singularity free |
1167 |
< |
representation utilizing quaternions was developed by Evans in |
1168 |
< |
1977\cite{Evans1977}. Unfortunately, this approach suffer from the |
1169 |
< |
nonseparable Hamiltonian resulted from quaternion representation, |
1170 |
< |
which prevents the symplectic algorithm to be utilized. Another |
1171 |
< |
different approach is to apply holonomic constraints to the atoms |
1172 |
< |
belonging to the rigid body. Each atom moves independently under the |
1173 |
< |
normal forces deriving from potential energy and constraint forces |
1174 |
< |
which are used to guarantee the rigidness. However, due to their |
1175 |
< |
iterative nature, SHAKE and Rattle algorithm converge very slowly |
1176 |
< |
when the number of constraint increases\cite{Ryckaert1977, |
1177 |
< |
Andersen1983}. |
1159 |
> |
integrate the equations of motion for orientational degrees of |
1160 |
> |
freedom. Euler angles are the natural choice to describe the |
1161 |
> |
rotational degrees of freedom. However, due to $\frac {1}{sin |
1162 |
> |
\theta}$ singularities, the numerical integration of corresponding |
1163 |
> |
equations of motion is very inefficient and inaccurate. Although an |
1164 |
> |
alternative integrator using multiple sets of Euler angles can |
1165 |
> |
overcome this difficulty\cite{Barojas1973}, the computational |
1166 |
> |
penalty and the loss of angular momentum conservation still remain. |
1167 |
> |
A singularity-free representation utilizing quaternions was |
1168 |
> |
developed by Evans in 1977\cite{Evans1977}. Unfortunately, this |
1169 |
> |
approach uses a nonseparable Hamiltonian resulting from the |
1170 |
> |
quaternion representation, which prevents the symplectic algorithm |
1171 |
> |
to be utilized. Another different approach is to apply holonomic |
1172 |
> |
constraints to the atoms belonging to the rigid body. Each atom |
1173 |
> |
moves independently under the normal forces deriving from potential |
1174 |
> |
energy and constraint forces which are used to guarantee the |
1175 |
> |
rigidness. However, due to their iterative nature, the SHAKE and |
1176 |
> |
Rattle algorithms also converge very slowly when the number of |
1177 |
> |
constraints increases\cite{Ryckaert1977, Andersen1983}. |
1178 |
|
|
1179 |
< |
The break through in geometric literature suggests that, in order to |
1179 |
> |
A break-through in geometric literature suggests that, in order to |
1180 |
|
develop a long-term integration scheme, one should preserve the |
1181 |
< |
symplectic structure of the flow. Introducing conjugate momentum to |
1182 |
< |
rotation matrix $Q$ and re-formulating Hamiltonian's equation, a |
1183 |
< |
symplectic integrator, RSHAKE\cite{Kol1997}, was proposed to evolve |
1184 |
< |
the Hamiltonian system in a constraint manifold by iteratively |
1185 |
< |
satisfying the orthogonality constraint $Q_T Q = 1$. An alternative |
1186 |
< |
method using quaternion representation was developed by |
1187 |
< |
Omelyan\cite{Omelyan1998}. However, both of these methods are |
1188 |
< |
iterative and inefficient. In this section, we will present a |
1181 |
> |
symplectic structure of the flow. By introducing a conjugate |
1182 |
> |
momentum to the rotation matrix $Q$ and re-formulating Hamiltonian's |
1183 |
> |
equation, a symplectic integrator, RSHAKE\cite{Kol1997}, was |
1184 |
> |
proposed to evolve the Hamiltonian system in a constraint manifold |
1185 |
> |
by iteratively satisfying the orthogonality constraint $Q^T Q = 1$. |
1186 |
> |
An alternative method using the quaternion representation was |
1187 |
> |
developed by Omelyan\cite{Omelyan1998}. However, both of these |
1188 |
> |
methods are iterative and inefficient. In this section, we descibe a |
1189 |
|
symplectic Lie-Poisson integrator for rigid body developed by |
1190 |
|
Dullweber and his coworkers\cite{Dullweber1997} in depth. |
1191 |
|
|
1192 |
< |
\subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body} |
1193 |
< |
The motion of the rigid body is Hamiltonian with the Hamiltonian |
1192 |
> |
\subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Bodies} |
1193 |
> |
The motion of a rigid body is Hamiltonian with the Hamiltonian |
1194 |
|
function |
1195 |
|
\begin{equation} |
1196 |
|
H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) + |
1204 |
|
I_{ii}^{ - 1} = \frac{1}{2}\sum\limits_{i \ne j} {J_{jj}^{ - 1} } |
1205 |
|
\] |
1206 |
|
where $I_{ii}$ is the diagonal element of the inertia tensor. This |
1207 |
< |
constrained Hamiltonian equation subjects to a holonomic constraint, |
1207 |
> |
