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+\chapter{\label{chapt:introduction}INTRODUCTION AND THEORETICAL BACKGROUND}
-–
+\section{\label{introSection:molecularDynamics}Molecular Dynamics}
-–
-–
+As a special discipline of molecular modeling, Molecular dynamics
-–
+has proven to be a powerful tool for studying the functions of
-–
+biological systems, providing structural, thermodynamic and
-–
+dynamical information.
-–
+\section{\label{introSection:classicalMechanics}Classical
+Mechanics}
+Closely related to Classical Mechanics, Molecular Dynamics
+simulations are carried out by integrating the equations of motion
+for a given system of particles. There are three fundamental ideas
-<
+behind classical mechanics. Firstly, One can determine the state of
->
+behind classical mechanics. Firstly, one can determine the state of
+a mechanical system at any time of interest; Secondly, all the
+mechanical properties of the system at that time can be determined
+by combining the knowledge of the properties of the system with the
+sufficient to predict the future behavior of the system.
+\subsection{\label{introSection:newtonian}Newtonian Mechanics}
-+
+The discovery of Newton's three laws of mechanics which govern the
-+
+motion of particles is the foundation of the classical mechanics.
-+
+Newton's first law defines a class of inertial frames. Inertial
-+
+frames are reference frames where a particle not interacting with
-+
+other bodies will move with constant speed in the same direction.
-+
+With respect to inertial frames, Newton's second law has the form
-+
+\begin{equation}
-+
+F = \frac {dp}{dt} = \frac {mdv}{dt}
-+
+\label{introEquation:newtonSecondLaw}
-+
+\end{equation}
-+
+A point mass interacting with other bodies moves with the
-+
+acceleration along the direction of the force acting on it. Let
-+
+$F_{ij}$ be the force that particle $i$ exerts on particle $j$, and
-+
+$F_{ji}$ be the force that particle $j$ exerts on particle $i$.
-+
+Newton's third law states that
-+
+\begin{equation}
-+
+F_{ij} = -F_{ji}
-+
+ \label{introEquation:newtonThirdLaw}
-+
+\end{equation}
-+
+Conservation laws of Newtonian Mechanics play very important roles
-+
+in solving mechanics problems. The linear momentum of a particle is
-+
+conserved if it is free or it experiences no force. The second
-+
+conservation theorem concerns the angular momentum of a particle.
-+
+The angular momentum $L$ of a particle with respect to an origin
-+
+from which $r$ is measured is defined to be
-+
+\begin{equation}
-+
+L \equiv r \times p \label{introEquation:angularMomentumDefinition}
-+
+\end{equation}
-+
+The torque $\tau$ with respect to the same origin is defined to be
-+
+\begin{equation}
-+
+\tau \equiv r \times F \label{introEquation:torqueDefinition}
-+
+\end{equation}
-+
+Differentiating Eq.~\ref{introEquation:angularMomentumDefinition},
-+
+\[
-+
+\dot L = \frac{d}{{dt}}(r \times p) = (\dot r \times p) + (r \times
-+
+\dot p)
-+
+\]
-+
+since
-+
+\[
-+
+\dot r \times p = \dot r \times mv = m\dot r \times \dot r \equiv 0
-+
+\]
-+
+thus,
-+
+\begin{equation}
-+
+\dot L = r \times \dot p = \tau
-+
+\end{equation}
-+
+If there are no external torques acting on a body, the angular
-+
+momentum of it is conserved. The last conservation theorem state
-+
+that if all forces are conservative, Energy
-+
+\begin{equation}E = T + V \label{introEquation:energyConservation}
-+
+\end{equation}
-+
+ is conserved. All of these conserved quantities are
-+
+important factors to determine the quality of numerical integration
-+
+schemes for rigid bodies \cite{Dullweber1997}.
-+
+\subsection{\label{introSection:lagrangian}Lagrangian Mechanics}
-<
+Newtonian Mechanics suffers from two important limitations: it
-<
+describes their motion in special cartesian coordinate systems.
-<
+Another limitation of Newtonian mechanics becomes obvious when we
-<
+try to describe systems with large numbers of particles. It becomes
-<
+very difficult to predict the properties of the system by carrying
-<
+out calculations involving the each individual interaction between
-<
+all the particles, even if we know all of the details of the
-<
+interaction. In order to overcome some of the practical difficulties
-<
+which arise in attempts to apply Newton's equation to complex
-<
+system, alternative procedures may be developed.
->
+Newtonian Mechanics suffers from two important limitations: motions
->
+can only be described in cartesian coordinate systems. Moreover, It
->
+become impossible to predict analytically the properties of the
->
+system even if we know all of the details of the interaction. In
->
+order to overcome some of the practical difficulties which arise in
->
+attempts to apply Newton's equation to complex system, approximate
->
+numerical procedures may be developed.
-<
+\subsection{\label{introSection:halmiltonPrinciple}Hamilton's
-<
+Principle}
->
+\subsubsection{\label{introSection:halmiltonPrinciple}\textbf{Hamilton's
->
+Principle}}
+Hamilton introduced the dynamical principle upon which it is
-<
+possible to base all of mechanics and, indeed, most of classical
-<
+physics. Hamilton's Principle may be stated as follow,
->
+possible to base all of mechanics and most of classical physics.
->
+Hamilton's Principle may be stated as follows,
+The actual trajectory, along which a dynamical system may move from
+one point to another within a specified time, is derived by finding
+\end{equation}
+For simple mechanical systems, where the forces acting on the
-<
+different part are derivable from a potential and the velocities are
-<
+small compared with that of light, the Lagrangian function $L$ can
-<
+be define as the difference between the kinetic energy of the system
-<
+and its potential energy,
->
+different parts are derivable from a potential, the Lagrangian
->
+function $L$ can be defined as the difference between the kinetic
->
+energy of the system and its potential energy,
+\begin{equation}
+L \equiv K - U = L(q_i ,\dot q_i ) ,
+\label{introEquation:lagrangianDef}
+\label{introEquation:halmitonianPrinciple2}
+\end{equation}
-<
+\subsection{\label{introSection:equationOfMotionLagrangian}The
-<
+Equations of Motion in Lagrangian Mechanics}
->
+\subsubsection{\label{introSection:equationOfMotionLagrangian}\textbf{The
->
+Equations of Motion in Lagrangian Mechanics}}
-<
+for a holonomic system of $f$ degrees of freedom, the equations of
-<
+motion in the Lagrangian form is
->
+For a system of $f$ degrees of freedom, the equations of motion in
->
+the Lagrangian form is
+\begin{equation}
+\frac{d}{{dt}}\frac{{\partial L}}{{\partial \dot q_i }} -
+\frac{{\partial L}}{{\partial q_i }} = 0,{\rm{ }}i = 1, \ldots,f
+Arising from Lagrangian Mechanics, Hamiltonian Mechanics was
+introduced by William Rowan Hamilton in 1833 as a re-formulation of
+classical mechanics. If the potential energy of a system is
-<
+independent of generalized velocities, the generalized momenta can
-<
+be defined as
->
+independent of velocities, the momenta can be defined as
+\begin{equation}
+p_i = \frac{\partial L}{\partial \dot q_i}
+\label{introEquation:generalizedMomenta}
+By identifying the coefficients of $dq_k$, $dp_k$ and dt, we can
+find
+\begin{equation}
-<
+\frac{{\partial H}}{{\partial p_k }} = q_k
->
+\frac{{\partial H}}{{\partial p_k }} = \dot {q_k}
+\label{introEquation:motionHamiltonianCoordinate}
+\end{equation}
+\begin{equation}
-<
+\frac{{\partial H}}{{\partial q_k }} =  - p_k
->
+\frac{{\partial H}}{{\partial q_k }} =  - \dot {p_k}
+\label{introEquation:motionHamiltonianMomentum}
+\end{equation}
+and
+Eq.~\ref{introEquation:motionHamiltonianCoordinate} and
+Eq.~\ref{introEquation:motionHamiltonianMomentum} are Hamilton's
+equation of motion. Due to their symmetrical formula, they are also
-<
+known as the canonical equations of motions.
->
+known as the canonical equations of motions \cite{Goldstein2001}.
+An important difference between Lagrangian approach and the
+Hamiltonian approach is that the Lagrangian is considered to be a
-<
+function of the generalized velocities $\dot q_i$ and the
-<
+generalized coordinates $q_i$, while the Hamiltonian is considered
-<
+to be a function of the generalized momenta $p_i$ and the conjugate
-<
+generalized coordinate $q_i$. Hamiltonian Mechanics is more
-<
+appropriate for application to statistical mechanics and quantum
-<
+mechanics, since it treats the coordinate and its time derivative as
-<
+independent variables and it only works with 1st-order differential
-<
+equations.
->
+function of the generalized velocities $\dot q_i$ and coordinates
->
+$q_i$, while the Hamiltonian is considered to be a function of the
->
+generalized momenta $p_i$ and the conjugate coordinates $q_i$.
->
+Hamiltonian Mechanics is more appropriate for application to
->
+statistical mechanics and quantum mechanics, since it treats the
->
+coordinate and its time derivative as independent variables and it
->
+only works with 1st-order differential equations\cite{Marion1990}.
-<
+\subsection{\label{introSection:poissonBrackets}Poisson Brackets}
->
+In Newtonian Mechanics, a system described by conservative forces
->
+conserves the total energy \ref{introEquation:energyConservation}.
->
+It follows that Hamilton's equations of motion conserve the total
->
+Hamiltonian.
->
+\begin{equation}
->
+\frac{{dH}}{{dt}} = \sum\limits_i {\left( {\frac{{\partial
->
+H}}{{\partial q_i }}\dot q_i  + \frac{{\partial H}}{{\partial p_i
->
+}}\dot p_i } \right)}  = \sum\limits_i {\left( {\frac{{\partial
->
+H}}{{\partial q_i }}\frac{{\partial H}}{{\partial p_i }} -
->
+\frac{{\partial H}}{{\partial p_i }}\frac{{\partial H}}{{\partial
->
+q_i }}} \right) = 0} \label{introEquation:conserveHalmitonian}
->
+\end{equation}
-–
+\subsection{\label{introSection:canonicalTransformation}Canonical
-–
+Transformation}
-–
+\section{\label{introSection:statisticalMechanics}Statistical
+Mechanics}
-<
+The thermodynamic behaviors and properties  of Molecular Dynamics
->
+The thermodynamic behaviors and properties of Molecular Dynamics
+simulation are governed by the principle of Statistical Mechanics.
+The following section will give a brief introduction to some of the
-<
+Statistical Mechanics concepts presented in this dissertation.
->
+Statistical Mechanics concepts and theorem presented in this
->
+dissertation.
-<
+\subsection{\label{introSection::ensemble}Ensemble}
->
+\subsection{\label{introSection:ensemble}Phase Space and Ensemble}
-+
+Mathematically, phase space is the space which represents all
-+
+possible states. Each possible state of the system corresponds to
-+
+one unique point in the phase space. For mechanical systems, the
-+
+phase space usually consists of all possible values of position and
-+
+momentum variables. Consider a dynamic system of $f$ particles in a
-+
+cartesian space, where each of the $6f$ coordinates and momenta is
-+
+assigned to one of $6f$ mutually orthogonal axes, the phase space of
-+
+this system is a $6f$ dimensional space. A point, $x = (q_1 , \ldots
-+
+,q_f ,p_1 , \ldots ,p_f )$, with a unique set of values of $6f$
-+
+coordinates and momenta is a phase space vector.
