31 |
|
$F_{ji}$ be the force that particle $j$ exerts on particle $i$. |
32 |
|
Newton's third law states that |
33 |
|
\begin{equation} |
34 |
< |
F_{ij} = -F_{ji} |
34 |
> |
F_{ij} = -F_{ji}. |
35 |
|
\label{introEquation:newtonThirdLaw} |
36 |
|
\end{equation} |
37 |
– |
|
37 |
|
Conservation laws of Newtonian Mechanics play very important roles |
38 |
|
in solving mechanics problems. The linear momentum of a particle is |
39 |
|
conserved if it is free or it experiences no force. The second |
62 |
|
\end{equation} |
63 |
|
If there are no external torques acting on a body, the angular |
64 |
|
momentum of it is conserved. The last conservation theorem state |
65 |
< |
that if all forces are conservative, Energy |
66 |
< |
\begin{equation}E = T + V \label{introEquation:energyConservation} |
65 |
> |
that if all forces are conservative, energy is conserved, |
66 |
> |
\begin{equation}E = T + V. \label{introEquation:energyConservation} |
67 |
|
\end{equation} |
68 |
< |
is conserved. All of these conserved quantities are |
69 |
< |
important factors to determine the quality of numerical integration |
70 |
< |
schemes for rigid bodies \cite{Dullweber1997}. |
68 |
> |
All of these conserved quantities are important factors to determine |
69 |
> |
the quality of numerical integration schemes for rigid bodies |
70 |
> |
\cite{Dullweber1997}. |
71 |
|
|
72 |
|
\subsection{\label{introSection:lagrangian}Lagrangian Mechanics} |
73 |
|
|
74 |
< |
Newtonian Mechanics suffers from two important limitations: motions |
75 |
< |
can only be described in cartesian coordinate systems. Moreover, It |
76 |
< |
become impossible to predict analytically the properties of the |
77 |
< |
system even if we know all of the details of the interaction. In |
78 |
< |
order to overcome some of the practical difficulties which arise in |
79 |
< |
attempts to apply Newton's equation to complex system, approximate |
80 |
< |
numerical procedures may be developed. |
74 |
> |
Newtonian Mechanics suffers from a important limitation: motions can |
75 |
> |
only be described in cartesian coordinate systems which make it |
76 |
> |
impossible to predict analytically the properties of the system even |
77 |
> |
if we know all of the details of the interaction. In order to |
78 |
> |
overcome some of the practical difficulties which arise in attempts |
79 |
> |
to apply Newton's equation to complex system, approximate numerical |
80 |
> |
procedures may be developed. |
81 |
|
|
82 |
|
\subsubsection{\label{introSection:halmiltonPrinciple}\textbf{Hamilton's |
83 |
|
Principle}} |
84 |
|
|
85 |
|
Hamilton introduced the dynamical principle upon which it is |
86 |
|
possible to base all of mechanics and most of classical physics. |
87 |
< |
Hamilton's Principle may be stated as follows, |
88 |
< |
|
89 |
< |
The actual trajectory, along which a dynamical system may move from |
90 |
< |
one point to another within a specified time, is derived by finding |
91 |
< |
the path which minimizes the time integral of the difference between |
93 |
< |
the kinetic, $K$, and potential energies, $U$. |
87 |
> |
Hamilton's Principle may be stated as follows: the actual |
88 |
> |
trajectory, along which a dynamical system may move from one point |
89 |
> |
to another within a specified time, is derived by finding the path |
90 |
> |
which minimizes the time integral of the difference between the |
91 |
> |
kinetic $K$, and potential energies $U$, |
92 |
|
\begin{equation} |
93 |
< |
\delta \int_{t_1 }^{t_2 } {(K - U)dt = 0} , |
93 |
> |
\delta \int_{t_1 }^{t_2 } {(K - U)dt = 0}. |
94 |
|
\label{introEquation:halmitonianPrinciple1} |
95 |
|
\end{equation} |
98 |
– |
|
96 |
|
For simple mechanical systems, where the forces acting on the |
97 |
|
different parts are derivable from a potential, the Lagrangian |
98 |
|
function $L$ can be defined as the difference between the kinetic |
99 |
|
energy of the system and its potential energy, |
100 |
|
\begin{equation} |
101 |
< |
L \equiv K - U = L(q_i ,\dot q_i ) , |
101 |
> |
L \equiv K - U = L(q_i ,\dot q_i ). |
102 |
|
\label{introEquation:lagrangianDef} |
103 |
|
\end{equation} |
104 |
< |
then Eq.~\ref{introEquation:halmitonianPrinciple1} becomes |
104 |
> |
Thus, Eq.~\ref{introEquation:halmitonianPrinciple1} becomes |
105 |
|
\begin{equation} |
106 |
< |
\delta \int_{t_1 }^{t_2 } {L dt = 0} , |
106 |
> |
\delta \int_{t_1 }^{t_2 } {L dt = 0} . |
107 |
|
\label{introEquation:halmitonianPrinciple2} |
108 |
|
\end{equation} |
109 |
|
|
135 |
|
p_i = \frac{{\partial L}}{{\partial q_i }} |
136 |
|
\label{introEquation:generalizedMomentaDot} |
137 |
|
\end{equation} |
141 |
– |
|
138 |
|
With the help of the generalized momenta, we may now define a new |
139 |
|
quantity $H$ by the equation |
140 |
|
\begin{equation} |
142 |
|
\label{introEquation:hamiltonianDefByLagrangian} |
143 |
|
\end{equation} |
144 |
|
where $ \dot q_1 \ldots \dot q_f $ are generalized velocities and |
145 |
< |
$L$ is the Lagrangian function for the system. |
146 |
< |
|
151 |
< |
Differentiating Eq.~\ref{introEquation:hamiltonianDefByLagrangian}, |
152 |
< |
one can obtain |
145 |
> |
$L$ is the Lagrangian function for the system. Differentiating |
146 |
> |
Eq.~\ref{introEquation:hamiltonianDefByLagrangian}, one can obtain |
147 |
|
\begin{equation} |
148 |
|
dH = \sum\limits_k {\left( {p_k d\dot q_k + \dot q_k dp_k - |
149 |
|
\frac{{\partial L}}{{\partial q_k }}dq_k - \frac{{\partial |
150 |
|
L}}{{\partial \dot q_k }}d\dot q_k } \right)} - \frac{{\partial |
151 |
< |
L}}{{\partial t}}dt \label{introEquation:diffHamiltonian1} |
151 |
> |
L}}{{\partial t}}dt . \label{introEquation:diffHamiltonian1} |
152 |
|
\end{equation} |
153 |
< |
Making use of Eq.~\ref{introEquation:generalizedMomenta}, the |
154 |
< |
second and fourth terms in the parentheses cancel. Therefore, |
153 |
> |
Making use of Eq.~\ref{introEquation:generalizedMomenta}, the second |
154 |
> |
and fourth terms in the parentheses cancel. Therefore, |
155 |
|
Eq.~\ref{introEquation:diffHamiltonian1} can be rewritten as |
156 |
|
\begin{equation} |
157 |
|
dH = \sum\limits_k {\left( {\dot q_k dp_k - \dot p_k dq_k } |
158 |
< |
\right)} - \frac{{\partial L}}{{\partial t}}dt |
158 |
> |
\right)} - \frac{{\partial L}}{{\partial t}}dt . |
159 |
|
\label{introEquation:diffHamiltonian2} |
160 |
|
\end{equation} |
161 |
|
By identifying the coefficients of $dq_k$, $dp_k$ and dt, we can |
174 |
|
t}} |
175 |
|
\label{introEquation:motionHamiltonianTime} |
176 |
|
\end{equation} |
177 |
< |
|
184 |
< |
Eq.~\ref{introEquation:motionHamiltonianCoordinate} and |
177 |
> |
where Eq.~\ref{introEquation:motionHamiltonianCoordinate} and |
178 |
|
Eq.~\ref{introEquation:motionHamiltonianMomentum} are Hamilton's |
179 |
|
equation of motion. Due to their symmetrical formula, they are also |
180 |
|
known as the canonical equations of motions \cite{Goldstein2001}. |