constrained Hamiltonian equation is subjected to a holonomic |
1208 |
> |
constraint, |
1209 |
|
\begin{equation} |
1210 |
|
Q^T Q = 1, \label{introEquation:orthogonalConstraint} |
1211 |
|
\end{equation} |
1212 |
< |
which is used to ensure rotation matrix's orthogonality. |
1213 |
< |
Differentiating \ref{introEquation:orthogonalConstraint} and using |
1214 |
< |
Equation \ref{introEquation:RBMotionMomentum}, one may obtain, |
1212 |
> |
which is used to ensure rotation matrix's unitarity. Differentiating |
1213 |
> |
\ref{introEquation:orthogonalConstraint} and using Equation |
1214 |
> |
\ref{introEquation:RBMotionMomentum}, one may obtain, |
1215 |
|
\begin{equation} |
1216 |
|
Q^T PJ^{ - 1} + J^{ - 1} P^T Q = 0 . \\ |
1217 |
|
\label{introEquation:RBFirstOrderConstraint} |
1229 |
|
\end{eqnarray} |
1230 |
|
|
1231 |
|
In general, there are two ways to satisfy the holonomic constraints. |
1232 |
< |
We can use constraint force provided by lagrange multiplier on the |
1233 |
< |
normal manifold to keep the motion on constraint space. Or we can |
1234 |
< |
simply evolve the system in constraint manifold. These two methods |
1235 |
< |
are proved to be equivalent. The holonomic constraint and equations |
1236 |
< |
of motions define a constraint manifold for rigid body |
1232 |
> |
We can use a constraint force provided by a Lagrange multiplier on |
1233 |
> |
the normal manifold to keep the motion on constraint space. Or we |
1234 |
> |
can simply evolve the system on the constraint manifold. These two |
1235 |
> |
methods have been proved to be equivalent. The holonomic constraint |
1236 |
> |
and equations of motions define a constraint manifold for rigid |
1237 |
> |
bodies |
1238 |
|
\[ |
1239 |
|
M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1} + J^{ - 1} P^T Q = 0} |
1240 |
|
\right\}. |
1241 |
|
\] |
1242 |
|
|
1243 |
|
Unfortunately, this constraint manifold is not the cotangent bundle |
1244 |
< |
$T_{\star}SO(3)$. However, it turns out that under symplectic |
1244 |
> |
$T^* SO(3)$ which can be consider as a symplectic manifold on Lie |
1245 |
> |
rotation group $SO(3)$. However, it turns out that under symplectic |
1246 |
|
transformation, the cotangent space and the phase space are |
1247 |
< |
diffeomorphic. Introducing |
1247 |
> |
diffeomorphic. By introducing |
1248 |
|
\[ |
1249 |
|
\tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right), |
1250 |
|
\] |
1276 |
|
respectively. |
1277 |
|
|
1278 |
|
As a common choice to describe the rotation dynamics of the rigid |
1279 |
< |
body, angular momentum on body frame $\Pi = Q^t P$ is introduced to |
1280 |
< |
rewrite the equations of motion, |
1279 |
> |
body, the angular momentum on the body fixed frame $\Pi = Q^t P$ is |
1280 |
> |
introduced to rewrite the equations of motion, |
1281 |
|
\begin{equation} |
1282 |
|
\begin{array}{l} |
1283 |
< |
\mathop \Pi \limits^ \bullet = J^{ - 1} \Pi ^T \Pi + Q^T \sum\limits_i {F_i (q,Q)X_i^T } - \Lambda \\ |
1284 |
< |
\mathop Q\limits^{{\rm{ }} \bullet } = Q\Pi {\rm{ }}J^{ - 1} \\ |
1283 |
> |
\dot \Pi = J^{ - 1} \Pi ^T \Pi + Q^T \sum\limits_i {F_i (q,Q)X_i^T } - \Lambda \\ |
1284 |
> |
\dot Q = Q\Pi {\rm{ }}J^{ - 1} \\ |
1285 |
|
\end{array} |
1286 |
|
\label{introEqaution:RBMotionPI} |
1287 |
|
\end{equation} |
1311 |
|
\] |
1312 |
|
Using \ref{introEqaution:RBMotionPI}, one can construct a skew |
1313 |
|
matrix, |
1314 |
< |
\begin{equation} |
1315 |
< |
(\mathop \Pi \limits^ \bullet - \mathop \Pi \limits^ {\bullet ^T} |
1316 |
< |
){\rm{ }} = {\rm{ }}(\Pi - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi + \Pi J^{ |
1317 |
< |
- 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T - X_i F_i (r,Q)^T Q]} - |
1318 |
< |
(\Lambda - \Lambda ^T ) . \label{introEquation:skewMatrixPI} |
1319 |
< |
\end{equation} |
1314 |
> |
|
1315 |
> |
\begin{eqnarry*} |
1316 |
> |
(\dot \Pi - \dot \Pi ^T ){\rm{ }} = {\rm{ }}(\Pi - \Pi ^T ){\rm{ |
1317 |
> |
}}(J^{ - 1} \Pi + \Pi J^{ - 1} ) + \sum\limits_i {[Q^T F_i |
1318 |
> |
(r,Q)X_i^T - X_i F_i (r,Q)^T Q]} - (\Lambda - \Lambda ^T ). |
1319 |
> |
\label{introEquation:skewMatrixPI} |
1320 |
> |
\end{eqnarray*} |
1321 |
> |
|
1322 |
|
Since $\Lambda$ is symmetric, the last term of Equation |
1323 |
|
\ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange |
1324 |
|
multiplier $\Lambda$ is absent from the equations of motion. This |
1325 |
< |
unique property eliminate the requirement of iterations which can |
1325 |
> |
unique property eliminates the requirement of iterations which can |
1326 |
|
not be avoided in other methods\cite{Kol1997, Omelyan1998}. |
1327 |
|
|
1328 |
< |
Applying hat-map isomorphism, we obtain the equation of motion for |