-+
-+
+%%%fix me
-+
+A microscopic state or microstate of a classical system is
-+
+specification of the complete phase space vector of a system at any
-+
+instant in time. An ensemble is defined as a collection of systems
-+
+sharing one or more macroscopic characteristics but each being in a
-+
+unique microstate. The complete ensemble is specified by giving all
-+
+systems or microstates consistent with the common macroscopic
-+
+characteristics of the ensemble. Although the state of each
-+
+individual system in the ensemble could be precisely described at
-+
+any instance in time by a suitable phase space vector, when using
-+
+ensembles for statistical purposes, there is no need to maintain
-+
+distinctions between individual systems, since the numbers of
-+
+systems at any time in the different states which correspond to
-+
+different regions of the phase space are more interesting. Moreover,
-+
+in the point of view of statistical mechanics, one would prefer to
-+
+use ensembles containing a large enough population of separate
-+
+members so that the numbers of systems in such different states can
-+
+be regarded as changing continuously as we traverse different
-+
+regions of the phase space. The condition of an ensemble at any time
-+
+can be regarded as appropriately specified by the density $\rho$
-+
+with which representative points are distributed over the phase
-+
+space. The density distribution for an ensemble with $f$ degrees of
-+
+freedom is defined as,
-+
+\begin{equation}
-+
+\rho  = \rho (q_1 , \ldots ,q_f ,p_1 , \ldots ,p_f ,t).
-+
+\label{introEquation:densityDistribution}
-+
+\end{equation}
-+
+Governed by the principles of mechanics, the phase points change
-+
+their locations which would change the density at any time at phase
-+
+space. Hence, the density distribution is also to be taken as a
-+
+function of the time.
-+
-+
+The number of systems $\delta N$ at time $t$ can be determined by,
-+
+\begin{equation}
-+
+\delta N = \rho (q,p,t)dq_1  \ldots dq_f dp_1  \ldots dp_f.
-+
+\label{introEquation:deltaN}
-+
+\end{equation}
-+
+Assuming a large enough population of systems, we can sufficiently
-+
+approximate $\delta N$ without introducing discontinuity when we go
-+
+from one region in the phase space to another. By integrating over
-+
+the whole phase space,
-+
+\begin{equation}
-+
+N = \int { \ldots \int {\rho (q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f
-+
+\label{introEquation:totalNumberSystem}
-+
+\end{equation}
-+
+gives us an expression for the total number of the systems. Hence,
-+
+the probability per unit in the phase space can be obtained by,
-+
+\begin{equation}
-+
+\frac{{\rho (q,p,t)}}{N} = \frac{{\rho (q,p,t)}}{{\int { \ldots \int
-+
+{\rho (q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}.
-+
+\label{introEquation:unitProbability}
-+
+\end{equation}
-+
+With the help of Eq.~\ref{introEquation:unitProbability} and the
-+
+knowledge of the system, it is possible to calculate the average
-+
+value of any desired quantity which depends on the coordinates and
-+
+momenta of the system. Even when the dynamics of the real system is
-+
+complex, or stochastic, or even discontinuous, the average
-+
+properties of the ensemble of possibilities as a whole remaining
-+
+well defined. For a classical system in thermal equilibrium with its
-+
+environment, the ensemble average of a mechanical quantity, $\langle
-+
+A(q , p) \rangle_t$, takes the form of an integral over the phase
-+
+space of the system,
-+
+\begin{equation}
-+
+\langle  A(q , p) \rangle_t = \frac{{\int { \ldots \int {A(q,p)\rho
-+
+(q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}{{\int { \ldots \int {\rho
-+
+(q,p,t)dq_1 } ...dq_f dp_1 } ...dp_f }}
-+
+\label{introEquation:ensembelAverage}
-+
+\end{equation}
-+
-+
+There are several different types of ensembles with different
-+
+statistical characteristics. As a function of macroscopic
-+
+parameters, such as temperature \textit{etc}, the partition function
-+
+can be used to describe the statistical properties of a system in
-+
+thermodynamic equilibrium.
-+
-+
+As an ensemble of systems, each of which is known to be thermally
-+
+isolated and conserve energy, the Microcanonical ensemble (NVE) has
-+
+a partition function like,
-+
+\begin{equation}
-+
+\Omega (N,V,E) = e^{\beta TS} \label{introEquation:NVEPartition}.
-+
+\end{equation}
-+
+A canonical ensemble (NVT)is an ensemble of systems, each of which
-+
+can share its energy with a large heat reservoir. The distribution
-+
+of the total energy amongst the possible dynamical states is given
-+
+by the partition function,
-+
+\begin{equation}
-+
+\Omega (N,V,T) = e^{ - \beta A}
-+
+\label{introEquation:NVTPartition}
-+
+\end{equation}
-+
+Here, $A$ is the Helmholtz free energy which is defined as $ A = U -
-+
+TS$. Since most experiments are carried out under constant pressure
-+
+condition, the isothermal-isobaric ensemble (NPT) plays a very
-+
+important role in molecular simulations. The isothermal-isobaric
-+
+ensemble allow the system to exchange energy with a heat bath of
-+
+temperature $T$ and to change the volume as well. Its partition
-+
+function is given as
-+
+\begin{equation}
-+
+\Delta (N,P,T) =  - e^{\beta G}.
-+
+ \label{introEquation:NPTPartition}
-+
+\end{equation}
-+
+Here, $G = U - TS + PV$, and $G$ is called Gibbs free energy.
-+
-+
+\subsection{\label{introSection:liouville}Liouville's theorem}
-+
-+
+Liouville's theorem is the foundation on which statistical mechanics
-+
+rests. It describes the time evolution of the phase space
-+
+distribution function. In order to calculate the rate of change of
-+
+$\rho$, we begin from Eq.~\ref{introEquation:deltaN}. If we consider
-+
+the two faces perpendicular to the $q_1$ axis, which are located at
-+
+$q_1$ and $q_1 + \delta q_1$, the number of phase points leaving the
-+
+opposite face is given by the expression,
-+
+\begin{equation}
-+
+\left( {\rho  + \frac{{\partial \rho }}{{\partial q_1 }}\delta q_1 }
-+
+\right)\left( {\dot q_1  + \frac{{\partial \dot q_1 }}{{\partial q_1
-+
+}}\delta q_1 } \right)\delta q_2  \ldots \delta q_f \delta p_1
-+
+\ldots \delta p_f .
-+
+\end{equation}
-+
+Summing all over the phase space, we obtain
-+
+\begin{equation}
-+
+\frac{{d(\delta N)}}{{dt}} =  - \sum\limits_{i = 1}^f {\left[ {\rho
-+
+\left( {\frac{{\partial \dot q_i }}{{\partial q_i }} +
-+
+\frac{{\partial \dot p_i }}{{\partial p_i }}} \right) + \left(
-+
+{\frac{{\partial \rho }}{{\partial q_i }}\dot q_i  + \frac{{\partial
-+
+\rho }}{{\partial p_i }}\dot p_i } \right)} \right]} \delta q_1
-+
+\ldots \delta q_f \delta p_1  \ldots \delta p_f .
-+
+\end{equation}
-+
+Differentiating the equations of motion in Hamiltonian formalism
-+
+(\ref{introEquation:motionHamiltonianCoordinate},
-+
+\ref{introEquation:motionHamiltonianMomentum}), we can show,
-+
+\begin{equation}
-+
+\sum\limits_i {\left( {\frac{{\partial \dot q_i }}{{\partial q_i }}
-+
++ \frac{{\partial \dot p_i }}{{\partial p_i }}} \right)}  = 0 ,
-+
+\end{equation}
-+
+which cancels the first terms of the right hand side. Furthermore,
-+
+dividing $ \delta q_1  \ldots \delta q_f \delta p_1  \ldots \delta
-+
+p_f $ in both sides, we can write out Liouville's theorem in a
-+
+simple form,
-+
+\begin{equation}
-+
+\frac{{\partial \rho }}{{\partial t}} + \sum\limits_{i = 1}^f
-+
+{\left( {\frac{{\partial \rho }}{{\partial q_i }}\dot q_i  +
-+
+\frac{{\partial \rho }}{{\partial p_i }}\dot p_i } \right)}  = 0 .
-+
+\label{introEquation:liouvilleTheorem}
-+
+\end{equation}
-+
-+
+Liouville's theorem states that the distribution function is
-+
+constant along any trajectory in phase space. In classical
-+
+statistical mechanics, since the number of members in an ensemble is
-+
+huge and constant, we can assume the local density has no reason
-+
+(other than classical mechanics) to change,
-+
+\begin{equation}
-+
+\frac{{\partial \rho }}{{\partial t}} = 0.
-+
+\label{introEquation:stationary}
-+
+\end{equation}
-+
+In such stationary system, the density of distribution $\rho$ can be
-+
+connected to the Hamiltonian $H$ through Maxwell-Boltzmann
-+
+distribution,
-+
+\begin{equation}
-+
+\rho  \propto e^{ - \beta H}
-+
+\label{introEquation:densityAndHamiltonian}
-+
+\end{equation}
-+
-+
+\subsubsection{\label{introSection:phaseSpaceConservation}\textbf{Conservation of Phase Space}}
-+
+Lets consider a region in the phase space,
-+
+\begin{equation}
-+
+\delta v = \int { \ldots \int {dq_1 } ...dq_f dp_1 } ..dp_f .
-+
+\end{equation}
-+
+If this region is small enough, the density $\rho$ can be regarded
-+
+as uniform over the whole integral. Thus, the number of phase points
-+
+inside this region is given by,
-+
+\begin{equation}
-+
+\delta N = \rho \delta v = \rho \int { \ldots \int {dq_1 } ...dq_f
-+
+dp_1 } ..dp_f.
-+
+\end{equation}
-+
-+
+\begin{equation}
-+
+\frac{{d(\delta N)}}{{dt}} = \frac{{d\rho }}{{dt}}\delta v + \rho
-+
+\frac{d}{{dt}}(\delta v) = 0.
-+
+\end{equation}
-+
+With the help of stationary assumption
-+
+(\ref{introEquation:stationary}), we obtain the principle of the
-+
+\emph{conservation of volume in phase space},
-+
+\begin{equation}
-+
+\frac{d}{{dt}}(\delta v) = \frac{d}{{dt}}\int { \ldots \int {dq_1 }
-+
+...dq_f dp_1 } ..dp_f  = 0.
-+
+\label{introEquation:volumePreserving}
-+
+\end{equation}
-+
-+
+\subsubsection{\label{introSection:liouvilleInOtherForms}\textbf{Liouville's Theorem in Other Forms}}
-+
-+
+Liouville's theorem can be expresses in a variety of different forms
-+
+which are convenient within different contexts. For any two function
-+
+$F$ and $G$ of the coordinates and momenta of a system, the Poisson
-+
+bracket ${F, G}$ is defined as
-+
+\begin{equation}
-+
+\left\{ {F,G} \right\} = \sum\limits_i {\left( {\frac{{\partial
-+
+F}}{{\partial q_i }}\frac{{\partial G}}{{\partial p_i }} -
-+
+\frac{{\partial F}}{{\partial p_i }}\frac{{\partial G}}{{\partial
-+
+q_i }}} \right)}.
-+
+\label{introEquation:poissonBracket}
-+
+\end{equation}
-+
+Substituting equations of motion in Hamiltonian formalism(
-+
+Eq.~\ref{introEquation:motionHamiltonianCoordinate} ,
-+
+Eq.~\ref{introEquation:motionHamiltonianMomentum} ) into
-+
+(Eq.~\ref{introEquation:liouvilleTheorem}), we can rewrite
-+
+Liouville's theorem using Poisson bracket notion,
-+
+\begin{equation}
-+
+\left( {\frac{{\partial \rho }}{{\partial t}}} \right) =  - \left\{
-+
+{\rho ,H} \right\}.