188 |
|
statistical mechanics and quantum mechanics, since it treats the |
189 |
|
coordinate and its time derivative as independent variables and it |
190 |
|
only works with 1st-order differential equations\cite{Marion1990}. |
198 |
– |
|
191 |
|
In Newtonian Mechanics, a system described by conservative forces |
192 |
< |
conserves the total energy \ref{introEquation:energyConservation}. |
193 |
< |
It follows that Hamilton's equations of motion conserve the total |
194 |
< |
Hamiltonian. |
192 |
> |
conserves the total energy |
193 |
> |
(Eq.~\ref{introEquation:energyConservation}). It follows that |
194 |
> |
Hamilton's equations of motion conserve the total Hamiltonian |
195 |
|
\begin{equation} |
196 |
|
\frac{{dH}}{{dt}} = \sum\limits_i {\left( {\frac{{\partial |
197 |
|
H}}{{\partial q_i }}\dot q_i + \frac{{\partial H}}{{\partial p_i |
198 |
|
}}\dot p_i } \right)} = \sum\limits_i {\left( {\frac{{\partial |
199 |
|
H}}{{\partial q_i }}\frac{{\partial H}}{{\partial p_i }} - |
200 |
|
\frac{{\partial H}}{{\partial p_i }}\frac{{\partial H}}{{\partial |
201 |
< |
q_i }}} \right) = 0} \label{introEquation:conserveHalmitonian} |
201 |
> |
q_i }}} \right) = 0}. \label{introEquation:conserveHalmitonian} |
202 |
|
\end{equation} |
203 |
|
|
204 |
|
\section{\label{introSection:statisticalMechanics}Statistical |
219 |
|
momentum variables. Consider a dynamic system of $f$ particles in a |
220 |
|
cartesian space, where each of the $6f$ coordinates and momenta is |
221 |
|
assigned to one of $6f$ mutually orthogonal axes, the phase space of |
222 |
< |
this system is a $6f$ dimensional space. A point, $x = (q_1 , \ldots |
223 |
< |
,q_f ,p_1 , \ldots ,p_f )$, with a unique set of values of $6f$ |
224 |
< |
coordinates and momenta is a phase space vector. |
225 |
< |
|
222 |
> |
this system is a $6f$ dimensional space. A point, $x = |
223 |
> |
(\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}} |
224 |
> |
\over q} _1 , \ldots |
225 |
> |
,\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}} |
226 |
> |
\over q} _f |
227 |
> |
,\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}} |
228 |
> |
\over p} _1 \ldots |
229 |
> |
,\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\rightharpoonup$}} |
230 |
> |
\over p} _f )$ , with a unique set of values of $6f$ coordinates and |
231 |
> |
momenta is a phase space vector. |
232 |
|
%%%fix me |
233 |
< |
A microscopic state or microstate of a classical system is |
234 |
< |
specification of the complete phase space vector of a system at any |
237 |
< |
instant in time. An ensemble is defined as a collection of systems |
238 |
< |
sharing one or more macroscopic characteristics but each being in a |
239 |
< |
unique microstate. The complete ensemble is specified by giving all |
240 |
< |
systems or microstates consistent with the common macroscopic |
241 |
< |
characteristics of the ensemble. Although the state of each |
242 |
< |
individual system in the ensemble could be precisely described at |
243 |
< |
any instance in time by a suitable phase space vector, when using |
244 |
< |
ensembles for statistical purposes, there is no need to maintain |
245 |
< |
distinctions between individual systems, since the numbers of |
246 |
< |
systems at any time in the different states which correspond to |
247 |
< |
different regions of the phase space are more interesting. Moreover, |
248 |
< |
in the point of view of statistical mechanics, one would prefer to |
249 |
< |
use ensembles containing a large enough population of separate |
250 |
< |
members so that the numbers of systems in such different states can |
251 |
< |
be regarded as changing continuously as we traverse different |
252 |
< |
regions of the phase space. The condition of an ensemble at any time |
233 |
> |
|
234 |
> |
In statistical mechanics, the condition of an ensemble at any time |
235 |
|
can be regarded as appropriately specified by the density $\rho$ |
236 |
|
with which representative points are distributed over the phase |
237 |
|
space. The density distribution for an ensemble with $f$ degrees of |
243 |
|
Governed by the principles of mechanics, the phase points change |
244 |
|
their locations which would change the density at any time at phase |
245 |
|
space. Hence, the density distribution is also to be taken as a |
246 |
< |
function of the time. |
247 |
< |
|
266 |
< |
The number of systems $\delta N$ at time $t$ can be determined by, |
246 |
> |
function of the time. The number of systems $\delta N$ at time $t$ |
247 |
> |
can be determined by, |
248 |
|
\begin{equation} |
249 |
|
\delta N = \rho (q,p,t)dq_1 \ldots dq_f dp_1 \ldots dp_f. |
250 |
|
\label{introEquation:deltaN} |
277 |
|
\begin{equation} |
278 |
|
\langle A(q , p) \rangle_t = \frac{{\int { \ldots \int {A(q,p)\rho |
279 |
|
(q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}{{\int { \ldots \int {\rho |
280 |
< |
(q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }} |
280 |
> |
(q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}. |
281 |
|
\label{introEquation:ensembelAverage} |
282 |
|
\end{equation} |
283 |
|
|
285 |
|
statistical characteristics. As a function of macroscopic |
286 |
|
parameters, such as temperature \textit{etc}, the partition function |
287 |
|
can be used to describe the statistical properties of a system in |
288 |
< |
thermodynamic equilibrium. |
289 |
< |
|
290 |
< |
As an ensemble of systems, each of which is known to be thermally |
310 |
< |
isolated and conserve energy, the Microcanonical ensemble (NVE) has |
311 |
< |
a partition function like, |
288 |
> |
thermodynamic equilibrium. As an ensemble of systems, each of which |
289 |
> |
is known to be thermally isolated and conserve energy, the |
290 |
> |
Microcanonical ensemble (NVE) has a partition function like, |
291 |
|
\begin{equation} |
292 |
< |
\Omega (N,V,E) = e^{\beta TS} \label{introEquation:NVEPartition}. |
292 |
> |
\Omega (N,V,E) = e^{\beta TS}. \label{introEquation:NVEPartition} |
293 |
|
\end{equation} |
294 |
< |
A canonical ensemble (NVT)is an ensemble of systems, each of which |
294 |
> |
A canonical ensemble (NVT) is an ensemble of systems, each of which |
295 |
|
can share its energy with a large heat reservoir. The distribution |
296 |
|
of the total energy amongst the possible dynamical states is given |
297 |
|
by the partition function, |
298 |
|
\begin{equation} |
299 |
< |
\Omega (N,V,T) = e^{ - \beta A} |
299 |
> |
\Omega (N,V,T) = e^{ - \beta A}. |
300 |
|
\label{introEquation:NVTPartition} |
301 |
|
\end{equation} |
302 |
|
Here, $A$ is the Helmholtz free energy which is defined as $ A = U - |
353 |
|
\frac{{\partial \rho }}{{\partial p_i }}\dot p_i } \right)} = 0 . |
354 |
|
\label{introEquation:liouvilleTheorem} |
355 |
|
\end{equation} |
377 |
– |
|
356 |
|
Liouville's theorem states that the distribution function is |