1329 |
< |
angular momentum on body frame |
1328 |
> |
Applying the hat-map isomorphism, we obtain the equation of motion |
1329 |
> |
for angular momentum on body frame |
1330 |
|
\begin{equation} |
1331 |
|
\dot \pi = \pi \times I^{ - 1} \pi + \sum\limits_i {\left( {Q^T |
1332 |
|
F_i (r,Q)} \right) \times X_i }. |
1341 |
|
\subsection{\label{introSection:SymplecticFreeRB}Symplectic |
1342 |
|
Lie-Poisson Integrator for Free Rigid Body} |
1343 |
|
|
1344 |
< |
If there is not external forces exerted on the rigid body, the only |
1345 |
< |
contribution to the rotational is from the kinetic potential (the |
1346 |
< |
first term of \ref{introEquation:bodyAngularMotion}). The free rigid |
1347 |
< |
body is an example of Lie-Poisson system with Hamiltonian function |
1344 |
> |
If there are no external forces exerted on the rigid body, the only |
1345 |
> |
contribution to the rotational motion is from the kinetic energy |
1346 |
> |
(the first term of \ref{introEquation:bodyAngularMotion}). The free |
1347 |
> |
rigid body is an example of a Lie-Poisson system with Hamiltonian |
1348 |
> |
function |
1349 |
|
\begin{equation} |
1350 |
|
T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 ) |
1351 |
|
\label{introEquation:rotationalKineticRB} |
1392 |
|
\end{array}} \right),\theta _1 = \frac{{\pi _1 }}{{I_1 }}\Delta t. |
1393 |
|
\] |
1394 |
|
To reduce the cost of computing expensive functions in $e^{\Delta |
1395 |
< |
tR_1 }$, we can use Cayley transformation, |
1395 |
> |
tR_1 }$, we can use Cayley transformation to obtain a single-aixs |
1396 |
> |
propagator, |
1397 |
|
\[ |
1398 |
|
e^{\Delta tR_1 } \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1 |
1399 |
|
) |
1400 |
|
\] |
1401 |
|
The flow maps for $T_2^r$ and $T_3^r$ can be found in the same |
1402 |
< |
manner. |
1403 |
< |
|
1435 |
< |
In order to construct a second-order symplectic method, we split the |
1436 |
< |
angular kinetic Hamiltonian function can into five terms |
1402 |
> |
manner. In order to construct a second-order symplectic method, we |
1403 |
> |
split the angular kinetic Hamiltonian function can into five terms |
1404 |
|
\[ |
1405 |
|
T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2 |
1406 |
|
) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r |
1407 |
< |
(\pi _1 ) |
1408 |
< |
\]. |
1409 |
< |
Concatenating flows corresponding to these five terms, we can obtain |
1410 |
< |
an symplectic integrator, |
1407 |
> |
(\pi _1 ). |
1408 |
> |
\] |
1409 |
> |
By concatenating the propagators corresponding to these five terms, |
1410 |
> |
we can obtain an symplectic integrator, |
1411 |
|
\[ |
1412 |
|
\varphi _{\Delta t,T^r } = \varphi _{\Delta t/2,\pi _1 } \circ |
1413 |
|
\varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t,\pi _3 } |
1434 |
|
\] |
1435 |
|
Thus $ [\nabla _\pi F(\pi )]^T J(\pi ) = - S'(\frac{{\parallel \pi |
1436 |
|
\parallel ^2 }}{2})\pi \times \pi = 0 $. This explicit |
1437 |
< |
Lie-Poisson integrator is found to be extremely efficient and stable |
1438 |
< |
which can be explained by the fact the small angle approximation is |
1439 |
< |
used and the norm of the angular momentum is conserved. |
1437 |
> |
Lie-Poisson integrator is found to be both extremely efficient and |
1438 |
> |
stable. These properties can be explained by the fact the small |
1439 |
> |
angle approximation is used and the norm of the angular momentum is |
1440 |
> |
conserved. |
1441 |
|
|
1442 |
|
\subsection{\label{introSection:RBHamiltonianSplitting} Hamiltonian |
1443 |
|
Splitting for Rigid Body} |
1464 |
|
\end{tabular} |
1465 |
|
\end{center} |
1466 |
|
\end{table} |
1467 |
< |
A second-order symplectic method is now obtained by the |
1468 |
< |
composition of the flow maps, |
1467 |
> |
A second-order symplectic method is now obtained by the composition |
1468 |
> |
of the position and velocity propagators, |
1469 |
|
\[ |
1470 |
|
\varphi _{\Delta t} = \varphi _{\Delta t/2,V} \circ \varphi |
1471 |
|
_{\Delta t,T} \circ \varphi _{\Delta t/2,V}. |
1472 |
|
\] |
1473 |
|
Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two |
1474 |
< |
sub-flows which corresponding to force and torque respectively, |
1474 |
> |
sub-propagators which corresponding to force and torque |
1475 |
> |
respectively, |
1476 |
|
\[ |
1477 |
|
\varphi _{\Delta t/2,V} = \varphi _{\Delta t/2,F} \circ \varphi |
1478 |
|
_{\Delta t/2,\tau }. |
1479 |
|
\] |
1480 |
|
Since the associated operators of $\varphi _{\Delta t/2,F} $ and |
1481 |
< |
$\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition |
1482 |
< |