-+
+\label{introEquation:liouvilleTheromInPoissin}
-+
+\end{equation}
-+
+Moreover, the Liouville operator is defined as
-+
+\begin{equation}
-+
+iL = \sum\limits_{i = 1}^f {\left( {\frac{{\partial H}}{{\partial
-+
+p_i }}\frac{\partial }{{\partial q_i }} - \frac{{\partial
-+
+H}}{{\partial q_i }}\frac{\partial }{{\partial p_i }}} \right)}
-+
+\label{introEquation:liouvilleOperator}
-+
+\end{equation}
-+
+In terms of Liouville operator, Liouville's equation can also be
-+
+expressed as
-+
+\begin{equation}
-+
+\left( {\frac{{\partial \rho }}{{\partial t}}} \right) =  - iL\rho
-+
+\label{introEquation:liouvilleTheoremInOperator}
-+
+\end{equation}
-+
+\subsection{\label{introSection:ergodic}The Ergodic Hypothesis}
-+
+Various thermodynamic properties can be calculated from Molecular
-+
+Dynamics simulation. By comparing experimental values with the
-+
+calculated properties, one can determine the accuracy of the
-+
+simulation and the quality of the underlying model. However, both
-+
+experiments and computer simulations are usually performed during a
-+
+certain time interval and the measurements are averaged over a
-+
+period of them which is different from the average behavior of
-+
+many-body system in Statistical Mechanics. Fortunately, the Ergodic
-+
+Hypothesis makes a connection between time average and the ensemble
-+
+average. It states that the time average and average over the
-+
+statistical ensemble are identical \cite{Frenkel1996, Leach2001}.
-+
+\begin{equation}
-+
+\langle A(q , p) \rangle_t = \mathop {\lim }\limits_{t \to \infty }
-+
+\frac{1}{t}\int\limits_0^t {A(q(t),p(t))dt = \int\limits_\Gamma
-+
+{A(q(t),p(t))} } \rho (q(t), p(t)) dqdp
-+
+\end{equation}
-+
+where $\langle  A(q , p) \rangle_t$ is an equilibrium value of a
-+
+physical quantity and $\rho (p(t), q(t))$ is the equilibrium
-+
+distribution function. If an observation is averaged over a
-+
+sufficiently long time (longer than relaxation time), all accessible
-+
+microstates in phase space are assumed to be equally probed, giving
-+
+a properly weighted statistical average. This allows the researcher
-+
+freedom of choice when deciding how best to measure a given
-+
+observable. In case an ensemble averaged approach sounds most
-+
+reasonable, the Monte Carlo techniques\cite{Metropolis1949} can be
-+
+utilized. Or if the system lends itself to a time averaging
-+
+approach, the Molecular Dynamics techniques in
-+
+Sec.~\ref{introSection:molecularDynamics} will be the best
-+
+choice\cite{Frenkel1996}.
-+
-+
+\section{\label{introSection:geometricIntegratos}Geometric Integrators}
-+
+A variety of numerical integrators have been proposed to simulate
-+
+the motions of atoms in MD simulation. They usually begin with
-+
+initial conditionals and move the objects in the direction governed
-+
+by the differential equations. However, most of them ignore the
-+
+hidden physical laws contained within the equations. Since 1990,
-+
+geometric integrators, which preserve various phase-flow invariants
-+
+such as symplectic structure, volume and time reversal symmetry, are
-+
+developed to address this issue\cite{Dullweber1997, McLachlan1998,
-+
+Leimkuhler1999}. The velocity Verlet method, which happens to be a
-+
+simple example of symplectic integrator, continues to gain
-+
+popularity in the molecular dynamics community. This fact can be
-+
+partly explained by its geometric nature.
-+
-+
+\subsection{\label{introSection:symplecticManifold}Symplectic Manifolds}
-+
+A \emph{manifold} is an abstract mathematical space. It looks
-+
+locally like Euclidean space, but when viewed globally, it may have
-+
+more complicated structure. A good example of manifold is the
-+
+surface of Earth. It seems to be flat locally, but it is round if
-+
+viewed as a whole. A \emph{differentiable manifold} (also known as
-+
+\emph{smooth manifold}) is a manifold on which it is possible to
-+
+apply calculus on \emph{differentiable manifold}. A \emph{symplectic
-+
+manifold} is defined as a pair $(M, \omega)$ which consists of a
-+
+\emph{differentiable manifold} $M$ and a close, non-degenerated,
-+
+bilinear symplectic form, $\omega$. A symplectic form on a vector
-+
+space $V$ is a function $\omega(x, y)$ which satisfies
-+
+$\omega(\lambda_1x_1+\lambda_2x_2, y) = \lambda_1\omega(x_1, y)+
-+
+\lambda_2\omega(x_2, y)$, $\omega(x, y) = - \omega(y, x)$ and
-+
+$\omega(x, x) = 0$. The cross product operation in vector field is
-+
+an example of symplectic form.
-+
-+
+One of the motivations to study \emph{symplectic manifolds} in
-+
+Hamiltonian Mechanics is that a symplectic manifold can represent
-+
+all possible configurations of the system and the phase space of the
-+
+system can be described by it's cotangent bundle. Every symplectic
-+
+manifold is even dimensional. For instance, in Hamilton equations,
-+
+coordinate and momentum always appear in pairs.
-+
-+
+\subsection{\label{introSection:ODE}Ordinary Differential Equations}
-+
-+
+For an ordinary differential system defined as
-+
+\begin{equation}
-+
+\dot x = f(x)
-+
+\end{equation}
-+
+where $x = x(q,p)^T$, this system is a canonical Hamiltonian, if
-+
+\begin{equation}
-+
+f(r) = J\nabla _x H(r).
-+
+\end{equation}
-+
+$H = H (q, p)$ is Hamiltonian function and $J$ is the skew-symmetric
-+
+matrix
-+
+\begin{equation}
-+
+J = \left( {\begin{array}{*{20}c}
-+
+& I  \\
-+
+   { - I} & 0  \\
-+
+\end{array}} \right)
-+
+\label{introEquation:canonicalMatrix}
-+
+\end{equation}
-+
+where $I$ is an identity matrix. Using this notation, Hamiltonian
-+
+system can be rewritten as,
-+
+\begin{equation}
-+
+\frac{d}{{dt}}x = J\nabla _x H(x)
-+
+\label{introEquation:compactHamiltonian}
-+
+\end{equation}In this case, $f$ is
-+
+called a \emph{Hamiltonian vector field}.
-+
-+
+Another generalization of Hamiltonian dynamics is Poisson
-+
+Dynamics\cite{Olver1986},
-+
+\begin{equation}
-+
+\dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian}
-+
+\end{equation}
-+
+The most obvious change being that matrix $J$ now depends on $x$.
-+
-+
+\subsection{\label{introSection:exactFlow}Exact Flow}
-+
-+
+Let $x(t)$ be the exact solution of the ODE system,
-+
+\begin{equation}
-+
+\frac{{dx}}{{dt}} = f(x) \label{introEquation:ODE}
-+
+\end{equation}
-+
+The exact flow(solution) $\varphi_\tau$ is defined by
-+
+\[
-+
+x(t+\tau) =\varphi_\tau(x(t))
-+
+\]
-+
+where $\tau$ is a fixed time step and $\varphi$ is a map from phase
-+
+space to itself. The flow has the continuous group property,
-+
+\begin{equation}
-+
+\varphi _{\tau _1 }  \circ \varphi _{\tau _2 }  = \varphi _{\tau _1
-+
++ \tau _2 } .
-+
+\end{equation}
-+
+In particular,
-+
+\begin{equation}
-+
+\varphi _\tau   \circ \varphi _{ - \tau }  = I
-+
+\end{equation}
-+
+Therefore, the exact flow is self-adjoint,
-+
+\begin{equation}
-+
+\varphi _\tau   = \varphi _{ - \tau }^{ - 1}.
-+
+\end{equation}
-+
+The exact flow can also be written in terms of the of an operator,
-+
+\begin{equation}
-+
+\varphi _\tau  (x) = e^{\tau \sum\limits_i {f_i (x)\frac{\partial
-+
+}{{\partial x_i }}} } (x) \equiv \exp (\tau f)(x).
-+
+\label{introEquation:exponentialOperator}
-+
+\end{equation}
-+
-+
+In most cases, it is not easy to find the exact flow $\varphi_\tau$.
-+
+Instead, we use an approximate map, $\psi_\tau$, which is usually
-+
+called integrator. The order of an integrator $\psi_\tau$ is $p$, if
-+
+the Taylor series of $\psi_\tau$ agree to order $p$,
-+
+\begin{equation}
-+
+\psi_\tau(x) = x + \tau f(x) + O(\tau^{p+1})
-+
+\end{equation}
-+
-+
+\subsection{\label{introSection:geometricProperties}Geometric Properties}
-+
-+
+The hidden geometric properties\cite{Budd1999, Marsden1998} of an
-+
+ODE and its flow play important roles in numerical studies. Many of
-+
+them can be found in systems which occur naturally in applications.
-+
-+
+Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is
-+
+a \emph{symplectic} flow if it satisfies,
-+
+\begin{equation}
-+
+{\varphi '}^T J \varphi ' = J.
-+
+\end{equation}
-+
+According to Liouville's theorem, the symplectic volume is invariant
-+
+under a Hamiltonian flow, which is the basis for classical
-+
+statistical mechanics. Furthermore, the flow of a Hamiltonian vector
-+
+field on a symplectic manifold can be shown to be a
-+
+symplectomorphism. As to the Poisson system,
-+
+\begin{equation}
-+
+{\varphi '}^T J \varphi ' = J \circ \varphi
-+
+\end{equation}
-+
+is the property that must be preserved by the integrator.
-+
-+
+It is possible to construct a \emph{volume-preserving} flow for a
-+
+source free ODE ($ \nabla \cdot f = 0 $), if the flow satisfies $
-+
+\det d\varphi  = 1$. One can show easily that a symplectic flow will
-+
+be volume-preserving.
-+
-+
+Changing the variables $y = h(x)$ in an ODE
-+
+(Eq.~\ref{introEquation:ODE}) will result in a new system,
-+
+\[
-+
+\dot y = \tilde f(y) = ((dh \cdot f)h^{ - 1} )(y).
-+
+\]
-+
+The vector filed $f$ has reversing symmetry $h$ if $f = - \tilde f$.
-+
+In other words, the flow of this vector field is reversible if and
-+
+only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $.
-+
-+
+A \emph{first integral}, or conserved quantity of a general
-+
+differential function is a function $ G:R^{2d}  \to R^d $ which is
-+
+constant for all solutions of the ODE $\frac{{dx}}{{dt}} = f(x)$ ,
-+
+\[
-+
+\frac{{dG(x(t))}}{{dt}} = 0.
-+
+\]
-+
+Using chain rule, one may obtain,
-+
+\[
-+
+\sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \bullet \nabla G,
-+
+\]
-+
+which is the condition for conserving \emph{first integral}. For a
-+
+canonical Hamiltonian system, the time evolution of an arbitrary
-+
+smooth function $G$ is given by,
-+
-+
+\begin{eqnarray}
-+
+\frac{{dG(x(t))}}{{dt}} & = & [\nabla _x G(x(t))]^T \dot x(t) \\
-+
+                        & = & [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\
-+
+\label{introEquation:firstIntegral1}
-+
+\end{eqnarray}
-+
-+
-+
+Using poisson bracket notion, Equation
-+
+\ref{introEquation:firstIntegral1} can be rewritten as
-+
+\[
-+
+\frac{d}{{dt}}G(x(t)) = \left\{ {G,H} \right\}(x(t)).