357 |
|
constant along any trajectory in phase space. In classical |
358 |
|
statistical mechanics, since the number of members in an ensemble is |
409 |
|
q_i }}} \right)}. |
410 |
|
\label{introEquation:poissonBracket} |
411 |
|
\end{equation} |
412 |
< |
Substituting equations of motion in Hamiltonian formalism( |
413 |
< |
Eq.~\ref{introEquation:motionHamiltonianCoordinate} , |
414 |
< |
Eq.~\ref{introEquation:motionHamiltonianMomentum} ) into |
412 |
> |
Substituting equations of motion in Hamiltonian formalism |
413 |
> |
(Eq.~\ref{introEquation:motionHamiltonianCoordinate} , |
414 |
> |
Eq.~\ref{introEquation:motionHamiltonianMomentum}) into |
415 |
|
(Eq.~\ref{introEquation:liouvilleTheorem}), we can rewrite |
416 |
|
Liouville's theorem using Poisson bracket notion, |
417 |
|
\begin{equation} |
445 |
|
many-body system in Statistical Mechanics. Fortunately, the Ergodic |
446 |
|
Hypothesis makes a connection between time average and the ensemble |
447 |
|
average. It states that the time average and average over the |
448 |
< |
statistical ensemble are identical \cite{Frenkel1996, Leach2001}. |
448 |
> |
statistical ensemble are identical \cite{Frenkel1996, Leach2001}: |
449 |
|
\begin{equation} |
450 |
|
\langle A(q , p) \rangle_t = \mathop {\lim }\limits_{t \to \infty } |
451 |
|
\frac{1}{t}\int\limits_0^t {A(q(t),p(t))dt = \int\limits_\Gamma |
459 |
|
a properly weighted statistical average. This allows the researcher |
460 |
|
freedom of choice when deciding how best to measure a given |
461 |
|
observable. In case an ensemble averaged approach sounds most |
462 |
< |
reasonable, the Monte Carlo techniques\cite{Metropolis1949} can be |
462 |
> |
reasonable, the Monte Carlo methods\cite{Metropolis1949} can be |
463 |
|
utilized. Or if the system lends itself to a time averaging |
464 |
|
approach, the Molecular Dynamics techniques in |
465 |
|
Sec.~\ref{introSection:molecularDynamics} will be the best |
494 |
|
$\omega(\lambda_1x_1+\lambda_2x_2, y) = \lambda_1\omega(x_1, y)+ |
495 |
|
\lambda_2\omega(x_2, y)$, $\omega(x, y) = - \omega(y, x)$ and |
496 |
|
$\omega(x, x) = 0$. The cross product operation in vector field is |
497 |
< |
an example of symplectic form. |
497 |
> |
an example of symplectic form. One of the motivations to study |
498 |
> |
\emph{symplectic manifolds} in Hamiltonian Mechanics is that a |
499 |
> |
symplectic manifold can represent all possible configurations of the |
500 |
> |
system and the phase space of the system can be described by it's |
501 |
> |
cotangent bundle. Every symplectic manifold is even dimensional. For |
502 |
> |
instance, in Hamilton equations, coordinate and momentum always |
503 |
> |
appear in pairs. |
504 |
|
|
521 |
– |
One of the motivations to study \emph{symplectic manifolds} in |
522 |
– |
Hamiltonian Mechanics is that a symplectic manifold can represent |
523 |
– |
all possible configurations of the system and the phase space of the |
524 |
– |
system can be described by it's cotangent bundle. Every symplectic |
525 |
– |
manifold is even dimensional. For instance, in Hamilton equations, |
526 |
– |
coordinate and momentum always appear in pairs. |
527 |
– |
|
505 |
|
\subsection{\label{introSection:ODE}Ordinary Differential Equations} |
506 |
|
|
507 |
|
For an ordinary differential system defined as |
509 |
|
\dot x = f(x) |
510 |
|
\end{equation} |
511 |
|
where $x = x(q,p)^T$, this system is a canonical Hamiltonian, if |
512 |
+ |
$f(r) = J\nabla _x H(r)$. Here, $H = H (q, p)$ is Hamiltonian |
513 |
+ |
function and $J$ is the skew-symmetric matrix |
514 |
|
\begin{equation} |
536 |
– |
f(r) = J\nabla _x H(r). |
537 |
– |
\end{equation} |
538 |
– |
$H = H (q, p)$ is Hamiltonian function and $J$ is the skew-symmetric |
539 |
– |
matrix |
540 |
– |
\begin{equation} |
515 |
|
J = \left( {\begin{array}{*{20}c} |
516 |
|
0 & I \\ |
517 |
|
{ - I} & 0 \\ |
521 |
|
where $I$ is an identity matrix. Using this notation, Hamiltonian |
522 |
|
system can be rewritten as, |
523 |
|
\begin{equation} |
524 |
< |
\frac{d}{{dt}}x = J\nabla _x H(x) |
524 |
> |
\frac{d}{{dt}}x = J\nabla _x H(x). |
525 |
|
\label{introEquation:compactHamiltonian} |
526 |
|
\end{equation}In this case, $f$ is |
527 |
< |
called a \emph{Hamiltonian vector field}. |
528 |
< |
|
555 |
< |
Another generalization of Hamiltonian dynamics is Poisson |
556 |
< |
Dynamics\cite{Olver1986}, |
527 |
> |
called a \emph{Hamiltonian vector field}. Another generalization of |
528 |
> |
Hamiltonian dynamics is Poisson Dynamics\cite{Olver1986}, |
529 |
|
\begin{equation} |
530 |
|
\dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian} |
531 |
|
\end{equation} |
533 |
|
|
534 |
|
\subsection{\label{introSection:exactFlow}Exact Flow} |
535 |
|
|
536 |
< |
Let $x(t)$ be the exact solution of the ODE system, |
537 |
< |
\begin{equation} |
538 |
< |
\frac{{dx}}{{dt}} = f(x) \label{introEquation:ODE} |
539 |
< |
\end{equation} |
540 |
< |
The exact flow(solution) $\varphi_\tau$ is defined by |
569 |
< |
\[ |
570 |
< |
x(t+\tau) =\varphi_\tau(x(t)) |
536 |
> |
Let $x(t)$ be the exact solution of the ODE |
537 |
> |
system,$\frac{{dx}}{{dt}} = f(x) \label{introEquation:ODE}$, we can |
538 |
> |
define its exact flow(solution) $\varphi_\tau$ |
539 |
> |
\[ x(t+\tau) |
540 |
> |
=\varphi_\tau(x(t)) |
541 |
|
\] |
542 |
|
where $\tau$ is a fixed time step and $\varphi$ is a map from phase |
543 |
|
space to itself. The flow has the continuous group property, |
559 |
|
}{{\partial x_i }}} } (x) \equiv \exp (\tau f)(x). |
560 |
|
\label{introEquation:exponentialOperator} |
561 |
|
\end{equation} |
592 |
– |
|
562 |
|
In most cases, it is not easy to find the exact flow $\varphi_\tau$. |
563 |
|
Instead, we use an approximate map, $\psi_\tau$, which is usually |
564 |
|
called integrator. The order of an integrator $\psi_\tau$ is $p$, if |
572 |
|
The hidden geometric properties\cite{Budd1999, Marsden1998} of an |
573 |
|
ODE and its flow play important roles in numerical studies. Many of |
574 |
|
them can be found in systems which occur naturally in applications. |
606 |
– |
|
575 |
|
Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is |
576 |
|
a \emph{symplectic} flow if it satisfies, |
577 |
|
\begin{equation} |
585 |
|
\begin{equation} |
586 |
|
{\varphi '}^T J \varphi ' = J \circ \varphi |
587 |
|
\end{equation} |
588 |
< |
is the property that must be preserved by the integrator. |
589 |
< |
|
590 |
< |
It is possible to construct a \emph{volume-preserving} flow for a |
591 |
< |
source free ODE ($ \nabla \cdot f = 0 $), if the flow satisfies $ |
592 |
< |
\det d\varphi = 1$. One can show easily that a symplectic flow will |
625 |
< |
be volume-preserving. |
626 |
< |
|
627 |
< |
Changing the variables $y = h(x)$ in an ODE |
588 |
> |
is the property that must be preserved by the integrator. It is |
589 |
> |