order inside $\varphi _{\Delta t/2,V}$ does not matter. |
1483 |
< |
|
1484 |
< |
Furthermore, kinetic potential can be separated to translational |
1516 |
< |
kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$, |
1481 |
> |
$\circ \varphi _{\Delta t/2,\tau }$ commute, the composition order |
1482 |
> |
inside $\varphi _{\Delta t/2,V}$ does not matter. Furthermore, the |
1483 |
> |
kinetic energy can be separated to translational kinetic term, $T^t |
1484 |
> |
(p)$, and rotational kinetic term, $T^r (\pi )$, |
1485 |
|
\begin{equation} |
1486 |
|
T(p,\pi ) =T^t (p) + T^r (\pi ). |
1487 |
|
\end{equation} |
1488 |
|
where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is |
1489 |
|
defined by \ref{introEquation:rotationalKineticRB}. Therefore, the |
1490 |
< |
corresponding flow maps are given by |
1490 |
> |
corresponding propagators are given by |
1491 |
|
\[ |
1492 |
|
\varphi _{\Delta t,T} = \varphi _{\Delta t,T^t } \circ \varphi |
1493 |
|
_{\Delta t,T^r }. |
1494 |
|
\] |
1495 |
< |
Finally, we obtain the overall symplectic flow maps for free moving |
1496 |
< |
rigid body |
1495 |
> |
Finally, we obtain the overall symplectic propagators for freely |
1496 |
> |
moving rigid bodies |
1497 |
|
\begin{equation} |
1498 |
|
\begin{array}{c} |
1499 |
|
\varphi _{\Delta t} = \varphi _{\Delta t/2,F} \circ \varphi _{\Delta t/2,\tau } \\ |
1507 |
|
As an alternative to newtonian dynamics, Langevin dynamics, which |
1508 |
|
mimics a simple heat bath with stochastic and dissipative forces, |
1509 |
|
has been applied in a variety of studies. This section will review |
1510 |
< |
the theory of Langevin dynamics simulation. A brief derivation of |
1511 |
< |
generalized Langevin equation will be given first. Follow that, we |
1512 |
< |
will discuss the physical meaning of the terms appearing in the |
1513 |
< |
equation as well as the calculation of friction tensor from |
1514 |
< |
hydrodynamics theory. |
1510 |
> |
the theory of Langevin dynamics. A brief derivation of generalized |
1511 |
> |
Langevin equation will be given first. Following that, we will |
1512 |
> |
discuss the physical meaning of the terms appearing in the equation |
1513 |
> |
as well as the calculation of friction tensor from hydrodynamics |
1514 |
> |
theory. |
1515 |
|
|
1516 |
|
\subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation} |
1517 |
|
|
1518 |
< |
Harmonic bath model, in which an effective set of harmonic |
1518 |
> |
A harmonic bath model, in which an effective set of harmonic |
1519 |
|
oscillators are used to mimic the effect of a linearly responding |
1520 |
|
environment, has been widely used in quantum chemistry and |
1521 |
|
statistical mechanics. One of the successful applications of |
1522 |
< |
Harmonic bath model is the derivation of Deriving Generalized |
1523 |
< |
Langevin Dynamics. Lets consider a system, in which the degree of |
1522 |
> |
Harmonic bath model is the derivation of the Generalized Langevin |
1523 |
> |
Dynamics (GLE). Lets consider a system, in which the degree of |
1524 |
|
freedom $x$ is assumed to couple to the bath linearly, giving a |
1525 |
|
Hamiltonian of the form |
1526 |
|
\begin{equation} |
1527 |
|
H = \frac{{p^2 }}{{2m}} + U(x) + H_B + \Delta U(x,x_1 , \ldots x_N) |
1528 |
|
\label{introEquation:bathGLE}. |
1529 |
|
\end{equation} |
1530 |
< |
Here $p$ is a momentum conjugate to $q$, $m$ is the mass associated |
1531 |
< |
with this degree of freedom, $H_B$ is harmonic bath Hamiltonian, |
1530 |
> |
Here $p$ is a momentum conjugate to $x$, $m$ is the mass associated |
1531 |
> |
with this degree of freedom, $H_B$ is a harmonic bath Hamiltonian, |
1532 |
|
\[ |
1533 |
|
H_B = \sum\limits_{\alpha = 1}^N {\left\{ {\frac{{p_\alpha ^2 |
1534 |
|
}}{{2m_\alpha }} + \frac{1}{2}m_\alpha \omega _\alpha ^2 } |
1536 |
|
\] |
1537 |
|
where the index $\alpha$ runs over all the bath degrees of freedom, |
1538 |
|
$\omega _\alpha$ are the harmonic bath frequencies, $m_\alpha$ are |
1539 |
< |
the harmonic bath masses, and $\Delta U$ is bilinear system-bath |
1539 |
> |
the harmonic bath masses, and $\Delta U$ is a bilinear system-bath |
1540 |
|
coupling, |
1541 |
|
\[ |
1542 |
|
\Delta U = - \sum\limits_{\alpha = 1}^N {g_\alpha x_\alpha x} |
1543 |
|
\] |
1544 |
< |
where $g_\alpha$ are the coupling constants between the bath and the |
1545 |
< |
coordinate $x$. Introducing |
1544 |
> |
where $g_\alpha$ are the coupling constants between the bath |
1545 |
> |
coordinates ($x_ \alpha$) and the system coordinate ($x$). |
1546 |
> |
Introducing |
1547 |
|
\[ |
1548 |
|
W(x) = U(x) - \sum\limits_{\alpha = 1}^N {\frac{{g_\alpha ^2 |
1549 |
|