-+
+\]
-+
+Therefore, the sufficient condition for $G$ to be the \emph{first
-+
+integral} of a Hamiltonian system is
-+
+\[
-+
+\left\{ {G,H} \right\} = 0.
-+
+\]
-+
+As well known, the Hamiltonian (or energy) H of a Hamiltonian system
-+
+is a \emph{first integral}, which is due to the fact $\{ H,H\}  =
-+
+$.
-+
-+
+When designing any numerical methods, one should always try to
-+
+preserve the structural properties of the original ODE and its flow.
-+
-+
+\subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods}
-+
+A lot of well established and very effective numerical methods have
-+
+been successful precisely because of their symplecticities even
-+
+though this fact was not recognized when they were first
-+
+constructed. The most famous example is the Verlet-leapfrog method
-+
+in molecular dynamics. In general, symplectic integrators can be
-+
+constructed using one of four different methods.
-+
+\begin{enumerate}
-+
+\item Generating functions
-+
+\item Variational methods
-+
+\item Runge-Kutta methods
-+
+\item Splitting methods
-+
+\end{enumerate}
-+
-+
+Generating function\cite{Channell1990} tends to lead to methods
-+
+which are cumbersome and difficult to use. In dissipative systems,
-+
+variational methods can capture the decay of energy
-+
+accurately\cite{Kane2000}. Since their geometrically unstable nature
-+
+against non-Hamiltonian perturbations, ordinary implicit Runge-Kutta
-+
+methods are not suitable for Hamiltonian system. Recently, various
-+
+high-order explicit Runge-Kutta methods
-+
+\cite{Owren1992,Chen2003}have been developed to overcome this
-+
+instability. However, due to computational penalty involved in
-+
+implementing the Runge-Kutta methods, they have not attracted much
-+
+attention from the Molecular Dynamics community. Instead, splitting
-+
+methods have been widely accepted since they exploit natural
-+
+decompositions of the system\cite{Tuckerman1992, McLachlan1998}.
-+
-+
+\subsubsection{\label{introSection:splittingMethod}\textbf{Splitting Methods}}
-+
-+
+The main idea behind splitting methods is to decompose the discrete
-+
+$\varphi_h$ as a composition of simpler flows,
-+
+\begin{equation}
-+
+\varphi _h  = \varphi _{h_1 }  \circ \varphi _{h_2 }  \ldots  \circ
-+
+\varphi _{h_n }
-+
+\label{introEquation:FlowDecomposition}
-+
+\end{equation}
-+
+where each of the sub-flow is chosen such that each represent a
-+
+simpler integration of the system.
-+
-+
+Suppose that a Hamiltonian system takes the form,
-+
+\[
-+
+H = H_1 + H_2.
-+
+\]
-+
+Here, $H_1$ and $H_2$ may represent different physical processes of
-+
+the system. For instance, they may relate to kinetic and potential
-+
+energy respectively, which is a natural decomposition of the
-+
+problem. If $H_1$ and $H_2$ can be integrated using exact flows
-+
+$\varphi_1(t)$ and $\varphi_2(t)$, respectively, a simple first
-+
+order expression is then given by the Lie-Trotter formula
-+
+\begin{equation}
-+
+\varphi _h  = \varphi _{1,h}  \circ \varphi _{2,h},
-+
+\label{introEquation:firstOrderSplitting}
-+
+\end{equation}
-+
+where $\varphi _h$ is the result of applying the corresponding
-+
+continuous $\varphi _i$ over a time $h$. By definition, as
-+
+$\varphi_i(t)$ is the exact solution of a Hamiltonian system, it
-+
+must follow that each operator $\varphi_i(t)$ is a symplectic map.
-+
+It is easy to show that any composition of symplectic flows yields a
-+
+symplectic map,
-+
+\begin{equation}
-+
+(\varphi '\phi ')^T J\varphi '\phi ' = \phi '^T \varphi '^T J\varphi
-+
+'\phi ' = \phi '^T J\phi ' = J,
-+
+ \label{introEquation:SymplecticFlowComposition}
-+
+\end{equation}
-+
+where $\phi$ and $\psi$ both are symplectic maps. Thus operator
-+
+splitting in this context automatically generates a symplectic map.
-+
-+
+The Lie-Trotter splitting(\ref{introEquation:firstOrderSplitting})
-+
+introduces local errors proportional to $h^2$, while Strang
-+
+splitting gives a second-order decomposition,
-+
+\begin{equation}
-+
+\varphi _h  = \varphi _{1,h/2}  \circ \varphi _{2,h}  \circ \varphi
-+
+_{1,h/2} , \label{introEquation:secondOrderSplitting}
-+
+\end{equation}
-+
+which has a local error proportional to $h^3$. The Sprang
-+
+splitting's popularity in molecular simulation community attribute
-+
+to its symmetric property,
-+
+\begin{equation}
-+
+\varphi _h^{ - 1} = \varphi _{ - h}.
-+
+\label{introEquation:timeReversible}
-+
+\end{equation},appendixFig:architecture
-+
-+
+\subsubsection{\label{introSection:exampleSplittingMethod}\textbf{Examples of the Splitting Method}}
-+
+The classical equation for a system consisting of interacting
-+
+particles can be written in Hamiltonian form,
-+
+\[
-+
+H = T + V
-+
+\]
-+
+where $T$ is the kinetic energy and $V$ is the potential energy.
-+
+Setting $H_1 = T, H_2 = V$ and applying the Strang splitting, one
-+
+obtains the following:
-+
+\begin{align}
-+
+q(\Delta t) &= q(0) + \dot{q}(0)\Delta t +
-+
+    \frac{F[q(0)]}{m}\frac{\Delta t^2}{2}, %
-+
+\label{introEquation:Lp10a} \\%
-+
+%
-+
+\dot{q}(\Delta t) &= \dot{q}(0) + \frac{\Delta t}{2m}
-+
+    \biggl [F[q(0)] + F[q(\Delta t)] \biggr]. %
-+
+\label{introEquation:Lp10b}
-+
+\end{align}
-+
+where $F(t)$ is the force at time $t$. This integration scheme is
-+
+known as \emph{velocity verlet} which is
-+
+symplectic(\ref{introEquation:SymplecticFlowComposition}),
-+
+time-reversible(\ref{introEquation:timeReversible}) and
-+
+volume-preserving (\ref{introEquation:volumePreserving}). These
-+
+geometric properties attribute to its long-time stability and its
-+
+popularity in the community. However, the most commonly used
-+
+velocity verlet integration scheme is written as below,
-+
+\begin{align}
-+
+\dot{q}\biggl (\frac{\Delta t}{2}\biggr ) &=
-+
+    \dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)], \label{introEquation:Lp9a}\\%
-+
+%
-+
+q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr ),%
-+
+    \label{introEquation:Lp9b}\\%
-+
+%
-+
+\dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) +
-+
+    \frac{\Delta t}{2m}\, F[q(t)]. \label{introEquation:Lp9c}
-+
+\end{align}
-+
+From the preceding splitting, one can see that the integration of
-+
+the equations of motion would follow:
-+
+\begin{enumerate}
-+
+\item calculate the velocities at the half step, $\frac{\Delta t}{2}$, from the forces calculated at the initial position.
-+
-+
+\item Use the half step velocities to move positions one whole step, $\Delta t$.
-+
-+
+\item Evaluate the forces at the new positions, $\mathbf{q}(\Delta t)$, and use the new forces to complete the velocity move.
-+
-+
+\item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values.
-+
+\end{enumerate}
-+
-+
+By simply switching the order of the propagators in the splitting
-+
+and composing a new integrator, the \emph{position verlet}
-+
+integrator, can be generated,
-+
+\begin{align}
-+
+\dot q(\Delta t) &= \dot q(0) + \Delta tF(q(0))\left[ {q(0) +
-+
+\frac{{\Delta t}}{{2m}}\dot q(0)} \right], %
-+
+\label{introEquation:positionVerlet1} \\%
-+
+%
-+
+q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
-+
+q(\Delta t)} \right]. %
-+
+ \label{introEquation:positionVerlet2}
-+
+\end{align}
-+
-+
+\subsubsection{\label{introSection:errorAnalysis}\textbf{Error Analysis and Higher Order Methods}}
-+
-+
+The Baker-Campbell-Hausdorff formula can be used to determine the
-+
+local error of splitting method in terms of the commutator of the
-+
+operators(\ref{introEquation:exponentialOperator}) associated with
-+
+the sub-flow. For operators $hX$ and $hY$ which are associated with
-+
+$\varphi_1(t)$ and $\varphi_2(t)$ respectively , we have
-+
+\begin{equation}
-+
+\exp (hX + hY) = \exp (hZ)
-+
+\end{equation}
-+
+where
-+
+\begin{equation}
-+
+hZ = hX + hY + \frac{{h^2 }}{2}[X,Y] + \frac{{h^3 }}{2}\left(
-+
+{[X,[X,Y]] + [Y,[Y,X]]} \right) +  \ldots .
-+
+\end{equation}
-+
+Here, $[X,Y]$ is the commutators of operator $X$ and $Y$ given by
-+
+\[
-+
+[X,Y] = XY - YX .
-+
+\]
-+
+Applying the Baker-Campbell-Hausdorff formula\cite{Varadarajan1974}
-+
+to the Sprang splitting, we can obtain
-+
+\begin{eqnarray*}
-+
+\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 \\
-+
+                                   &   & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\
-+
+                                   &   & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3[X,[X,Y]]/24 + \ldots )
-+
+\end{eqnarray*}
-+
+Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0,\] the dominant local
-+
+error of Spring splitting is proportional to $h^3$. The same
-+
+procedure can be applied to a general splitting,  of the form
-+
+\begin{equation}
-+
+\varphi _{b_m h}^2  \circ \varphi _{a_m h}^1  \circ \varphi _{b_{m -
-+
+} h}^2  \circ  \ldots  \circ \varphi _{a_1 h}^1 .
-+
+\end{equation}
-+
+A careful choice of coefficient $a_1 \ldots b_m$ will lead to higher
-+
+order methods. Yoshida proposed an elegant way to compose higher
-+
+order methods based on symmetric splitting\cite{Yoshida1990}. Given
-+
+a symmetric second order base method $ \varphi _h^{(2)} $, a
-+
+fourth-order symmetric method can be constructed by composing,
-+
+\[
-+
+\varphi _h^{(4)}  = \varphi _{\alpha h}^{(2)}  \circ \varphi _{\beta
-+
+h}^{(2)}  \circ \varphi _{\alpha h}^{(2)}
-+
+\]
-+
+where $ \alpha  =  - \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$ and $ \beta
-+
+= \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$. Moreover, a symmetric
-+
+integrator $ \varphi _h^{(2n + 2)}$ can be composed by
-+
+\begin{equation}
-+
+\varphi _h^{(2n + 2)}  = \varphi _{\alpha h}^{(2n)}  \circ \varphi
-+
+_{\beta h}^{(2n)}  \circ \varphi _{\alpha h}^{(2n)},
-+
+\end{equation}
-+
+if the weights are chosen as
-+
+\[
-+
+\alpha  =  - \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }},\beta =
-+
+\frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }} .