possible to construct a \emph{volume-preserving} flow for a source |
590 |
> |
free ODE ($ \nabla \cdot f = 0 $), if the flow satisfies $ \det |
591 |
> |
d\varphi = 1$. One can show easily that a symplectic flow will be |
592 |
> |
volume-preserving. Changing the variables $y = h(x)$ in an ODE |
593 |
|
(Eq.~\ref{introEquation:ODE}) will result in a new system, |
594 |
|
\[ |
595 |
|
\dot y = \tilde f(y) = ((dh \cdot f)h^{ - 1} )(y). |
596 |
|
\] |
597 |
|
The vector filed $f$ has reversing symmetry $h$ if $f = - \tilde f$. |
598 |
|
In other words, the flow of this vector field is reversible if and |
599 |
< |
only if $ h \circ \varphi ^{ - 1} = \varphi \circ h $. |
600 |
< |
|
636 |
< |
A \emph{first integral}, or conserved quantity of a general |
599 |
> |
only if $ h \circ \varphi ^{ - 1} = \varphi \circ h $. A |
600 |
> |
\emph{first integral}, or conserved quantity of a general |
601 |
|
differential function is a function $ G:R^{2d} \to R^d $ which is |
602 |
|
constant for all solutions of the ODE $\frac{{dx}}{{dt}} = f(x)$ , |
603 |
|
\[ |
605 |
|
\] |
606 |
|
Using chain rule, one may obtain, |
607 |
|
\[ |
608 |
< |
\sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \bullet \nabla G, |
608 |
> |
\sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \dot \nabla G, |
609 |
|
\] |
610 |
|
which is the condition for conserving \emph{first integral}. For a |
611 |
|
canonical Hamiltonian system, the time evolution of an arbitrary |
612 |
|
smooth function $G$ is given by, |
613 |
< |
|
614 |
< |
\begin{eqnarray} |
615 |
< |
\frac{{dG(x(t))}}{{dt}} & = & [\nabla _x G(x(t))]^T \dot x(t) \\ |
652 |
< |
& = & [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\ |
613 |
> |
\begin{eqnarray} |
614 |
> |
\frac{{dG(x(t))}}{{dt}} & = & [\nabla _x G(x(t))]^T \dot x(t) \notag\\ |
615 |
> |
& = & [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). |
616 |
|
\label{introEquation:firstIntegral1} |
617 |
|
\end{eqnarray} |
618 |
< |
|
619 |
< |
|
657 |
< |
Using poisson bracket notion, Equation |
658 |
< |
\ref{introEquation:firstIntegral1} can be rewritten as |
618 |
> |
Using poisson bracket notion, Eq.~\ref{introEquation:firstIntegral1} |
619 |
> |
can be rewritten as |
620 |
|
\[ |
621 |
|
\frac{d}{{dt}}G(x(t)) = \left\{ {G,H} \right\}(x(t)). |
622 |
|
\] |
623 |
|
Therefore, the sufficient condition for $G$ to be the \emph{first |
624 |
< |
integral} of a Hamiltonian system is |
664 |
< |
\[ |
665 |
< |
\left\{ {G,H} \right\} = 0. |
666 |
< |
\] |
624 |
> |
integral} of a Hamiltonian system is $\left\{ {G,H} \right\} = 0.$ |
625 |
|
As well known, the Hamiltonian (or energy) H of a Hamiltonian system |
626 |
|
is a \emph{first integral}, which is due to the fact $\{ H,H\} = |
627 |
< |
0$. |
670 |
< |
|
671 |
< |
When designing any numerical methods, one should always try to |
627 |
> |
0$. When designing any numerical methods, one should always try to |
628 |
|
preserve the structural properties of the original ODE and its flow. |
629 |
|
|
630 |
|
\subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods} |
640 |
|
\item Runge-Kutta methods |
641 |
|
\item Splitting methods |
642 |
|
\end{enumerate} |
687 |
– |
|
643 |
|
Generating function\cite{Channell1990} tends to lead to methods |
644 |
|
which are cumbersome and difficult to use. In dissipative systems, |
645 |
|
variational methods can capture the decay of energy |
646 |
|
accurately\cite{Kane2000}. Since their geometrically unstable nature |
647 |
|
against non-Hamiltonian perturbations, ordinary implicit Runge-Kutta |
648 |
|
methods are not suitable for Hamiltonian system. Recently, various |
649 |
< |
high-order explicit Runge-Kutta methods |
650 |
< |
\cite{Owren1992,Chen2003}have been developed to overcome this |
651 |
< |
instability. However, due to computational penalty involved in |
652 |
< |
implementing the Runge-Kutta methods, they have not attracted much |
653 |
< |
attention from the Molecular Dynamics community. Instead, splitting |
654 |
< |
methods have been widely accepted since they exploit natural |
655 |
< |
decompositions of the system\cite{Tuckerman1992, McLachlan1998}. |
649 |
> |
high-order explicit Runge-Kutta methods \cite{Owren1992,Chen2003} |
650 |
> |
have been developed to overcome this instability. However, due to |
651 |
> |
computational penalty involved in implementing the Runge-Kutta |
652 |
> |
methods, they have not attracted much attention from the Molecular |
653 |
> |
Dynamics community. Instead, splitting methods have been widely |
654 |
> |
accepted since they exploit natural decompositions of the |
655 |
> |
system\cite{Tuckerman1992, McLachlan1998}. |
656 |
|
|
657 |
|
\subsubsection{\label{introSection:splittingMethod}\textbf{Splitting Methods}} |
658 |
|
|
664 |
|
\label{introEquation:FlowDecomposition} |
665 |
|
\end{equation} |
666 |
|
where each of the sub-flow is chosen such that each represent a |
667 |
< |
simpler integration of the system. |
668 |
< |
|
714 |
< |
Suppose that a Hamiltonian system takes the form, |
667 |
> |
simpler integration of the system. Suppose that a Hamiltonian system |
668 |
> |
takes the form, |
669 |
|
\[ |
670 |
|
H = H_1 + H_2. |
671 |
|
\] |
693 |
|
where $\phi$ and $\psi$ both are symplectic maps. Thus operator |
694 |
|
splitting in this context automatically generates a symplectic map. |
695 |
|
|
696 |
< |
The Lie-Trotter splitting(\ref{introEquation:firstOrderSplitting}) |
697 |
< |
introduces local errors proportional to $h^2$, while Strang |
698 |
< |
splitting gives a second-order decomposition, |
696 |
> |
The Lie-Trotter |
697 |
> |
splitting(Eq.~\ref{introEquation:firstOrderSplitting}) introduces |
698 |
> |
local errors proportional to $h^2$, while Strang splitting gives a |
699 |
> |
second-order decomposition, |
700 |
|
\begin{equation} |
701 |
|
\varphi _h = \varphi _{1,h/2} \circ \varphi _{2,h} \circ \varphi |
702 |
|
_{1,h/2} , \label{introEquation:secondOrderSplitting} |
707 |
|
\begin{equation} |
708 |
|
\varphi _h^{ - 1} = \varphi _{ - h}. |
709 |
|
\label{introEquation:timeReversible} |
710 |
< |
\end{equation},appendixFig:architecture |
710 |
> |
\end{equation} |
711 |
|
|
712 |
|
\subsubsection{\label{introSection:exampleSplittingMethod}\textbf{Examples of the Splitting Method}} |
713 |
|
The classical equation for a system consisting of interacting |
756 |
|
|
757 |
|
\item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values. |
758 |
|
\end{enumerate} |
804 |
– |
|
759 |
|
By simply switching the order of the propagators in the splitting |
760 |
|
and composing a new integrator, the \emph{position verlet} |
761 |
|
integrator, can be generated, |
793 |
|
\begin{eqnarray*} |
794 |
|
\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 \\ |
795 |
|
& & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\ |
796 |
< |
& & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3[X,[X,Y]]/24 + \ldots ) |