}}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1558 |
|
\] |
1559 |
|
Since the first two terms of the new Hamiltonian depend only on the |
1560 |
|
system coordinates, we can get the equations of motion for |
1561 |
< |
Generalized Langevin Dynamics by Hamilton's equations |
1593 |
< |
\ref{introEquation:motionHamiltonianCoordinate, |
1594 |
< |
introEquation:motionHamiltonianMomentum}, |
1561 |
> |
Generalized Langevin Dynamics by Hamilton's equations, |
1562 |
|
\begin{equation} |
1563 |
|
m\ddot x = - \frac{{\partial W(x)}}{{\partial x}} - |
1564 |
|
\sum\limits_{\alpha = 1}^N {g_\alpha \left( {x_\alpha - |
1575 |
|
In order to derive an equation for $x$, the dynamics of the bath |
1576 |
|
variables $x_\alpha$ must be solved exactly first. As an integral |
1577 |
|
transform which is particularly useful in solving linear ordinary |
1578 |
< |
differential equations, Laplace transform is the appropriate tool to |
1579 |
< |
solve this problem. The basic idea is to transform the difficult |
1578 |
> |
differential equations,the Laplace transform is the appropriate tool |
1579 |
> |
to solve this problem. The basic idea is to transform the difficult |
1580 |
|
differential equations into simple algebra problems which can be |
1581 |
< |
solved easily. Then applying inverse Laplace transform, also known |
1582 |
< |
as the Bromwich integral, we can retrieve the solutions of the |
1581 |
> |
solved easily. Then, by applying the inverse Laplace transform, also |
1582 |
> |
known as the Bromwich integral, we can retrieve the solutions of the |
1583 |
|
original problems. |
1584 |
|
|
1585 |
|
Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace |
1599 |
|
\end{eqnarray*} |
1600 |
|
|
1601 |
|
|
1602 |
< |
Applying Laplace transform to the bath coordinates, we obtain |
1602 |
> |
Applying the Laplace transform to the bath coordinates, we obtain |
1603 |
|
\begin{eqnarray*} |
1604 |
|
p^2 L(x_\alpha ) - px_\alpha (0) - \dot x_\alpha (0) & = & - \omega _\alpha ^2 L(x_\alpha ) + \frac{{g_\alpha }}{{\omega _\alpha }}L(x) \\ |
1605 |
|
L(x_\alpha ) & = & \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }} \\ |
1663 |
|
\end{equation} |
1664 |
|
which is known as the \emph{generalized Langevin equation}. |
1665 |
|
|
1666 |
< |
\subsubsection{\label{introSection:randomForceDynamicFrictionKernel}Random Force and Dynamic Friction Kernel} |
1666 |
> |
\subsubsection{\label{introSection:randomForceDynamicFrictionKernel}\textbf{Random Force and Dynamic Friction Kernel}} |
1667 |
|
|
1668 |
|
One may notice that $R(t)$ depends only on initial conditions, which |
1669 |
|
implies it is completely deterministic within the context of a |
1676 |
|
\end{array} |
1677 |
|
\] |
1678 |
|
This property is what we expect from a truly random process. As long |
1679 |
< |
as the model, which is gaussian distribution in general, chosen for |
1680 |
< |
$R(t)$ is a truly random process, the stochastic nature of the GLE |
1714 |
< |
still remains. |
1679 |
> |
as the model chosen for $R(t)$ was a gaussian distribution in |
1680 |
> |
general, the stochastic nature of the GLE still remains. |
1681 |
|
|
1682 |
|
%dynamic friction kernel |
1683 |
|
The convolution integral |
1698 |
|
m\ddot x = - \frac{\partial }{{\partial x}}\left( {W(x) + |
1699 |
|
\frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t), |
1700 |
|
\] |
1701 |
< |
which can be used to describe dynamic caging effect. The other |
1702 |
< |
extreme is the bath that responds infinitely quickly to motions in |
1703 |
< |
the system. Thus, $\xi (t)$ can be taken as a $delta$ function in |
1704 |
< |
time: |
1701 |
> |
which can be used to describe the effect of dynamic caging in |
1702 |
> |
viscous solvents. The other extreme is the bath that responds |
1703 |
> |
infinitely quickly to motions in the system. Thus, $\xi (t)$ can be |
1704 |
> |
taken as a $delta$ function in time: |
1705 |
|
\[ |
1706 |
|
\xi (t) = 2\xi _0 \delta (t) |
1707 |
|
\] |
1717 |
|
\end{equation} |
1718 |
|
which is known as the Langevin equation. The static friction |
1719 |
|
coefficient $\xi _0$ can either be calculated from spectral density |
1720 |
< |
or be determined by Stokes' law for regular shaped particles.A |
1720 |
> |
or be determined by Stokes' law for regular shaped particles. A |
1721 |
|
briefly review on calculating friction tensor for arbitrary shaped |
1722 |
|
particles is given in Sec.~\ref{introSection:frictionTensor}. |
1723 |
|
|
1724 |
< |
\subsubsection{\label{introSection:secondFluctuationDissipation}The Second Fluctuation Dissipation Theorem} |
1724 |
> |