-+
+\]
-+
-+
+\section{\label{introSection:molecularDynamics}Molecular Dynamics}
-+
-+
+As one of the principal tools of molecular modeling, Molecular
-+
+dynamics has proven to be a powerful tool for studying the functions
-+
+of biological systems, providing structural, thermodynamic and
-+
+dynamical information. The basic idea of molecular dynamics is that
-+
+macroscopic properties are related to microscopic behavior and
-+
+microscopic behavior can be calculated from the trajectories in
-+
+simulations. For instance, instantaneous temperature of an
-+
+Hamiltonian system of $N$ particle can be measured by
-+
+\[
-+
+T = \sum\limits_{i = 1}^N {\frac{{m_i v_i^2 }}{{fk_B }}}
-+
+\]
-+
+where $m_i$ and $v_i$ are the mass and velocity of $i$th particle
-+
+respectively, $f$ is the number of degrees of freedom, and $k_B$ is
-+
+the boltzman constant.
-+
-+
+A typical molecular dynamics run consists of three essential steps:
-+
+\begin{enumerate}
-+
+  \item Initialization
-+
+    \begin{enumerate}
-+
+    \item Preliminary preparation
-+
+    \item Minimization
-+
+    \item Heating
-+
+    \item Equilibration
-+
+    \end{enumerate}
-+
+  \item Production
-+
+  \item Analysis
-+
+\end{enumerate}
-+
+These three individual steps will be covered in the following
-+
+sections. Sec.~\ref{introSec:initialSystemSettings} deals with the
-+
+initialization of a simulation. Sec.~\ref{introSection:production}
-+
+will discusse issues in production run.
-+
+Sec.~\ref{introSection:Analysis} provides the theoretical tools for
-+
+trajectory analysis.
-+
-+
+\subsection{\label{introSec:initialSystemSettings}Initialization}
-+
-+
+\subsubsection{\textbf{Preliminary preparation}}
-+
-+
+When selecting the starting structure of a molecule for molecular
-+
+simulation, one may retrieve its Cartesian coordinates from public
-+
+databases, such as RCSB Protein Data Bank \textit{etc}. Although
-+
+thousands of crystal structures of molecules are discovered every
-+
+year, many more remain unknown due to the difficulties of
-+
+purification and crystallization. Even for molecules with known
-+
+structure, some important information is missing. For example, a
-+
+missing hydrogen atom which acts as donor in hydrogen bonding must
-+
+be added. Moreover, in order to include electrostatic interaction,
-+
+one may need to specify the partial charges for individual atoms.
-+
+Under some circumstances, we may even need to prepare the system in
-+
+a special configuration. For instance, when studying transport
-+
+phenomenon in membrane systems, we may prepare the lipids in a
-+
+bilayer structure instead of placing lipids randomly in solvent,
-+
+since we are not interested in the slow self-aggregation process.
-+
-+
+\subsubsection{\textbf{Minimization}}
-+
-+
+It is quite possible that some of molecules in the system from
-+
+preliminary preparation may be overlapping with each other. This
-+
+close proximity leads to high initial potential energy which
-+
+consequently jeopardizes any molecular dynamics simulations. To
-+
+remove these steric overlaps, one typically performs energy
-+
+minimization to find a more reasonable conformation. Several energy
-+
+minimization methods have been developed to exploit the energy
-+
+surface and to locate the local minimum. While converging slowly
-+
+near the minimum, steepest descent method is extremely robust when
-+
+systems are strongly anharmonic. Thus, it is often used to refine
-+
+structure from crystallographic data. Relied on the gradient or
-+
+hessian, advanced methods like Newton-Raphson converge rapidly to a
-+
+local minimum, but become unstable if the energy surface is far from
-+
+quadratic. Another factor that must be taken into account, when
-+
+choosing energy minimization method, is the size of the system.
-+
+Steepest descent and conjugate gradient can deal with models of any
-+
+size. Because of the limits on computer memory to store the hessian
-+
+matrix and the computing power needed to diagonalized these
-+
+matrices, most Newton-Raphson methods can not be used with very
-+
+large systems.
-+
-+
+\subsubsection{\textbf{Heating}}
-+
-+
+Typically, Heating is performed by assigning random velocities
-+
+according to a Maxwell-Boltzman distribution for a desired
-+
+temperature. Beginning at a lower temperature and gradually
-+
+increasing the temperature by assigning larger random velocities, we
-+
+end up with setting the temperature of the system to a final
-+
+temperature at which the simulation will be conducted. In heating
-+
+phase, we should also keep the system from drifting or rotating as a
-+
+whole. To do this, the net linear momentum and angular momentum of
-+
+the system is shifted to zero after each resampling from the Maxwell
-+
+-Boltzman distribution.
-+
-+
+\subsubsection{\textbf{Equilibration}}
-+
-+
+The purpose of equilibration is to allow the system to evolve
-+
+spontaneously for a period of time and reach equilibrium. The
-+
+procedure is continued until various statistical properties, such as
-+
+temperature, pressure, energy, volume and other structural
-+
+properties \textit{etc}, become independent of time. Strictly
-+
+speaking, minimization and heating are not necessary, provided the
-+
+equilibration process is long enough. However, these steps can serve
-+
+as a means to arrive at an equilibrated structure in an effective
-+
+way.
-+
-+
+\subsection{\label{introSection:production}Production}
-+
-+
+The production run is the most important step of the simulation, in
-+
+which the equilibrated structure is used as a starting point and the
-+
+motions of the molecules are collected for later analysis. In order
-+
+to capture the macroscopic properties of the system, the molecular
-+
+dynamics simulation must be performed by sampling correctly and
-+
+efficiently from the relevant thermodynamic ensemble.
-+
-+
+The most expensive part of a molecular dynamics simulation is the
-+
+calculation of non-bonded forces, such as van der Waals force and
-+
+Coulombic forces \textit{etc}. For a system of $N$ particles, the
-+
+complexity of the algorithm for pair-wise interactions is $O(N^2 )$,
-+
+which making large simulations prohibitive in the absence of any
-+
+algorithmic tricks.
-+
-+
+A natural approach to avoid system size issues is to represent the
-+
+bulk behavior by a finite number of the particles. However, this
-+
+approach will suffer from the surface effect at the edges of the
-+
+simulation. To offset this, \textit{Periodic boundary conditions}
-+
+(see Fig.~\ref{introFig:pbc}) is developed to simulate bulk
-+
+properties with a relatively small number of particles. In this
-+
+method, the simulation box is replicated throughout space to form an
-+
+infinite lattice. During the simulation, when a particle moves in
-+
+the primary cell, its image in other cells move in exactly the same
-+
+direction with exactly the same orientation. Thus, as a particle
-+
+leaves the primary cell, one of its images will enter through the
-+
+opposite face.
-+
+\begin{figure}
-+
+\centering
-+
+\includegraphics[width=\linewidth]{pbc.eps}
-+
+\caption[An illustration of periodic boundary conditions]{A 2-D
-+
+illustration of periodic boundary conditions. As one particle leaves
-+
+the left of the simulation box, an image of it enters the right.}
-+
+\label{introFig:pbc}
-+
+\end{figure}
-+
-+
+%cutoff and minimum image convention
-+
+Another important technique to improve the efficiency of force
-+
+evaluation is to apply spherical cutoff where particles farther than
-+
+a predetermined distance are not included in the calculation
-+
+\cite{Frenkel1996}. The use of a cutoff radius will cause a
-+
+discontinuity in the potential energy curve. Fortunately, one can
-+
+shift simple radial potential to ensure the potential curve go
-+
+smoothly to zero at the cutoff radius. The cutoff strategy works
-+
+well for Lennard-Jones interaction because of its short range
-+
+nature. However, simply truncating the electrostatic interaction
-+
+with the use of cutoffs has been shown to lead to severe artifacts
-+
+in simulations. The Ewald summation, in which the slowly decaying
-+
+Coulomb potential is transformed into direct and reciprocal sums
-+
+with rapid and absolute convergence, has proved to minimize the
-+
+periodicity artifacts in liquid simulations. Taking the advantages
-+
+of the fast Fourier transform (FFT) for calculating discrete Fourier
-+
+transforms, the particle mesh-based
-+
+methods\cite{Hockney1981,Shimada1993, Luty1994} are accelerated from
-+
+$O(N^{3/2})$ to $O(N logN)$. An alternative approach is the
-+
+\emph{fast multipole method}\cite{Greengard1987, Greengard1994},
-+
+which treats Coulombic interactions exactly at short range, and
-+
+approximate the potential at long range through multipolar
-+
+expansion. In spite of their wide acceptance at the molecular
-+
+simulation community, these two methods are difficult to implement
-+
+correctly and efficiently. Instead, we use a damped and
-+
+charge-neutralized Coulomb potential method developed by Wolf and
-+
+his coworkers\cite{Wolf1999}. The shifted Coulomb potential for
-+
+particle $i$ and particle $j$ at distance $r_{rj}$ is given by:
-+
+\begin{equation}
-+
+V(r_{ij})= \frac{q_i q_j \textrm{erfc}(\alpha
-+
+r_{ij})}{r_{ij}}-\lim_{r_{ij}\rightarrow
-+
+R_\textrm{c}}\left\{\frac{q_iq_j \textrm{erfc}(\alpha
-+
+r_{ij})}{r_{ij}}\right\}. \label{introEquation:shiftedCoulomb}
-+
+\end{equation}
-+
+where $\alpha$ is the convergence parameter. Due to the lack of
-+
+inherent periodicity and rapid convergence,this method is extremely
-+
+efficient and easy to implement.
-+
+\begin{figure}
-+
+\centering
-+
+\includegraphics[width=\linewidth]{shifted_coulomb.eps}
-+
+\caption[An illustration of shifted Coulomb potential]{An
-+
+illustration of shifted Coulomb potential.}
-+
+\label{introFigure:shiftedCoulomb}
-+
+\end{figure}
-+
-+
+%multiple time step
-+
-+
+\subsection{\label{introSection:Analysis} Analysis}
-+
-+
+Recently, advanced visualization technique have become applied to
-+
+monitor the motions of molecules. Although the dynamics of the
-+
+system can be described qualitatively from animation, quantitative
-+
+trajectory analysis are more useful. According to the principles of
-+
+Statistical Mechanics, Sec.~\ref{introSection:statisticalMechanics},
-+
+one can compute thermodynamic properties, analyze fluctuations of
-+
+structural parameters, and investigate time-dependent processes of
-+
+the molecule from the trajectories.
-+
-+
+\subsubsection{\label{introSection:thermodynamicsProperties}\textbf{Thermodynamic Properties}}
-+
-+
+Thermodynamic properties, which can be expressed in terms of some
-+
+function of the coordinates and momenta of all particles in the
-+
+system, can be directly computed from molecular dynamics. The usual
-+
+way to measure the pressure is based on virial theorem of Clausius
-+
+which states that the virial is equal to $-3Nk_BT$. For a system
-+
+with forces between particles, the total virial, $W$, contains the
-+
+contribution from external pressure and interaction between the
-+
+particles:
-+
+\[
-+
+W =  - 3PV + \left\langle {\sum\limits_{i < j} {r{}_{ij} \cdot
-+
+f_{ij} } } \right\rangle
-+
+\]
-+
+where $f_{ij}$ is the force between particle $i$ and $j$ at a
-+
+distance $r_{ij}$. Thus, the expression for the pressure is given
-+
+by:
-+
+\begin{equation}
-+
+P = \frac{{Nk_B T}}{V} - \frac{1}{{3V}}\left\langle {\sum\limits_{i
-+
+< j} {r{}_{ij} \cdot f_{ij} } } \right\rangle
-+
+\end{equation}
-+
-+
+\subsubsection{\label{introSection:structuralProperties}\textbf{Structural Properties}}
-+
-+
+Structural Properties of a simple fluid can be described by a set of
-+
+distribution functions. Among these functions,the \emph{pair
-+
+distribution function}, also known as \emph{radial distribution
-+
+function}, is of most fundamental importance to liquid theory.