796 |
> |
& & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3[X,[X,Y]]/24 + \ldots |
797 |
> |
). |
798 |
|
\end{eqnarray*} |
799 |
< |
Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0,\] the dominant local |
799 |
> |
Since $ [X,Y] + [Y,X] = 0$ and $ [X,X] = 0$, the dominant local |
800 |
|
error of Spring splitting is proportional to $h^3$. The same |
801 |
< |
procedure can be applied to a general splitting, of the form |
801 |
> |
procedure can be applied to a general splitting of the form |
802 |
|
\begin{equation} |
803 |
|
\varphi _{b_m h}^2 \circ \varphi _{a_m h}^1 \circ \varphi _{b_{m - |
804 |
|
1} h}^2 \circ \ldots \circ \varphi _{a_1 h}^1 . |
943 |
|
Coulombic forces \textit{etc}. For a system of $N$ particles, the |
944 |
|
complexity of the algorithm for pair-wise interactions is $O(N^2 )$, |
945 |
|
which making large simulations prohibitive in the absence of any |
946 |
< |
algorithmic tricks. |
947 |
< |
|
948 |
< |
A natural approach to avoid system size issues is to represent the |
949 |
< |
bulk behavior by a finite number of the particles. However, this |
950 |
< |
approach will suffer from the surface effect at the edges of the |
951 |
< |
simulation. To offset this, \textit{Periodic boundary conditions} |
952 |
< |
(see Fig.~\ref{introFig:pbc}) is developed to simulate bulk |
953 |
< |
properties with a relatively small number of particles. In this |
954 |
< |
method, the simulation box is replicated throughout space to form an |
955 |
< |
infinite lattice. During the simulation, when a particle moves in |
956 |
< |
the primary cell, its image in other cells move in exactly the same |
957 |
< |
direction with exactly the same orientation. Thus, as a particle |
1003 |
< |
leaves the primary cell, one of its images will enter through the |
1004 |
< |
opposite face. |
946 |
> |
algorithmic tricks. A natural approach to avoid system size issues |
947 |
> |
is to represent the bulk behavior by a finite number of the |
948 |
> |
particles. However, this approach will suffer from the surface |
949 |
> |
effect at the edges of the simulation. To offset this, |
950 |
> |
\textit{Periodic boundary conditions} (see Fig.~\ref{introFig:pbc}) |
951 |
> |
is developed to simulate bulk properties with a relatively small |
952 |
> |
number of particles. In this method, the simulation box is |
953 |
> |
replicated throughout space to form an infinite lattice. During the |
954 |
> |
simulation, when a particle moves in the primary cell, its image in |
955 |
> |
other cells move in exactly the same direction with exactly the same |
956 |
> |
orientation. Thus, as a particle leaves the primary cell, one of its |
957 |
> |
images will enter through the opposite face. |
958 |
|
\begin{figure} |
959 |
|
\centering |
960 |
|
\includegraphics[width=\linewidth]{pbc.eps} |
1017 |
|
monitor the motions of molecules. Although the dynamics of the |
1018 |
|
system can be described qualitatively from animation, quantitative |
1019 |
|
trajectory analysis are more useful. According to the principles of |
1020 |
< |
Statistical Mechanics, Sec.~\ref{introSection:statisticalMechanics}, |
1021 |
< |
one can compute thermodynamic properties, analyze fluctuations of |
1022 |
< |
structural parameters, and investigate time-dependent processes of |
1023 |
< |
the molecule from the trajectories. |
1020 |
> |
Statistical Mechanics in |
1021 |
> |
Sec.~\ref{introSection:statisticalMechanics}, one can compute |
1022 |
> |
thermodynamic properties, analyze fluctuations of structural |
1023 |
> |
parameters, and investigate time-dependent processes of the molecule |
1024 |
> |
from the trajectories. |
1025 |
|
|
1026 |
|
\subsubsection{\label{introSection:thermodynamicsProperties}\textbf{Thermodynamic Properties}} |
1027 |
|
|
1058 |
|
to justify the correctness of a liquid model. Moreover, various |
1059 |
|
equilibrium thermodynamic and structural properties can also be |
1060 |
|
expressed in terms of radial distribution function \cite{Allen1987}. |
1107 |
– |
|
1061 |
|
The pair distribution functions $g(r)$ gives the probability that a |
1062 |
|
particle $i$ will be located at a distance $r$ from a another |
1063 |
|
particle $j$ in the system |
1064 |
< |
\[ |
1064 |
> |
\begin{equation} |
1065 |
|
g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j |
1066 |
|
\ne i} {\delta (r - r_{ij} )} } } \right\rangle = \frac{\rho |
1067 |
|
(r)}{\rho}. |
1068 |
< |
\] |
1068 |
> |
\end{equation} |
1069 |
|
Note that the delta function can be replaced by a histogram in |
1070 |
< |
computer simulation. Figure |
1071 |
< |
\ref{introFigure:pairDistributionFunction} shows a typical pair |
1072 |
< |
distribution function for the liquid argon system. The occurrence of |
1120 |
< |
several peaks in the plot of $g(r)$ suggests that it is more likely |
1121 |
< |
to find particles at certain radial values than at others. This is a |
1122 |
< |
result of the attractive interaction at such distances. Because of |
1123 |
< |
the strong repulsive forces at short distance, the probability of |
1124 |
< |
locating particles at distances less than about 3.7{\AA} from each |
1125 |
< |
other is essentially zero. |
1070 |
> |
computer simulation. Peaks in $g(r)$ represent solvent shells, and |
1071 |
> |
the height of these peaks gradually decreases to 1 as the liquid of |
1072 |
> |
large distance approaches the bulk density. |
1073 |
|
|
1127 |
– |
%\begin{figure} |
1128 |
– |
%\centering |
1129 |
– |
%\includegraphics[width=\linewidth]{pdf.eps} |
1130 |
– |
%\caption[Pair distribution function for the liquid argon |
1131 |
– |
%]{Pair distribution function for the liquid argon} |
1132 |
– |
%\label{introFigure:pairDistributionFunction} |
1133 |
– |
%\end{figure} |
1074 |
|
|
1075 |
|
\subsubsection{\label{introSection:timeDependentProperties}\textbf{Time-dependent |
1076 |
|
Properties}} |
1102 |
|
Here $u_{tot}$ is the net dipole of the entire system and is given |
1103 |
|
by |
1104 |
|
\[ |
1105 |
< |
u_{tot} (t) = \sum\limits_i {u_i (t)} |
1105 |
> |
u_{tot} (t) = \sum\limits_i {u_i (t)}. |
1106 |
|
\] |
1107 |
|
In principle, many time correlation functions can be related with |
1108 |
|
Fourier transforms of the infrared, Raman, and inelastic neutron |
1111 |
|
each frequency using the following relationship: |
1112 |
|
\[ |
1113 |
|
\hat c_{dipole} (v) = \int_{ - \infty }^\infty {c_{dipole} (t)e^{ - |
1114 |
< |
i2\pi vt} dt} |
1114 |
> |
i2\pi vt} dt}. |
1115 |
|
\] |
1116 |
|
|
1117 |
|
\section{\label{introSection:rigidBody}Dynamics of Rigid Bodies} |
1179 |
|
Q^T Q = 1, \label{introEquation:orthogonalConstraint} |
1180 |
|
\end{equation} |
1181 |
|
which is used to ensure rotation matrix's unitarity. Differentiating |
1182 |
< |
\ref{introEquation:orthogonalConstraint} and using Equation |
1183 |
< |
\ref{introEquation:RBMotionMomentum}, one may obtain, |
1182 |
> |