\subsubsection{\label{introSection:secondFluctuationDissipation}\textbf{The Second Fluctuation Dissipation Theorem}} |
1725 |
|
|
1726 |
|
Defining a new set of coordinates, |
1727 |
|
\[ |
1750 |
|
\end{equation} |
1751 |
|
In effect, it acts as a constraint on the possible ways in which one |
1752 |
|
can model the random force and friction kernel. |
1787 |
– |
|
1788 |
– |
\subsection{\label{introSection:frictionTensor} Friction Tensor} |
1789 |
– |
Theoretically, the friction kernel can be determined using velocity |
1790 |
– |
autocorrelation function. However, this approach become impractical |
1791 |
– |
when the system become more and more complicate. Instead, various |
1792 |
– |
approaches based on hydrodynamics have been developed to calculate |
1793 |
– |
the friction coefficients. The friction effect is isotropic in |
1794 |
– |
Equation, $\zeta$ can be taken as a scalar. In general, friction |
1795 |
– |
tensor $\Xi$ is a $6\times 6$ matrix given by |
1796 |
– |
\[ |
1797 |
– |
\Xi = \left( {\begin{array}{*{20}c} |
1798 |
– |
{\Xi _{}^{tt} } & {\Xi _{}^{rt} } \\ |
1799 |
– |
{\Xi _{}^{tr} } & {\Xi _{}^{rr} } \\ |
1800 |
– |
\end{array}} \right). |
1801 |
– |
\] |
1802 |
– |
Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction |
1803 |
– |
tensor and rotational resistance (friction) tensor respectively, |
1804 |
– |
while ${\Xi^{tr} }$ is translation-rotation coupling tensor and $ |
1805 |
– |
{\Xi^{rt} }$ is rotation-translation coupling tensor. When a |
1806 |
– |
particle moves in a fluid, it may experience friction force or |
1807 |
– |
torque along the opposite direction of the velocity or angular |
1808 |
– |
velocity, |
1809 |
– |
\[ |
1810 |
– |
\left( \begin{array}{l} |
1811 |
– |
F_R \\ |
1812 |
– |
\tau _R \\ |
1813 |
– |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
1814 |
– |
{\Xi ^{tt} } & {\Xi ^{rt} } \\ |
1815 |
– |
{\Xi ^{tr} } & {\Xi ^{rr} } \\ |
1816 |
– |
\end{array}} \right)\left( \begin{array}{l} |
1817 |
– |
v \\ |
1818 |
– |
w \\ |
1819 |
– |
\end{array} \right) |
1820 |
– |
\] |
1821 |
– |
where $F_r$ is the friction force and $\tau _R$ is the friction |
1822 |
– |
toque. |
1823 |
– |
|
1824 |
– |
\subsubsection{\label{introSection:resistanceTensorRegular}The Resistance Tensor for Regular Shape} |
1825 |
– |
|
1826 |
– |
For a spherical particle, the translational and rotational friction |
1827 |
– |
constant can be calculated from Stoke's law, |
1828 |
– |
\[ |
1829 |
– |
\Xi ^{tt} = \left( {\begin{array}{*{20}c} |
1830 |
– |
{6\pi \eta R} & 0 & 0 \\ |
1831 |
– |
0 & {6\pi \eta R} & 0 \\ |
1832 |
– |
0 & 0 & {6\pi \eta R} \\ |
1833 |
– |
\end{array}} \right) |
1834 |
– |
\] |
1835 |
– |
and |
1836 |
– |
\[ |
1837 |
– |
\Xi ^{rr} = \left( {\begin{array}{*{20}c} |
1838 |
– |
{8\pi \eta R^3 } & 0 & 0 \\ |
1839 |
– |
0 & {8\pi \eta R^3 } & 0 \\ |
1840 |
– |
0 & 0 & {8\pi \eta R^3 } \\ |
1841 |
– |
\end{array}} \right) |
1842 |
– |
\] |
1843 |
– |
where $\eta$ is the viscosity of the solvent and $R$ is the |
1844 |
– |
hydrodynamics radius. |
1845 |
– |
|
1846 |
– |
Other non-spherical shape, such as cylinder and ellipsoid |
1847 |
– |
\textit{etc}, are widely used as reference for developing new |
1848 |
– |
hydrodynamics theory, because their properties can be calculated |
1849 |
– |
exactly. In 1936, Perrin extended Stokes's law to general ellipsoid, |
1850 |
– |
also called a triaxial ellipsoid, which is given in Cartesian |
1851 |
– |
coordinates by\cite{Perrin1934, Perrin1936} |
1852 |
– |
\[ |
1853 |
– |
\frac{{x^2 }}{{a^2 }} + \frac{{y^2 }}{{b^2 }} + \frac{{z^2 }}{{c^2 |
1854 |
– |
}} = 1 |
1855 |
– |
\] |
1856 |
– |
where the semi-axes are of lengths $a$, $b$, and $c$. Unfortunately, |
1857 |
– |
due to the complexity of the elliptic integral, only the ellipsoid |
1858 |
– |
with the restriction of two axes having to be equal, \textit{i.e.} |
1859 |
– |
prolate($ a \ge b = c$) and oblate ($ a < b = c $), can be solved |
1860 |
– |
exactly. Introducing an elliptic integral parameter $S$ for prolate, |
1861 |
– |
\[ |
1862 |
– |
S = \frac{2}{{\sqrt {a^2 - b^2 } }}\ln \frac{{a + \sqrt {a^2 - b^2 |
1863 |
– |
} }}{b}, |
1864 |
– |
\] |
1865 |
– |
and oblate, |
1866 |
– |
\[ |
1867 |
– |
S = \frac{2}{{\sqrt {b^2 - a^2 } }}arctg\frac{{\sqrt {b^2 - a^2 } |
1868 |
– |
}}{a} |
1869 |
– |
\], |
1870 |
– |
one can write down the translational and rotational resistance |
1871 |
– |
tensors |
1872 |
– |
\[ |
1873 |
– |
\begin{array}{l} |
1874 |
– |
\Xi _a^{tt} = 16\pi \eta \frac{{a^2 - b^2 }}{{(2a^2 - b^2 )S - 2a}} \\ |
1875 |
– |
\Xi _b^{tt} = \Xi _c^{tt} = 32\pi \eta \frac{{a^2 - b^2 }}{{(2a^2 - 3b^2 )S + 2a}} \\ |
1876 |
– |
\end{array}, |
1877 |
– |
\] |
1878 |
– |