-+
+Experimentally, pair distribution function can be gathered by
-+
+Fourier transforming raw data from a series of neutron diffraction
-+
+experiments and integrating over the surface factor
-+
+\cite{Powles1973}. The experimental results can serve as a criterion
-+
+to justify the correctness of a liquid model. Moreover, various
-+
+equilibrium thermodynamic and structural properties can also be
-+
+expressed in terms of radial distribution function \cite{Allen1987}.
-+
-+
+The pair distribution functions $g(r)$ gives the probability that a
-+
+particle $i$ will be located at a distance $r$ from a another
-+
+particle $j$ in the system
-+
+\[
-+
+g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j
-+
+\ne i} {\delta (r - r_{ij} )} } } \right\rangle = \fract{\rho
-+
+(r)}{\rho}.
-+
+\]
-+
+Note that the delta function can be replaced by a histogram in
-+
+computer simulation. Figure
-+
+\ref{introFigure:pairDistributionFunction} shows a typical pair
-+
+distribution function for the liquid argon system. The occurrence of
-+
+several peaks in the plot of $g(r)$ suggests that it is more likely
-+
+to find particles at certain radial values than at others. This is a
-+
+result of the attractive interaction at such distances. Because of
-+
+the strong repulsive forces at short distance, the probability of
-+
+locating particles at distances less than about 3.7{\AA} from each
-+
+other is essentially zero.
-+
-+
+%\begin{figure}
-+
+%\centering
-+
+%\includegraphics[width=\linewidth]{pdf.eps}
-+
+%\caption[Pair distribution function for the liquid argon
-+
+%]{Pair distribution function for the liquid argon}
-+
+%\label{introFigure:pairDistributionFunction}
-+
+%\end{figure}
-+
-+
+\subsubsection{\label{introSection:timeDependentProperties}\textbf{Time-dependent
-+
+Properties}}
-+
-+
+Time-dependent properties are usually calculated using \emph{time
-+
+correlation functions}, which correlate random variables $A$ and $B$
-+
+at two different times,
-+
+\begin{equation}
-+
+C_{AB} (t) = \left\langle {A(t)B(0)} \right\rangle.
-+
+\label{introEquation:timeCorrelationFunction}
-+
+\end{equation}
-+
+If $A$ and $B$ refer to same variable, this kind of correlation
-+
+function is called an \emph{autocorrelation function}. One example
-+
+of an auto correlation function is the velocity auto-correlation
-+
+function which is directly related to transport properties of
-+
+molecular liquids:
-+
+\[
-+
+D = \frac{1}{3}\int\limits_0^\infty  {\left\langle {v(t) \cdot v(0)}
-+
+\right\rangle } dt
-+
+\]
-+
+where $D$ is diffusion constant. Unlike the velocity autocorrelation
-+
+function, which is averaging over time origins and over all the
-+
+atoms, the dipole autocorrelation functions are calculated for the
-+
+entire system. The dipole autocorrelation function is given by:
-+
+\[
-+
+c_{dipole}  = \left\langle {u_{tot} (t) \cdot u_{tot} (t)}
-+
+\right\rangle
-+
+\]
-+
+Here $u_{tot}$ is the net dipole of the entire system and is given
-+
+by
-+
+\[
-+
+u_{tot} (t) = \sum\limits_i {u_i (t)}
-+
+\]
-+
+In principle, many time correlation functions can be related with
-+
+Fourier transforms of the infrared, Raman, and inelastic neutron
-+
+scattering spectra of molecular liquids. In practice, one can
-+
+extract the IR spectrum from the intensity of dipole fluctuation at
-+
+each frequency using the following relationship:
-+
+\[
-+
+\hat c_{dipole} (v) = \int_{ - \infty }^\infty  {c_{dipole} (t)e^{ -
-+
+i2\pi vt} dt}
-+
+\]
-+
+\section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
-<
+\section{\label{introSection:correlationFunctions}Correlation Functions}
->
+Rigid bodies are frequently involved in the modeling of different
->
+areas, from engineering, physics, to chemistry. For example,
->
+missiles and vehicle are usually modeled by rigid bodies.  The
->
+movement of the objects in 3D gaming engine or other physics
->
+simulator is governed by rigid body dynamics. In molecular
->
+simulations, rigid bodies are used to simplify protein-protein
->
+docking studies\cite{Gray2003}.
-<
+\section{\label{introSection:langevinDynamics}Langevin Dynamics}
->
+It is very important to develop stable and efficient methods to
->
+integrate the equations of motion for orientational degrees of
->
+freedom. Euler angles are the natural choice to describe the
->
+rotational degrees of freedom. However, due to $\frac {1}{sin
->
+\theta}$ singularities, the numerical integration of corresponding
->
+equations of motion is very inefficient and inaccurate. Although an
->
+alternative integrator using multiple sets of Euler angles can
->
+overcome this difficulty\cite{Barojas1973}, the computational
->
+penalty and the loss of angular momentum conservation still remain.
->
+A singularity-free representation utilizing quaternions was
->
+developed by Evans in 1977\cite{Evans1977}. Unfortunately, this
->
+approach uses a nonseparable Hamiltonian resulting from the
->
+quaternion representation, which prevents the symplectic algorithm
->
+to be utilized. Another different approach is to apply holonomic
->
+constraints to the atoms belonging to the rigid body. Each atom
->
+moves independently under the normal forces deriving from potential
->
+energy and constraint forces which are used to guarantee the
->
+rigidness. However, due to their iterative nature, the SHAKE and
->
+Rattle algorithms also converge very slowly when the number of
->
+constraints increases\cite{Ryckaert1977, Andersen1983}.
-<
+\subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics}
->
+A break-through in geometric literature suggests that, in order to
->
+develop a long-term integration scheme, one should preserve the
->
+symplectic structure of the flow. By introducing a conjugate
->
+momentum to the rotation matrix $Q$ and re-formulating Hamiltonian's
->
+equation, a symplectic integrator, RSHAKE\cite{Kol1997}, was
->
+proposed to evolve the Hamiltonian system in a constraint manifold
->
+by iteratively satisfying the orthogonality constraint $Q^T Q = 1$.
->
+An alternative method using the quaternion representation was
->
+developed by Omelyan\cite{Omelyan1998}. However, both of these
->
+methods are iterative and inefficient. In this section, we descibe a
->
+symplectic Lie-Poisson integrator for rigid body developed by
->
+Dullweber and his coworkers\cite{Dullweber1997} in depth.
-<
+\subsection{\label{introSection:hydroynamics}Hydrodynamics}
->
+\subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Bodies}
->
+The motion of a rigid body is Hamiltonian with the Hamiltonian
->
+function
->
+\begin{equation}
->
+H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
->
+V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
->
+\label{introEquation:RBHamiltonian}
->
+\end{equation}
->
+Here, $q$ and $Q$  are the position and rotation matrix for the
->
+rigid-body, $p$ and $P$  are conjugate momenta to $q$  and $Q$ , and
->
+$J$, a diagonal matrix, is defined by
->
+\[
->
+I_{ii}^{ - 1}  = \frac{1}{2}\sum\limits_{i \ne j} {J_{jj}^{ - 1} }
->
+\]
->
+where $I_{ii}$ is the diagonal element of the inertia tensor. This
->
+constrained Hamiltonian equation is subjected to a holonomic
->
+constraint,
->
+\begin{equation}
->
+Q^T Q = 1, \label{introEquation:orthogonalConstraint}
->
+\end{equation}
->
+which is used to ensure rotation matrix's unitarity. Differentiating
->
+\ref{introEquation:orthogonalConstraint} and using Equation
->
+\ref{introEquation:RBMotionMomentum}, one may obtain,
->
+\begin{equation}
->
+Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0 . \\
->
+\label{introEquation:RBFirstOrderConstraint}
->
+\end{equation}
->
->
+Using Equation (\ref{introEquation:motionHamiltonianCoordinate},
->
+\ref{introEquation:motionHamiltonianMomentum}), one can write down
->
+the equations of motion,
->
->
+\begin{eqnarray}
->
+ \frac{{dq}}{{dt}} & = & \frac{p}{m} \label{introEquation:RBMotionPosition}\\
->
+ \frac{{dp}}{{dt}} & = & - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\
->
+ \frac{{dQ}}{{dt}} & = & PJ^{ - 1}  \label{introEquation:RBMotionRotation}\\
->
+ \frac{{dP}}{{dt}} & = & - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}
->
+\end{eqnarray}
->
->
+In general, there are two ways to satisfy the holonomic constraints.
->
+We can use a constraint force provided by a Lagrange multiplier on
->
+the normal manifold to keep the motion on constraint space. Or we
->
+can simply evolve the system on the constraint manifold. These two
->
+methods have been proved to be equivalent. The holonomic constraint
->
+and equations of motions define a constraint manifold for rigid
->
+bodies
->
+\[
->
+M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0}
->
+\right\}.
->
+\]
->
->
+Unfortunately, this constraint manifold is not the cotangent bundle
->
+$T_{\star}SO(3)$. However, it turns out that under symplectic
->
+transformation, the cotangent space and the phase space are
->
+diffeomorphic. By introducing
->
+\[
->
+\tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right),
->
+\]
->
+the mechanical system subject to a holonomic constraint manifold $M$
->
+can be re-formulated as a Hamiltonian system on the cotangent space
->
+\[
->
+T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q =
->
+,\tilde Q^T \tilde PJ^{ - 1}  + J^{ - 1} P^T \tilde Q = 0} \right\}
->
+\]
->
->
+For a body fixed vector $X_i$ with respect to the center of mass of
->
+the rigid body, its corresponding lab fixed vector $X_0^{lab}$  is
->
+given as
->
+\begin{equation}
->
+X_i^{lab} = Q X_i + q.
->
+\end{equation}
->
+Therefore, potential energy $V(q,Q)$ is defined by
->
+\[
->
+V(q,Q) = V(Q X_0 + q).
->
+\]
->
+Hence, the force and torque are given by
->
+\[
->
+\nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)},
->
+\]
->
+and
->
+\[
->
+\nabla _Q V(q,Q) = F(q,Q)X_i^t
->
+\]
->
+respectively.
->
->
+As a common choice to describe the rotation dynamics of the rigid
->
+body, the angular momentum on the body fixed frame $\Pi  = Q^t P$ is
->
+introduced to rewrite the equations of motion,
->
+\begin{equation}
->
+ \begin{array}{l}
->
+ \mathop \Pi \limits^ \bullet   = J^{ - 1} \Pi ^T \Pi  + Q^T \sum\limits_i {F_i (q,Q)X_i^T }  - \Lambda  \\
->
+ \mathop Q\limits^{{\rm{   }} \bullet }  = Q\Pi {\rm{ }}J^{ - 1}  \\
->
+ \end{array}
->
+ \label{introEqaution:RBMotionPI}
->
+\end{equation}
->
+, as well as holonomic constraints,
->
+\[
->
+\begin{array}{l}
->
+ \Pi J^{ - 1}  + J^{ - 1} \Pi ^t  = 0 \\
->
+ Q^T Q = 1 \\
->
+ \end{array}
->
+\]
->
->
+For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in
->
+so(3)^ \star$, the hat-map isomorphism,
->
+\begin{equation}
->
+v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left(
->
+{\begin{array}{*{20}c}
->
+& { - v_3 } & {v_2 }  \\
->
+   {v_3 } & 0 & { - v_1 }  \\
->
+   { - v_2 } & {v_1 } & 0  \\
->
+\end{array}} \right),
->
+\label{introEquation:hatmapIsomorphism}
->
+\end{equation}
->
+will let us associate the matrix products with traditional vector
->
+operations
->
+\[
->
+\hat vu = v \times u
->
+\]
->
+Using \ref{introEqaution:RBMotionPI}, one can construct a skew
->
+matrix,
->
+\begin{equation}
->
+(\mathop \Pi \limits^ \bullet   - \mathop \Pi \limits^ {\bullet  ^T}
->
+){\rm{ }} = {\rm{ }}(\Pi  - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi  + \Pi J^{
->
+- 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T  - X_i F_i (r,Q)^T Q]} -
->
+(\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
->
+\end{equation}
->
+Since $\Lambda$ is symmetric, the last term of Equation
->
+\ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange
->
+multiplier $\Lambda$ is absent from the equations of motion. This
->
+unique property eliminates the requirement of iterations which can
->
+not be avoided in other methods\cite{Kol1997, Omelyan1998}.