Eq.~\ref{introEquation:orthogonalConstraint} and using |
1183 |
> |
Eq.~\ref{introEquation:RBMotionMomentum}, one may obtain, |
1184 |
|
\begin{equation} |
1185 |
|
Q^T PJ^{ - 1} + J^{ - 1} P^T Q = 0 . \\ |
1186 |
|
\label{introEquation:RBFirstOrderConstraint} |
1187 |
|
\end{equation} |
1248 |
– |
|
1188 |
|
Using Equation (\ref{introEquation:motionHamiltonianCoordinate}, |
1189 |
|
\ref{introEquation:motionHamiltonianMomentum}), one can write down |
1190 |
|
the equations of motion, |
1252 |
– |
|
1191 |
|
\begin{eqnarray} |
1192 |
< |
\frac{{dq}}{{dt}} & = & \frac{p}{m} \label{introEquation:RBMotionPosition}\\ |
1193 |
< |
\frac{{dp}}{{dt}} & = & - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\ |
1194 |
< |
\frac{{dQ}}{{dt}} & = & PJ^{ - 1} \label{introEquation:RBMotionRotation}\\ |
1192 |
> |
\frac{{dq}}{{dt}} & = & \frac{p}{m}, \label{introEquation:RBMotionPosition}\\ |
1193 |
> |
\frac{{dp}}{{dt}} & = & - \nabla _q V(q,Q), \label{introEquation:RBMotionMomentum}\\ |
1194 |
> |
\frac{{dQ}}{{dt}} & = & PJ^{ - 1}, \label{introEquation:RBMotionRotation}\\ |
1195 |
|
\frac{{dP}}{{dt}} & = & - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP} |
1196 |
|
\end{eqnarray} |
1259 |
– |
|
1197 |
|
In general, there are two ways to satisfy the holonomic constraints. |
1198 |
|
We can use a constraint force provided by a Lagrange multiplier on |
1199 |
|
the normal manifold to keep the motion on constraint space. Or we |
1205 |
|
M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1} + J^{ - 1} P^T Q = 0} |
1206 |
|
\right\}. |
1207 |
|
\] |
1271 |
– |
|
1208 |
|
Unfortunately, this constraint manifold is not the cotangent bundle |
1209 |
< |
$T_{\star}SO(3)$. However, it turns out that under symplectic |
1209 |
> |
$T^* SO(3)$ which can be consider as a symplectic manifold on Lie |
1210 |
> |
rotation group $SO(3)$. However, it turns out that under symplectic |
1211 |
|
transformation, the cotangent space and the phase space are |
1212 |
|
diffeomorphic. By introducing |
1213 |
|
\[ |
1219 |
|
T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q = |
1220 |
|
1,\tilde Q^T \tilde PJ^{ - 1} + J^{ - 1} P^T \tilde Q = 0} \right\} |
1221 |
|
\] |
1285 |
– |
|
1222 |
|
For a body fixed vector $X_i$ with respect to the center of mass of |
1223 |
|
the rigid body, its corresponding lab fixed vector $X_0^{lab}$ is |
1224 |
|
given as |
1237 |
|
\[ |
1238 |
|
\nabla _Q V(q,Q) = F(q,Q)X_i^t |
1239 |
|
\] |
1240 |
< |
respectively. |
1241 |
< |
|
1242 |
< |
As a common choice to describe the rotation dynamics of the rigid |
1307 |
< |
body, the angular momentum on the body fixed frame $\Pi = Q^t P$ is |
1308 |
< |
introduced to rewrite the equations of motion, |
1240 |
> |
respectively. As a common choice to describe the rotation dynamics |
1241 |
> |
of the rigid body, the angular momentum on the body fixed frame $\Pi |
1242 |
> |
= Q^t P$ is introduced to rewrite the equations of motion, |
1243 |
|
\begin{equation} |
1244 |
|
\begin{array}{l} |
1245 |
< |
\mathop \Pi \limits^ \bullet = J^{ - 1} \Pi ^T \Pi + Q^T \sum\limits_i {F_i (q,Q)X_i^T } - \Lambda \\ |
1246 |
< |
\mathop Q\limits^{{\rm{ }} \bullet } = Q\Pi {\rm{ }}J^{ - 1} \\ |
1245 |
> |
\dot \Pi = J^{ - 1} \Pi ^T \Pi + Q^T \sum\limits_i {F_i (q,Q)X_i^T } - \Lambda, \\ |
1246 |
> |
\dot Q = Q\Pi {\rm{ }}J^{ - 1}, \\ |
1247 |
|
\end{array} |
1248 |
|
\label{introEqaution:RBMotionPI} |
1249 |
|
\end{equation} |
1250 |
< |
, as well as holonomic constraints, |
1251 |
< |
\[ |
1252 |
< |
\begin{array}{l} |
1319 |
< |
\Pi J^{ - 1} + J^{ - 1} \Pi ^t = 0 \\ |
1320 |
< |
Q^T Q = 1 \\ |
1321 |
< |
\end{array} |
1322 |
< |
\] |
1323 |
< |
|
1324 |
< |
For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in |
1325 |
< |
so(3)^ \star$, the hat-map isomorphism, |
1250 |
> |
as well as holonomic constraints $\Pi J^{ - 1} + J^{ - 1} \Pi ^t = |
1251 |
> |
0$ and $Q^T Q = 1$. For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a |
1252 |
> |
matrix $\hat v \in so(3)^ \star$, the hat-map isomorphism, |
1253 |
|
\begin{equation} |
1254 |
|
v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left( |
1255 |
|
{\begin{array}{*{20}c} |
1262 |
|
will let us associate the matrix products with traditional vector |
1263 |
|
operations |
1264 |
|
\[ |
1265 |
< |
\hat vu = v \times u |
1265 |
> |
\hat vu = v \times u. |
1266 |
|
\] |
1267 |
< |
Using \ref{introEqaution:RBMotionPI}, one can construct a skew |
1267 |
> |
Using Eq.~\ref{introEqaution:RBMotionPI}, one can construct a skew |
1268 |
|
matrix, |
1269 |
+ |
\begin{eqnarray} |
1270 |
+ |
(\dot \Pi - \dot \Pi ^T )&= &(\Pi - \Pi ^T )(J^{ - 1} \Pi + \Pi J^{ - 1} ) \notag \\ |
1271 |
+ |
& & + \sum\limits_i {[Q^T F_i (r,Q)X_i^T - X_i F_i (r,Q)^T Q]} - |
1272 |
+ |
(\Lambda - \Lambda ^T ). \label{introEquation:skewMatrixPI} |
1273 |
+ |
\end{eqnarray} |
1274 |
+ |
Since $\Lambda$ is symmetric, the last term of |
1275 |
+ |
Eq.~\ref{introEquation:skewMatrixPI} is zero, which implies the |
1276 |
+ |
Lagrange multiplier $\Lambda$ is absent from the equations of |
1277 |
+ |
motion. This unique property eliminates the requirement of |
1278 |
+ |
iterations which can not be avoided in other methods\cite{Kol1997, |
1279 |
+ |
Omelyan1998}. Applying the hat-map isomorphism, we obtain the |
1280 |
+ |
equation of motion for angular momentum on body frame |
1281 |
|
\begin{equation} |
1343 |
– |
(\mathop \Pi \limits^ \bullet - \mathop \Pi \limits^ {\bullet ^T} |
1344 |
– |
){\rm{ }} = {\rm{ }}(\Pi - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi + \Pi J^{ |
1345 |
– |
- 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T - X_i F_i (r,Q)^T Q]} - |
1346 |
– |
(\Lambda - \Lambda ^T ) . \label{introEquation:skewMatrixPI} |
1347 |
– |
\end{equation} |
1348 |
– |
Since $\Lambda$ is symmetric, the last term of Equation |
1349 |
– |
\ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange |
1350 |
– |
multiplier $\Lambda$ is absent from the equations of motion. This |
1351 |
– |
unique property eliminates the requirement of iterations which can |
1352 |
– |
not be avoided in other methods\cite{Kol1997, Omelyan1998}. |
1353 |
– |
|
1354 |
– |
Applying the hat-map isomorphism, we obtain the equation of motion |
1355 |
– |
for angular momentum on body frame |
1356 |
– |
\begin{equation} |
1282 |
|
\dot \pi = \pi \times I^{ - 1} \pi + \sum\limits_i {\left( {Q^T |
1283 |
|
F_i (r,Q)} \right) \times X_i }. |
1284 |
|
\label{introEquation:bodyAngularMotion} |
1286 |
|
In the same manner, the equation of motion for rotation matrix is |
1287 |
|
given by |
1288 |
|
\[ |
1289 |
< |
\dot Q = Qskew(I^{ - 1} \pi ) |
1289 |
> |
\dot Q = Qskew(I^{ - 1} \pi ). |
1290 |
|
\] |
1291 |
|
|
1292 |
|
\subsection{\label{introSection:SymplecticFreeRB}Symplectic |
1308 |
|
0 & {\pi _3 } & { - \pi _2 } \\ |
1309 |
|
{ - \pi _3 } & 0 & {\pi _1 } \\ |
1310 |
|
{\pi _2 } & { - \pi _1 } & 0 \\ |
1311 |
< |