and |
1879 |
– |
\[ |
1880 |
– |
\begin{array}{l} |
1881 |
– |
\Xi _a^{rr} = \frac{{32\pi }}{3}\eta \frac{{(a^2 - b^2 )b^2 }}{{2a - b^2 S}} \\ |
1882 |
– |
\Xi _b^{rr} = \Xi _c^{rr} = \frac{{32\pi }}{3}\eta \frac{{(a^4 - b^4 )}}{{(2a^2 - b^2 )S - 2a}} \\ |
1883 |
– |
\end{array}. |
1884 |
– |
\] |
1885 |
– |
|
1886 |
– |
\subsubsection{\label{introSection:resistanceTensorRegularArbitrary}The Resistance Tensor for Arbitrary Shape} |
1887 |
– |
|
1888 |
– |
Unlike spherical and other regular shaped molecules, there is not |
1889 |
– |
analytical solution for friction tensor of any arbitrary shaped |
1890 |
– |
rigid molecules. The ellipsoid of revolution model and general |
1891 |
– |
triaxial ellipsoid model have been used to approximate the |
1892 |
– |
hydrodynamic properties of rigid bodies. However, since the mapping |
1893 |
– |
from all possible ellipsoidal space, $r$-space, to all possible |
1894 |
– |
combination of rotational diffusion coefficients, $D$-space is not |
1895 |
– |
unique\cite{Wegener1979} as well as the intrinsic coupling between |
1896 |
– |
translational and rotational motion of rigid body, general ellipsoid |
1897 |
– |
is not always suitable for modeling arbitrarily shaped rigid |
1898 |
– |
molecule. A number of studies have been devoted to determine the |
1899 |
– |
friction tensor for irregularly shaped rigid bodies using more |
1900 |
– |
advanced method where the molecule of interest was modeled by |
1901 |
– |
combinations of spheres(beads)\cite{Carrasco1999} and the |
1902 |
– |
hydrodynamics properties of the molecule can be calculated using the |
1903 |
– |
hydrodynamic interaction tensor. Let us consider a rigid assembly of |
1904 |
– |
$N$ beads immersed in a continuous medium. Due to hydrodynamics |
1905 |
– |
interaction, the ``net'' velocity of $i$th bead, $v'_i$ is different |
1906 |
– |
than its unperturbed velocity $v_i$, |
1907 |
– |
\[ |
1908 |
– |
v'_i = v_i - \sum\limits_{j \ne i} {T_{ij} F_j } |
1909 |
– |
\] |
1910 |
– |
where $F_i$ is the frictional force, and $T_{ij}$ is the |
1911 |
– |
hydrodynamic interaction tensor. The friction force of $i$th bead is |
1912 |
– |
proportional to its ``net'' velocity |
1913 |
– |
\begin{equation} |
1914 |
– |
F_i = \zeta _i v_i - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }. |
1915 |
– |
\label{introEquation:tensorExpression} |
1916 |
– |
\end{equation} |
1917 |
– |
This equation is the basis for deriving the hydrodynamic tensor. In |
1918 |
– |
1930, Oseen and Burgers gave a simple solution to Equation |
1919 |
– |
\ref{introEquation:tensorExpression} |
1920 |
– |
\begin{equation} |
1921 |
– |
T_{ij} = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij} |
1922 |
– |
R_{ij}^T }}{{R_{ij}^2 }}} \right). |
1923 |
– |
\label{introEquation:oseenTensor} |
1924 |
– |
\end{equation} |
1925 |
– |
Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$. |
1926 |
– |
A second order expression for element of different size was |
1927 |
– |
introduced by Rotne and Prager\cite{Rotne1969} and improved by |
1928 |
– |
Garc\'{i}a de la Torre and Bloomfield\cite{Torre1977}, |
1929 |
– |
\begin{equation} |
1930 |
– |
T_{ij} = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I + |
1931 |
– |
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma |
1932 |
– |
_i^2 + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} - |
1933 |
– |
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right]. |
1934 |
– |
\label{introEquation:RPTensorNonOverlapped} |
1935 |
– |
\end{equation} |
1936 |
– |
Both of the Equation \ref{introEquation:oseenTensor} and Equation |
1937 |
– |
\ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij} |
1938 |
– |
\ge \sigma _i + \sigma _j$. An alternative expression for |
1939 |
– |
overlapping beads with the same radius, $\sigma$, is given by |
1940 |
– |
\begin{equation} |
1941 |
– |
T_{ij} = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 - |
1942 |
– |
\frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I + |
1943 |
– |
\frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right] |
1944 |
– |
\label{introEquation:RPTensorOverlapped} |
1945 |
– |
\end{equation} |
1946 |
– |
|
1947 |
– |
To calculate the resistance tensor at an arbitrary origin $O$, we |
1948 |
– |
construct a $3N \times 3N$ matrix consisting of $N \times N$ |
1949 |
– |
$B_{ij}$ blocks |
1950 |
– |
\begin{equation} |
1951 |
– |
B = \left( {\begin{array}{*{20}c} |
1952 |
– |
{B_{11} } & \ldots & {B_{1N} } \\ |
1953 |
– |
\vdots & \ddots & \vdots \\ |
1954 |
– |
{B_{N1} } & \cdots & {B_{NN} } \\ |
1955 |
– |
\end{array}} \right), |
1956 |
– |
\end{equation} |
1957 |