->
->
+Applying the hat-map isomorphism, we obtain the equation of motion
->
+for angular momentum on body frame
->
+\begin{equation}
->
+\dot \pi  = \pi  \times I^{ - 1} \pi  + \sum\limits_i {\left( {Q^T
->
+F_i (r,Q)} \right) \times X_i }.
->
+\label{introEquation:bodyAngularMotion}
->
+\end{equation}
->
+In the same manner, the equation of motion for rotation matrix is
->
+given by
->
+\[
->
+\dot Q = Qskew(I^{ - 1} \pi )
->
+\]
->
->
+\subsection{\label{introSection:SymplecticFreeRB}Symplectic
->
+Lie-Poisson Integrator for Free Rigid Body}
->
->
+If there are no external forces exerted on the rigid body, the only
->
+contribution to the rotational motion is from the kinetic energy
->
+(the first term of \ref{introEquation:bodyAngularMotion}). The free
->
+rigid body is an example of a Lie-Poisson system with Hamiltonian
->
+function
->
+\begin{equation}
->
+T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 )
->
+\label{introEquation:rotationalKineticRB}
->
+\end{equation}
->
+where $T_i^r (\pi _i ) = \frac{{\pi _i ^2 }}{{2I_i }}$ and
->
+Lie-Poisson structure matrix,
->
+\begin{equation}
->
+J(\pi ) = \left( {\begin{array}{*{20}c}
->
+& {\pi _3 } & { - \pi _2 }  \\
->
+   { - \pi _3 } & 0 & {\pi _1 }  \\
->
+   {\pi _2 } & { - \pi _1 } & 0  \\
->
+\end{array}} \right)
->
+\end{equation}
->
+Thus, the dynamics of free rigid body is governed by
->
+\begin{equation}
->
+\frac{d}{{dt}}\pi  = J(\pi )\nabla _\pi  T^r (\pi )
->
+\end{equation}
->
->
+One may notice that each $T_i^r$ in Equation
->
+\ref{introEquation:rotationalKineticRB} can be solved exactly. For
->
+instance, the equations of motion due to $T_1^r$ are given by
->
+\begin{equation}
->
+\frac{d}{{dt}}\pi  = R_1 \pi ,\frac{d}{{dt}}Q = QR_1
->
+\label{introEqaution:RBMotionSingleTerm}
->
+\end{equation}
->
+where
->
+\[ R_1  = \left( {\begin{array}{*{20}c}
->
+& 0 & 0  \\
->
+& 0 & {\pi _1 }  \\
->
+& { - \pi _1 } & 0  \\
->
+\end{array}} \right).
->
+\]
->
+The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is
->
+\[
->
+\pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) =
->
+Q(0)e^{\Delta tR_1 }
->
+\]
->
+with
->
+\[
->
+e^{\Delta tR_1 }  = \left( {\begin{array}{*{20}c}
->
+& 0 & 0  \\
->
+& {\cos \theta _1 } & {\sin \theta _1 }  \\
->
+& { - \sin \theta _1 } & {\cos \theta _1 }  \\
->
+\end{array}} \right),\theta _1  = \frac{{\pi _1 }}{{I_1 }}\Delta t.
->
+\]
->
+To reduce the cost of computing expensive functions in $e^{\Delta
->
+tR_1 }$, we can use Cayley transformation to obtain a single-aixs
->
+propagator,
->
+\[
->
+e^{\Delta tR_1 }  \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1
->
+)
->
+\]
->
+The flow maps for $T_2^r$ and $T_3^r$ can be found in the same
->
+manner. In order to construct a second-order symplectic method, we
->
+split the angular kinetic Hamiltonian function can into five terms
->
+\[
->
+T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2
->
+) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r
->
+(\pi _1 ).
->
+\]
->
+By concatenating the propagators corresponding to these five terms,
->
+we can obtain an symplectic integrator,
->
+\[
->
+\varphi _{\Delta t,T^r }  = \varphi _{\Delta t/2,\pi _1 }  \circ
->
+\varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
->
+\circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
->
+_1 }.
->
+\]
->
->
+The non-canonical Lie-Poisson bracket ${F, G}$ of two function
->
+$F(\pi )$ and $G(\pi )$ is defined by
->
+\[
->
+\{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
->
+)
->
+\]
->
+If the Poisson bracket of a function $F$ with an arbitrary smooth
->
+function $G$ is zero, $F$ is a \emph{Casimir}, which is the
->
+conserved quantity in Poisson system. We can easily verify that the
->
+norm of the angular momentum, $\parallel \pi
->
+\parallel$, is a \emph{Casimir}. Let$ F(\pi ) = S(\frac{{\parallel
->
+\pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
->
+then by the chain rule
->
+\[
->
+\nabla _\pi  F(\pi ) = S'(\frac{{\parallel \pi \parallel ^2
->
+}}{2})\pi
->
+\]
->
+Thus $ [\nabla _\pi  F(\pi )]^T J(\pi ) =  - S'(\frac{{\parallel \pi
->
+\parallel ^2 }}{2})\pi  \times \pi  = 0 $. This explicit
->
+Lie-Poisson integrator is found to be both extremely efficient and
->
+stable. These properties can be explained by the fact the small
->
+angle approximation is used and the norm of the angular momentum is
->
+conserved.
->
->
+\subsection{\label{introSection:RBHamiltonianSplitting} Hamiltonian
->
+Splitting for Rigid Body}
->
->
+The Hamiltonian of rigid body can be separated in terms of kinetic
->
+energy and potential energy,
->
+\[
->
+H = T(p,\pi ) + V(q,Q)
->
+\]
->
+The equations of motion corresponding to potential energy and
->
+kinetic energy are listed in the below table,
->
+\begin{table}
->
+\caption{Equations of motion due to Potential and Kinetic Energies}
->
+\begin{center}
->
+\begin{tabular}{|l|l|}
->
+  \hline
->
+  % after \\: \hline or \cline{col1-col2} \cline{col3-col4} ...
->
+  Potential & Kinetic \\
->
+  $\frac{{dq}}{{dt}} = \frac{p}{m}$ & $\frac{d}{{dt}}q = p$ \\
->
+  $\frac{d}{{dt}}p =  - \frac{{\partial V}}{{\partial q}}$ & $ \frac{d}{{dt}}p = 0$ \\
->
+  $\frac{d}{{dt}}Q = 0$ & $ \frac{d}{{dt}}Q = Qskew(I^{ - 1} j)$ \\
->
+  $ \frac{d}{{dt}}\pi  = \sum\limits_i {\left( {Q^T F_i (r,Q)} \right) \times X_i }$ & $\frac{d}{{dt}}\pi  = \pi  \times I^{ - 1} \pi$\\
->
+  \hline
->
+\end{tabular}
->
+\end{center}
->
+\end{table}
->
+A second-order symplectic method is now obtained by the composition
->
+of the position and velocity propagators,
->
+\[
->
+\varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
->
+_{\Delta t,T}  \circ \varphi _{\Delta t/2,V}.
->
+\]
->
+Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two
->
+sub-propagators which corresponding to force and torque
->
+respectively,
->
+\[
->
+\varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
->
+_{\Delta t/2,\tau }.
->
+\]
->
+Since the associated operators of $\varphi _{\Delta t/2,F} $ and
->
+$\circ \varphi _{\Delta t/2,\tau }$ commute, the composition order
->
+inside $\varphi _{\Delta t/2,V}$ does not matter. Furthermore, the
->
+kinetic energy can be separated to translational kinetic term, $T^t
->
+(p)$, and rotational kinetic term, $T^r (\pi )$,
->
+\begin{equation}
->
+T(p,\pi ) =T^t (p) + T^r (\pi ).
->
+\end{equation}
->
+where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is
->
+defined by \ref{introEquation:rotationalKineticRB}. Therefore, the
->
+corresponding propagators are given by
->
+\[
->
+\varphi _{\Delta t,T}  = \varphi _{\Delta t,T^t }  \circ \varphi
->
+_{\Delta t,T^r }.
->
+\]
->
+Finally, we obtain the overall symplectic propagators for freely
->
+moving rigid bodies
->
+\begin{equation}
->
+\begin{array}{c}
->
+ \varphi _{\Delta t}  = \varphi _{\Delta t/2,F}  \circ \varphi _{\Delta t/2,\tau }  \\
->
+  \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 }  \\
->
+  \circ \varphi _{\Delta t/2,\tau }  \circ \varphi _{\Delta t/2,F}  .\\
->
+ \end{array}
->
+\label{introEquation:overallRBFlowMaps}
->
+\end{equation}
->
->
+\section{\label{introSection:langevinDynamics}Langevin Dynamics}
->
+As an alternative to newtonian dynamics, Langevin dynamics, which
->
+mimics a simple heat bath with stochastic and dissipative forces,
->
+has been applied in a variety of studies. This section will review
->
+the theory of Langevin dynamics. A brief derivation of generalized
->
+Langevin equation will be given first. Following that, we will
->
+discuss the physical meaning of the terms appearing in the equation
->
+as well as the calculation of friction tensor from hydrodynamics
->
+theory.
->
->
+\subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation}
->
->
+A harmonic bath model, in which an effective set of harmonic
->
+oscillators are used to mimic the effect of a linearly responding
->
+environment, has been widely used in quantum chemistry and
->
+statistical mechanics. One of the successful applications of
->
+Harmonic bath model is the derivation of the Generalized Langevin
->
+Dynamics (GLE). Lets consider a system, in which the degree of
->
+freedom $x$ is assumed to couple to the bath linearly, giving a
->
+Hamiltonian of the form
->
+\begin{equation}
->
+H = \frac{{p^2 }}{{2m}} + U(x) + H_B  + \Delta U(x,x_1 , \ldots x_N)
->
+\label{introEquation:bathGLE}.
->
+\end{equation}
->
+Here $p$ is a momentum conjugate to $x$, $m$ is the mass associated
->
+with this degree of freedom, $H_B$ is a harmonic bath Hamiltonian,
->
+\[
->
+H_B  = \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
->
+}}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  \omega _\alpha ^2 }
->
+\right\}}
->
+\]
->
+where the index $\alpha$ runs over all the bath degrees of freedom,
->
+$\omega _\alpha$ are the harmonic bath frequencies, $m_\alpha$ are
->
+the harmonic bath masses, and $\Delta U$ is a bilinear system-bath
->
+coupling,
->
+\[
->
+\Delta U =  - \sum\limits_{\alpha  = 1}^N {g_\alpha  x_\alpha  x}
->
+\]
->
+where $g_\alpha$ are the coupling constants between the bath
->
+coordinates ($x_ \apha$) and the system coordinate ($x$).