\end{array}} \right) |
1311 |
> |
\end{array}} \right). |
1312 |
|
\end{equation} |
1313 |
|
Thus, the dynamics of free rigid body is governed by |
1314 |
|
\begin{equation} |
1315 |
< |
\frac{d}{{dt}}\pi = J(\pi )\nabla _\pi T^r (\pi ) |
1315 |
> |
\frac{d}{{dt}}\pi = J(\pi )\nabla _\pi T^r (\pi ). |
1316 |
|
\end{equation} |
1317 |
< |
|
1318 |
< |
One may notice that each $T_i^r$ in Equation |
1319 |
< |
\ref{introEquation:rotationalKineticRB} can be solved exactly. For |
1395 |
< |
instance, the equations of motion due to $T_1^r$ are given by |
1317 |
> |
One may notice that each $T_i^r$ in |
1318 |
> |
Eq.~\ref{introEquation:rotationalKineticRB} can be solved exactly. |
1319 |
> |
For instance, the equations of motion due to $T_1^r$ are given by |
1320 |
|
\begin{equation} |
1321 |
|
\frac{d}{{dt}}\pi = R_1 \pi ,\frac{d}{{dt}}Q = QR_1 |
1322 |
|
\label{introEqaution:RBMotionSingleTerm} |
1323 |
|
\end{equation} |
1324 |
< |
where |
1324 |
> |
with |
1325 |
|
\[ R_1 = \left( {\begin{array}{*{20}c} |
1326 |
|
0 & 0 & 0 \\ |
1327 |
|
0 & 0 & {\pi _1 } \\ |
1328 |
|
0 & { - \pi _1 } & 0 \\ |
1329 |
|
\end{array}} \right). |
1330 |
|
\] |
1331 |
< |
The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is |
1331 |
> |
The solutions of Eq.~\ref{introEqaution:RBMotionSingleTerm} is |
1332 |
|
\[ |
1333 |
|
\pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) = |
1334 |
|
Q(0)e^{\Delta tR_1 } |
1346 |
|
propagator, |
1347 |
|
\[ |
1348 |
|
e^{\Delta tR_1 } \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1 |
1349 |
< |
) |
1349 |
> |
). |
1350 |
|
\] |
1351 |
|
The flow maps for $T_2^r$ and $T_3^r$ can be found in the same |
1352 |
|
manner. In order to construct a second-order symplectic method, we |
1353 |
< |
split the angular kinetic Hamiltonian function can into five terms |
1353 |
> |
split the angular kinetic Hamiltonian function into five terms |
1354 |
|
\[ |
1355 |
|
T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2 |
1356 |
|
) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r |
1364 |
|
\circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t/2,\pi |
1365 |
|
_1 }. |
1366 |
|
\] |
1443 |
– |
|
1367 |
|
The non-canonical Lie-Poisson bracket ${F, G}$ of two function |
1368 |
|
$F(\pi )$ and $G(\pi )$ is defined by |
1369 |
|
\[ |
1370 |
|
\{ F,G\} (\pi ) = [\nabla _\pi F(\pi )]^T J(\pi )\nabla _\pi G(\pi |
1371 |
< |
) |
1371 |
> |
). |
1372 |
|
\] |
1373 |
|
If the Poisson bracket of a function $F$ with an arbitrary smooth |
1374 |
|
function $G$ is zero, $F$ is a \emph{Casimir}, which is the |
1379 |
|
then by the chain rule |
1380 |
|
\[ |
1381 |
|
\nabla _\pi F(\pi ) = S'(\frac{{\parallel \pi \parallel ^2 |
1382 |
< |
}}{2})\pi |
1382 |
> |
}}{2})\pi. |
1383 |
|
\] |
1384 |
< |
Thus $ [\nabla _\pi F(\pi )]^T J(\pi ) = - S'(\frac{{\parallel \pi |
1384 |
> |
Thus, $ [\nabla _\pi F(\pi )]^T J(\pi ) = - S'(\frac{{\parallel |
1385 |
> |
\pi |
1386 |
|
\parallel ^2 }}{2})\pi \times \pi = 0 $. This explicit |
1387 |
|
Lie-Poisson integrator is found to be both extremely efficient and |
1388 |
|
stable. These properties can be explained by the fact the small |
1393 |
|
Splitting for Rigid Body} |
1394 |
|
|
1395 |
|
The Hamiltonian of rigid body can be separated in terms of kinetic |
1396 |
< |
energy and potential energy, |
1397 |
< |
\[ |
1398 |
< |
H = T(p,\pi ) + V(q,Q) |
1475 |
< |
\] |
1476 |
< |
The equations of motion corresponding to potential energy and |
1477 |
< |
kinetic energy are listed in the below table, |
1396 |
> |
energy and potential energy,$H = T(p,\pi ) + V(q,Q)$. The equations |
1397 |
> |
of motion corresponding to potential energy and kinetic energy are |
1398 |
> |
listed in the below table, |
1399 |
|
\begin{table} |
1400 |
< |
\caption{Equations of motion due to Potential and Kinetic Energies} |
1400 |
> |
\caption{EQUATIONS OF MOTION DUE TO POTENTIAL AND KINETIC ENERGIES} |
1401 |
|
\begin{center} |
1402 |
|
\begin{tabular}{|l|l|} |
1403 |
|
\hline |
1433 |
|
T(p,\pi ) =T^t (p) + T^r (\pi ). |
1434 |
|
\end{equation} |
1435 |
|
where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is |
1436 |
< |
defined by \ref{introEquation:rotationalKineticRB}. Therefore, the |
1437 |
< |
corresponding propagators are given by |
1436 |
> |
defined by Eq.~\ref{introEquation:rotationalKineticRB}. Therefore, |
1437 |
> |
the corresponding propagators are given by |
1438 |
|
\[ |
1439 |
|
\varphi _{\Delta t,T} = \varphi _{\Delta t,T^t } \circ \varphi |
1440 |
|
_{\Delta t,T^r }. |
1441 |
|
\] |
1442 |
|
Finally, we obtain the overall symplectic propagators for freely |
1443 |
|
moving rigid bodies |
1444 |
< |
\begin{equation} |
1445 |
< |
\begin{array}{c} |
1446 |
< |
\varphi _{\Delta t} = \varphi _{\Delta t/2,F} \circ \varphi _{\Delta t/2,\tau } \\ |
1447 |
< |
\circ \varphi _{\Delta t,T^t } \circ \varphi _{\Delta t/2,\pi _1 } \circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t,\pi _3 } \circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t/2,\pi _1 } \\ |
1527 |
< |
\circ \varphi _{\Delta t/2,\tau } \circ \varphi _{\Delta t/2,F} .\\ |
1528 |
< |
\end{array} |
1444 |
> |
\begin{eqnarray} |
1445 |
> |
\varphi _{\Delta t} &=& \varphi _{\Delta t/2,F} \circ \varphi _{\Delta t/2,\tau } \notag\\ |
1446 |
> |
& & \circ \varphi _{\Delta t,T^t } \circ \varphi _{\Delta t/2,\pi _1 } \circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t,\pi _3 } \circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t/2,\pi _1 } \notag\\ |
1447 |
> |
& & \circ \varphi _{\Delta t/2,\tau } \circ \varphi _{\Delta t/2,F} .\\ |
1448 |
|
\label{introEquation:overallRBFlowMaps} |
1449 |
< |
\end{equation} |
1449 |
> |
\end{eqnarray} |
1450 |
|
|
1451 |
|
\section{\label{introSection:langevinDynamics}Langevin Dynamics} |
1452 |
|
As an alternative to newtonian dynamics, Langevin dynamics, which |
1492 |
|
\[ |
1493 |
|
W(x) = U(x) - \sum\limits_{\alpha = 1}^N {\frac{{g_\alpha ^2 |
1494 |
|
}}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1495 |
< |
\] and combining the last two terms in Equation |
1496 |
< |
\ref{introEquation:bathGLE}, we may rewrite the Harmonic bath |
1578 |
< |
Hamiltonian as |
1495 |
> |
\] |
1496 |
> |
and combining the last two terms in Eq.~\ref{introEquation:bathGLE}, we may rewrite the Harmonic bath Hamiltonian as |
1497 |
|
\[ |
1498 |
|
H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha = 1}^N |
1499 |
|
{\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha }} + \frac{1}{2}m_\alpha |
1500 |
|
w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha }}{{m_\alpha |
1501 |
< |
w_\alpha ^2 }}x} \right)^2 } \right\}} |
1501 |
> |
w_\alpha ^2 }}x} \right)^2 } \right\}}. |
1502 |
|
\] |
1503 |
|
Since the first two terms of the new Hamiltonian depend only on the |
1504 |
|
system coordinates, we can get the equations of motion for |
1515 |
|
\frac{{g_\alpha }}{{m_\alpha w_\alpha ^2 }}x} \right). |