– |
where $B_{ij}$ is given by |
1958 |
– |
\[ |
1959 |
– |
B_{ij} = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij} |
1960 |
– |
)T_{ij} |
1961 |
– |
\] |
1962 |
– |
where $\delta _{ij}$ is Kronecker delta function. Inverting matrix |
1963 |
– |
$B$, we obtain |
1964 |
– |
|
1965 |
– |
\[ |
1966 |
– |
C = B^{ - 1} = \left( {\begin{array}{*{20}c} |
1967 |
– |
{C_{11} } & \ldots & {C_{1N} } \\ |
1968 |
– |
\vdots & \ddots & \vdots \\ |
1969 |
– |
{C_{N1} } & \cdots & {C_{NN} } \\ |
1970 |
– |
\end{array}} \right) |
1971 |
– |
\] |
1972 |
– |
, which can be partitioned into $N \times N$ $3 \times 3$ block |
1973 |
– |
$C_{ij}$. With the help of $C_{ij}$ and skew matrix $U_i$ |
1974 |
– |
\[ |
1975 |
– |
U_i = \left( {\begin{array}{*{20}c} |
1976 |
– |
0 & { - z_i } & {y_i } \\ |
1977 |
– |
{z_i } & 0 & { - x_i } \\ |
1978 |
– |
{ - y_i } & {x_i } & 0 \\ |
1979 |
– |
\end{array}} \right) |
1980 |
– |
\] |
1981 |
– |
where $x_i$, $y_i$, $z_i$ are the components of the vector joining |
1982 |
– |
bead $i$ and origin $O$. Hence, the elements of resistance tensor at |
1983 |
– |
arbitrary origin $O$ can be written as |
1984 |
– |
\begin{equation} |
1985 |
– |
\begin{array}{l} |
1986 |
– |
\Xi _{}^{tt} = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\ |
1987 |
– |
\Xi _{}^{tr} = \Xi _{}^{rt} = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\ |
1988 |
– |
\Xi _{}^{rr} = - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j \\ |
1989 |
– |
\end{array} |
1990 |
– |
\label{introEquation:ResistanceTensorArbitraryOrigin} |
1991 |
– |
\end{equation} |
1992 |
– |
|
1993 |
– |
The resistance tensor depends on the origin to which they refer. The |
1994 |
– |
proper location for applying friction force is the center of |
1995 |
– |
resistance (reaction), at which the trace of rotational resistance |
1996 |
– |
tensor, $ \Xi ^{rr}$ reaches minimum. Mathematically, the center of |
1997 |
– |
resistance is defined as an unique point of the rigid body at which |
1998 |
– |
the translation-rotation coupling tensor are symmetric, |
1999 |
– |
\begin{equation} |
2000 |
– |
\Xi^{tr} = \left( {\Xi^{tr} } \right)^T |
2001 |
– |
\label{introEquation:definitionCR} |
2002 |
– |
\end{equation} |
2003 |
– |
Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin}, |
2004 |
– |
we can easily find out that the translational resistance tensor is |
2005 |
– |
origin independent, while the rotational resistance tensor and |
2006 |
– |
translation-rotation coupling resistance tensor depend on the |
2007 |
– |
origin. Given resistance tensor at an arbitrary origin $O$, and a |
2008 |
– |
vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can |
2009 |
– |
obtain the resistance tensor at $P$ by |
2010 |
– |
\begin{equation} |
2011 |
– |
\begin{array}{l} |
2012 |
– |
\Xi _P^{tt} = \Xi _O^{tt} \\ |
2013 |
– |
\Xi _P^{tr} = \Xi _P^{rt} = \Xi _O^{tr} - U_{OP} \Xi _O^{tt} \\ |
2014 |
– |
\Xi _P^{rr} = \Xi _O^{rr} - U_{OP} \Xi _O^{tt} U_{OP} + \Xi _O^{tr} U_{OP} - U_{OP} \Xi _O^{{tr} ^{^T }} \\ |
2015 |
– |
\end{array} |
2016 |
– |
\label{introEquation:resistanceTensorTransformation} |
2017 |
– |
\end{equation} |
2018 |
– |
where |
2019 |
– |
\[ |
2020 |
– |
U_{OP} = \left( {\begin{array}{*{20}c} |
2021 |
– |
0 & { - z_{OP} } & {y_{OP} } \\ |
2022 |
– |
{z_i } & 0 & { - x_{OP} } \\ |
2023 |
– |
{ - y_{OP} } & {x_{OP} } & 0 \\ |
2024 |
– |
\end{array}} \right) |
2025 |
– |
\] |
2026 |
– |
Using Equations \ref{introEquation:definitionCR} and |
2027 |
– |
\ref{introEquation:resistanceTensorTransformation}, one can locate |
2028 |
– |
the position of center of resistance, |
2029 |
– |
\begin{eqnarray*} |
2030 |
– |
\left( \begin{array}{l} |
2031 |
– |
x_{OR} \\ |
2032 |
– |
y_{OR} \\ |
2033 |
– |
z_{OR} \\ |
2034 |
– |
\end{array} \right) & = &\left( {\begin{array}{*{20}c} |
2035 |
– |
{(\Xi _O^{rr} )_{yy} + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} } \\ |
2036 |
– |
{ - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz} + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} } \\ |
2037 |
– |
{ - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx} + (\Xi _O^{rr} )_{yy} } \\ |
2038 |
– |
\end{array}} \right)^{ - 1} \\ |
2039 |
– |
& & \left( \begin{array}{l} |
2040 |
– |
(\Xi _O^{tr} )_{yz} - (\Xi _O^{tr} )_{zy} \\ |
2041 |
– |
(\Xi _O^{tr} )_{zx} - (\Xi _O^{tr} )_{xz} \\ |
2042 |
– |
(\Xi _O^{tr} )_{xy} - (\Xi _O^{tr} )_{yx} \\ |
2043 |
– |
\end{array} \right) \\ |
2044 |
– |
\end{eqnarray*} |
2045 |
– |
|
2046 |
– |
|
2047 |
– |
|
2048 |
– |
where $x_OR$, $y_OR$, $z_OR$ are the components of the vector |
2049 |
– |
joining center of resistance $R$ and origin $O$. |