->
+Introducing
->
+\[
->
+W(x) = U(x) - \sum\limits_{\alpha  = 1}^N {\frac{{g_\alpha ^2
->
+}}{{2m_\alpha  w_\alpha ^2 }}} x^2
->
+\] and combining the last two terms in Equation
->
+\ref{introEquation:bathGLE}, we may rewrite the Harmonic bath
->
+Hamiltonian as
->
+\[
->
+H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha  = 1}^N
->
+{\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
->
+w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
->
+w_\alpha ^2 }}x} \right)^2 } \right\}}
->
+\]
->
+Since the first two terms of the new Hamiltonian depend only on the
->
+system coordinates, we can get the equations of motion for
->
+Generalized Langevin Dynamics by Hamilton's equations,
->
+\begin{equation}
->
+m\ddot x =  - \frac{{\partial W(x)}}{{\partial x}} -
->
+\sum\limits_{\alpha  = 1}^N {g_\alpha  \left( {x_\alpha   -
->
+\frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right)},
->
+\label{introEquation:coorMotionGLE}
->
+\end{equation}
->
+and
->
+\begin{equation}
->
+m\ddot x_\alpha   =  - m_\alpha  w_\alpha ^2 \left( {x_\alpha   -
->
+\frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right).
->
+\label{introEquation:bathMotionGLE}
->
+\end{equation}
->
->
+In order to derive an equation for $x$, the dynamics of the bath
->
+variables $x_\alpha$ must be solved exactly first. As an integral
->
+transform which is particularly useful in solving linear ordinary
->
+differential equations,the Laplace transform is the appropriate tool
->
+to solve this problem. The basic idea is to transform the difficult
->
+differential equations into simple algebra problems which can be
->
+solved easily. Then, by applying the inverse Laplace transform, also
->
+known as the Bromwich integral, we can retrieve the solutions of the
->
+original problems.
->
->
+Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace
->
+transform of f(t) is a new function defined as
->
+\[
->
+L(f(t)) \equiv F(p) = \int_0^\infty  {f(t)e^{ - pt} dt}
->
+\]
->
+where  $p$ is real and  $L$ is called the Laplace Transform
->
+Operator. Below are some important properties of Laplace transform
->
->
+\begin{eqnarray*}
->
+ L(x + y)  & = & L(x) + L(y) \\
->
+ L(ax)     & = & aL(x) \\
->
+ L(\dot x) & = & pL(x) - px(0) \\
->
+ L(\ddot x)& = & p^2 L(x) - px(0) - \dot x(0) \\
->
+ L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right)& = & G(p)H(p) \\
->
+ \end{eqnarray*}
->
->
->
+Applying the Laplace transform to the bath coordinates, we obtain
->
+\begin{eqnarray*}
->
+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) \\
->
+L(x_\alpha  ) & = & \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }} \\
->
+\end{eqnarray*}
->
->
+By the same way, the system coordinates become
->
+\begin{eqnarray*}
->
+ mL(\ddot x) & = & - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\
->
+  & & \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\}}  \\
->
+\end{eqnarray*}
->
->
+With the help of some relatively important inverse Laplace
->
+transformations:
->
+\[
->
+\begin{array}{c}
->
+ L(\cos at) = \frac{p}{{p^2  + a^2 }} \\
->
+ L(\sin at) = \frac{a}{{p^2  + a^2 }} \\
->
+ L(1) = \frac{1}{p} \\
->
+ \end{array}
->
+\]
->
+, we obtain
->
+\begin{eqnarray*}
->
+m\ddot x & =  & - \frac{{\partial W(x)}}{{\partial x}} -
->
+\sum\limits_{\alpha  = 1}^N {\left\{ {\left( { - \frac{{g_\alpha ^2
->
+}}{{m_\alpha  \omega _\alpha ^2 }}} \right)\int_0^t {\cos (\omega
->
+_\alpha  t)\dot x(t - \tau )d\tau } } \right\}}  \\
->
+& & + \sum\limits_{\alpha  = 1}^N {\left\{ {\left[ {g_\alpha
->
+x_\alpha (0) - \frac{{g_\alpha  }}{{m_\alpha  \omega _\alpha  }}}
->
+\right]\cos (\omega _\alpha  t) + \frac{{g_\alpha  \dot x_\alpha
->
+(0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t)} \right\}}
->
+\end{eqnarray*}
->
+\begin{eqnarray*}
->
+m\ddot x & = & - \frac{{\partial W(x)}}{{\partial x}} - \int_0^t
->
+{\sum\limits_{\alpha  = 1}^N {\left( { - \frac{{g_\alpha ^2
->
+}}{{m_\alpha  \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha
->
+t)\dot x(t - \tau )d} \tau }  \\
->
+& & + \sum\limits_{\alpha  = 1}^N {\left\{ {\left[ {g_\alpha
->
+x_\alpha (0) - \frac{{g_\alpha }}{{m_\alpha \omega _\alpha  }}}
->
+\right]\cos (\omega _\alpha  t) + \frac{{g_\alpha  \dot x_\alpha
->
+(0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t)} \right\}}
->
+\end{eqnarray*}
->
+Introducing a \emph{dynamic friction kernel}
->
+\begin{equation}
->
+\xi (t) = \sum\limits_{\alpha  = 1}^N {\left( { - \frac{{g_\alpha ^2
->
+}}{{m_\alpha  \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha  t)}
->
+\label{introEquation:dynamicFrictionKernelDefinition}
->
+\end{equation}
->
+and \emph{a random force}
->
+\begin{equation}
->
+R(t) = \sum\limits_{\alpha  = 1}^N {\left( {g_\alpha  x_\alpha  (0)
->
+- \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}x(0)}
->
+\right)\cos (\omega _\alpha  t)}  + \frac{{\dot x_\alpha
->
+(0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t),
->
+\label{introEquation:randomForceDefinition}
->
+\end{equation}
->
+the equation of motion can be rewritten as
->
+\begin{equation}
->
+m\ddot x =  - \frac{{\partial W}}{{\partial x}} - \int_0^t {\xi
->
+(t)\dot x(t - \tau )d\tau }  + R(t)
->
+\label{introEuqation:GeneralizedLangevinDynamics}
->
+\end{equation}
->
+which is known as the \emph{generalized Langevin equation}.
->
->
+\subsubsection{\label{introSection:randomForceDynamicFrictionKernel}\textbf{Random Force and Dynamic Friction Kernel}}
->
->
+One may notice that $R(t)$ depends only on initial conditions, which
->
+implies it is completely deterministic within the context of a
->
+harmonic bath. However, it is easy to verify that $R(t)$ is totally
->
+uncorrelated to $x$ and $\dot x$,
->
+\[
->
+\begin{array}{l}
->
+ \left\langle {x(t)R(t)} \right\rangle  = 0, \\
->
+ \left\langle {\dot x(t)R(t)} \right\rangle  = 0. \\
->
+ \end{array}
->
+\]
->
+This property is what we expect from a truly random process. As long
->
+as the model chosen for $R(t)$ was a gaussian distribution in
->
+general, the stochastic nature of the GLE still remains.
->
->
+%dynamic friction kernel
->
+The convolution integral
->
+\[
->
+\int_0^t {\xi (t)\dot x(t - \tau )d\tau }
->
+\]
->
+depends on the entire history of the evolution of $x$, which implies
->
+that the bath retains memory of previous motions. In other words,
->
+the bath requires a finite time to respond to change in the motion
->
+of the system. For a sluggish bath which responds slowly to changes
->
+in the system coordinate, we may regard $\xi(t)$ as a constant
->
+$\xi(t) = \Xi_0$. Hence, the convolution integral becomes
->
+\[
->
+\int_0^t {\xi (t)\dot x(t - \tau )d\tau }  = \xi _0 (x(t) - x(0))
->
+\]
->
+and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes
->
+\[
->
+m\ddot x =  - \frac{\partial }{{\partial x}}\left( {W(x) +
->
+\frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t),
->
+\]
->
+which can be used to describe the effect of dynamic caging in
->
+viscous solvents. The other extreme is the bath that responds
->
+infinitely quickly to motions in the system. Thus, $\xi (t)$ can be
->
+taken as a $delta$ function in time:
->
+\[
->
+\xi (t) = 2\xi _0 \delta (t)
->
+\]
->
+Hence, the convolution integral becomes
->
+\[
->
+\int_0^t {\xi (t)\dot x(t - \tau )d\tau }  = 2\xi _0 \int_0^t
->
+{\delta (t)\dot x(t - \tau )d\tau }  = \xi _0 \dot x(t),
->
+\]
->
+and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes
->
+\begin{equation}
->
+m\ddot x =  - \frac{{\partial W(x)}}{{\partial x}} - \xi _0 \dot
->
+x(t) + R(t) \label{introEquation:LangevinEquation}
->
+\end{equation}
->
+which is known as the Langevin equation. The static friction
->
+coefficient $\xi _0$ can either be calculated from spectral density
->
+or be determined by Stokes' law for regular shaped particles. A
->
+briefly review on calculating friction tensor for arbitrary shaped
->
+particles is given in Sec.~\ref{introSection:frictionTensor}.
->
->
+\subsubsection{\label{introSection:secondFluctuationDissipation}\textbf{The Second Fluctuation Dissipation Theorem}}
->
->
+Defining a new set of coordinates,
->
+\[
->
+q_\alpha  (t) = x_\alpha  (t) - \frac{1}{{m_\alpha  \omega _\alpha
->
+^2 }}x(0)
->
+\],
->
+we can rewrite $R(T)$ as
->
+\[
->
+R(t) = \sum\limits_{\alpha  = 1}^N {g_\alpha  q_\alpha  (t)}.
->
+\]
->
+And since the $q$ coordinates are harmonic oscillators,
->
->
+\begin{eqnarray*}
->
+ \left\langle {q_\alpha ^2 } \right\rangle  & = & \frac{{kT}}{{m_\alpha  \omega _\alpha ^2 }} \\
->
+ \left\langle {q_\alpha  (t)q_\alpha  (0)} \right\rangle & = & \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha  t) \\
->
+ \left\langle {q_\alpha  (t)q_\beta  (0)} \right\rangle & = &\delta _{\alpha \beta } \left\langle {q_\alpha  (t)q_\alpha  (0)} \right\rangle  \\
->
+ \left\langle {R(t)R(0)} \right\rangle & = & \sum\limits_\alpha  {\sum\limits_\beta  {g_\alpha  g_\beta  \left\langle {q_\alpha  (t)q_\beta  (0)} \right\rangle } }  \\
->
+  & = &\sum\limits_\alpha  {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha  t)}  \\
->
+  & = &kT\xi (t) \\
->
+\end{eqnarray*}
->
->
+Thus, we recover the \emph{second fluctuation dissipation theorem}
->
+\begin{equation}
->
+\xi (t) = \left\langle {R(t)R(0)} \right\rangle
->
+\label{introEquation:secondFluctuationDissipation}.
->
+\end{equation}
->
+In effect, it acts as a constraint on the possible ways in which one
->
+can model the random force and friction kernel.

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Comparing trunk/tengDissertation/Introduction.tex (file contents): Revision 2693 by tim, Wed Apr 5 03:44:32 2006 UTC vs. Revision 2872 by tim, Wed Jun 21 16:28:25 2006 UTC

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Comparing trunk/tengDissertation/Introduction.tex (file contents):
Revision 2693 by tim, Wed Apr 5 03:44:32 2006 UTC vs.
Revision 2872 by tim, Wed Jun 21 16:28:25 2006 UTC