1516 |
|
\label{introEquation:bathMotionGLE} |
1517 |
|
\end{equation} |
1600 |
– |
|
1518 |
|
In order to derive an equation for $x$, the dynamics of the bath |
1519 |
|
variables $x_\alpha$ must be solved exactly first. As an integral |
1520 |
|
transform which is particularly useful in solving linear ordinary |
1523 |
|
differential equations into simple algebra problems which can be |
1524 |
|
solved easily. Then, by applying the inverse Laplace transform, also |
1525 |
|
known as the Bromwich integral, we can retrieve the solutions of the |
1526 |
< |
original problems. |
1527 |
< |
|
1611 |
< |
Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace |
1612 |
< |
transform of f(t) is a new function defined as |
1526 |
> |
original problems. Let $f(t)$ be a function defined on $ [0,\infty ) |
1527 |
> |
$, the Laplace transform of $f(t)$ is a new function defined as |
1528 |
|
\[ |
1529 |
|
L(f(t)) \equiv F(p) = \int_0^\infty {f(t)e^{ - pt} dt} |
1530 |
|
\] |
1531 |
|
where $p$ is real and $L$ is called the Laplace Transform |
1532 |
|
Operator. Below are some important properties of Laplace transform |
1618 |
– |
|
1533 |
|
\begin{eqnarray*} |
1534 |
|
L(x + y) & = & L(x) + L(y) \\ |
1535 |
|
L(ax) & = & aL(x) \\ |
1537 |
|
L(\ddot x)& = & p^2 L(x) - px(0) - \dot x(0) \\ |
1538 |
|
L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right)& = & G(p)H(p) \\ |
1539 |
|
\end{eqnarray*} |
1626 |
– |
|
1627 |
– |
|
1540 |
|
Applying the Laplace transform to the bath coordinates, we obtain |
1541 |
|
\begin{eqnarray*} |
1542 |
< |
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) \\ |
1543 |
< |
L(x_\alpha ) & = & \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }} \\ |
1542 |
> |
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), \\ |
1543 |
> |
L(x_\alpha ) & = & \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }}. \\ |
1544 |
|
\end{eqnarray*} |
1633 |
– |
|
1545 |
|
By the same way, the system coordinates become |
1546 |
|
\begin{eqnarray*} |
1547 |
< |
mL(\ddot x) & = & - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\ |
1548 |
< |
& & \mbox{} - \sum\limits_{\alpha = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}\frac{p}{{p^2 + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2 + \omega _\alpha ^2 }}g_\alpha x_\alpha (0) - \frac{1}{{p^2 + \omega _\alpha ^2 }}g_\alpha \dot x_\alpha (0)} \right\}} \\ |
1547 |
> |
mL(\ddot x) & = & |
1548 |
> |
- \sum\limits_{\alpha = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}\frac{p}{{p^2 + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2 + \omega _\alpha ^2 }}g_\alpha x_\alpha (0) - \frac{1}{{p^2 + \omega _\alpha ^2 }}g_\alpha \dot x_\alpha (0)} \right\}} \\ |
1549 |
> |
& & - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}}. |
1550 |
|
\end{eqnarray*} |
1639 |
– |
|
1551 |
|
With the help of some relatively important inverse Laplace |
1552 |
|
transformations: |
1553 |
|
\[ |
1557 |
|
L(1) = \frac{1}{p} \\ |
1558 |
|
\end{array} |
1559 |
|
\] |
1560 |
< |
, we obtain |
1560 |
> |
we obtain |
1561 |
|
\begin{eqnarray*} |
1562 |
|
m\ddot x & = & - \frac{{\partial W(x)}}{{\partial x}} - |
1563 |
|
\sum\limits_{\alpha = 1}^N {\left\{ {\left( { - \frac{{g_\alpha ^2 |
1605 |
|
One may notice that $R(t)$ depends only on initial conditions, which |
1606 |
|
implies it is completely deterministic within the context of a |
1607 |
|
harmonic bath. However, it is easy to verify that $R(t)$ is totally |
1608 |
< |
uncorrelated to $x$ and $\dot x$, |
1609 |
< |
\[ |
1610 |
< |
\begin{array}{l} |
1611 |
< |
\left\langle {x(t)R(t)} \right\rangle = 0, \\ |
1701 |
< |
\left\langle {\dot x(t)R(t)} \right\rangle = 0. \\ |
1702 |
< |
\end{array} |
1703 |
< |
\] |
1704 |
< |
This property is what we expect from a truly random process. As long |
1705 |
< |
as the model chosen for $R(t)$ was a gaussian distribution in |
1608 |
> |
uncorrelated to $x$ and $\dot x$,$\left\langle {x(t)R(t)} |
1609 |
> |
\right\rangle = 0, \left\langle {\dot x(t)R(t)} \right\rangle = |
1610 |
> |
0.$ This property is what we expect from a truly random process. As |
1611 |
> |
long as the model chosen for $R(t)$ was a gaussian distribution in |
1612 |
|
general, the stochastic nature of the GLE still remains. |
1707 |
– |
|
1613 |
|
%dynamic friction kernel |
1614 |
|
The convolution integral |
1615 |
|
\[ |
1624 |
|
\[ |
1625 |
|
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } = \xi _0 (x(t) - x(0)) |
1626 |
|
\] |
1627 |
< |
and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1627 |
> |
and Eq.~\ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1628 |
|
\[ |
1629 |
|
m\ddot x = - \frac{\partial }{{\partial x}}\left( {W(x) + |
1630 |
|
\frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t), |
1641 |
|
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } = 2\xi _0 \int_0^t |
1642 |
|
{\delta (t)\dot x(t - \tau )d\tau } = \xi _0 \dot x(t), |
1643 |
|
\] |
1644 |
< |
and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1644 |
> |
and Eq.~\ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1645 |
|
\begin{equation} |
1646 |
|
m\ddot x = - \frac{{\partial W(x)}}{{\partial x}} - \xi _0 \dot |
1647 |
|
x(t) + R(t) \label{introEquation:LangevinEquation} |
1654 |
|
|
1655 |
|
\subsubsection{\label{introSection:secondFluctuationDissipation}\textbf{The Second Fluctuation Dissipation Theorem}} |
1656 |
|
|
1657 |
< |
Defining a new set of coordinates, |
1657 |
> |
Defining a new set of coordinates |
1658 |
|
\[ |
1659 |
|
q_\alpha (t) = x_\alpha (t) - \frac{1}{{m_\alpha \omega _\alpha |
1660 |
< |
^2 }}x(0) |
1661 |
< |
\], |
1660 |
> |
^2 }}x(0), |
1661 |
> |
\] |
1662 |
|
we can rewrite $R(T)$ as |
1663 |
|
\[ |
1664 |
|
R(t) = \sum\limits_{\alpha = 1}^N {g_\alpha q_\alpha (t)}. |
1665 |
|
\] |
1666 |
|
And since the $q$ coordinates are harmonic oscillators, |
1762 |
– |
|
1667 |
|
\begin{eqnarray*} |
1668 |
|
\left\langle {q_\alpha ^2 } \right\rangle & = & \frac{{kT}}{{m_\alpha \omega _\alpha ^2 }} \\ |
1669 |
|
\left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle & = & \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t) \\ |
1672 |
|
& = &\sum\limits_\alpha {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t)} \\ |
1673 |
|
& = &kT\xi (t) \\ |
1674 |
|
\end{eqnarray*} |
1771 |
– |
|
1675 |
|
Thus, we recover the \emph{second fluctuation dissipation theorem} |
1676 |
|
\begin{equation} |
1677 |
|
\xi (t) = \left\langle {R(t)R(0)} \right\rangle |
1678 |
< |
\label{introEquation:secondFluctuationDissipation}. |
1678 |
> |
\label{introEquation:secondFluctuationDissipation}, |
1679 |
|
\end{equation} |
1680 |
< |
In effect, it acts as a constraint on the possible ways in which one |
1681 |
< |
can model the random force and friction kernel. |
1680 |
> |
which acts as a constraint on the possible ways in which one can |
1681 |
> |
model the random force and friction kernel. |