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# Line 208 | Line 208 | Statistical Mechanics concepts and theorem presented i
208   The thermodynamic behaviors and properties of Molecular Dynamics
209   simulation are governed by the principle of Statistical Mechanics.
210   The following section will give a brief introduction to some of the
211 < Statistical Mechanics concepts and theorem presented in this
211 > Statistical Mechanics concepts and theorems presented in this
212   dissertation.
213  
214   \subsection{\label{introSection:ensemble}Phase Space and Ensemble}
# Line 372 | Line 372 | bracket ${F, G}$ is defined as
372   Liouville's theorem can be expressed in a variety of different forms
373   which are convenient within different contexts. For any two function
374   $F$ and $G$ of the coordinates and momenta of a system, the Poisson
375 < bracket ${F, G}$ is defined as
375 > bracket $\{F,G\}$ is defined as
376   \begin{equation}
377   \left\{ {F,G} \right\} = \sum\limits_i {\left( {\frac{{\partial
378   F}}{{\partial q_i }}\frac{{\partial G}}{{\partial p_i }} -
# Line 439 | Line 439 | initial conditionals and move the objects in the direc
439   \section{\label{introSection:geometricIntegratos}Geometric Integrators}
440   A variety of numerical integrators have been proposed to simulate
441   the motions of atoms in MD simulation. They usually begin with
442 < initial conditionals and move the objects in the direction governed
442 > initial conditions and move the objects in the direction governed
443   by the differential equations. However, most of them ignore the
444   hidden physical laws contained within the equations. Since 1990,
445   geometric integrators, which preserve various phase-flow invariants
# Line 459 | Line 459 | defined as a pair $(M, \omega)$ which consists of a
459   \emph{smooth manifold}) is a manifold on which it is possible to
460   apply calculus\cite{Hirsch1997}. A \emph{symplectic manifold} is
461   defined as a pair $(M, \omega)$ which consists of a
462 < \emph{differentiable manifold} $M$ and a close, non-degenerated,
462 > \emph{differentiable manifold} $M$ and a close, non-degenerate,
463   bilinear symplectic form, $\omega$. A symplectic form on a vector
464   space $V$ is a function $\omega(x, y)$ which satisfies
465   $\omega(\lambda_1x_1+\lambda_2x_2, y) = \lambda_1\omega(x_1, y)+
# Line 479 | Line 479 | where $x = x(q,p)^T$, this system is a canonical Hamil
479   \begin{equation}
480   \dot x = f(x)
481   \end{equation}
482 < where $x = x(q,p)^T$, this system is a canonical Hamiltonian, if
482 > where $x = x(q,p)$, this system is a canonical Hamiltonian, if
483   $f(x) = J\nabla _x H(x)$. Here, $H = H (q, p)$ is Hamiltonian
484   function and $J$ is the skew-symmetric matrix
485   \begin{equation}
# Line 527 | Line 527 | The exact propagator can also be written in terms of o
527   \begin{equation}
528   \varphi _\tau   = \varphi _{ - \tau }^{ - 1}.
529   \end{equation}
530 < The exact propagator can also be written in terms of operator,
530 > The exact propagator can also be written as an operator,
531   \begin{equation}
532   \varphi _\tau  (x) = e^{\tau \sum\limits_i {f_i (x)\frac{\partial
533   }{{\partial x_i }}} } (x) \equiv \exp (\tau f)(x).
# Line 729 | Line 729 | the equations of motion would follow:
729  
730   \item Use the half step velocities to move positions one whole step, $\Delta t$.
731  
732 < \item Evaluate the forces at the new positions, $\mathbf{q}(\Delta t)$, and use the new forces to complete the velocity move.
732 > \item Evaluate the forces at the new positions, $q(\Delta t)$, and use the new forces to complete the velocity move.
733  
734   \item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values.
735   \end{enumerate}
# Line 868 | Line 868 | near the minimum, steepest descent method is extremely
868   minimization to find a more reasonable conformation. Several energy
869   minimization methods have been developed to exploit the energy
870   surface and to locate the local minimum. While converging slowly
871 < near the minimum, steepest descent method is extremely robust when
871 > near the minimum, the steepest descent method is extremely robust when
872   systems are strongly anharmonic. Thus, it is often used to refine
873   structures from crystallographic data. Relying on the Hessian,
874   advanced methods like Newton-Raphson converge rapidly to a local
# Line 887 | Line 887 | at which the simulation will be conducted. In heating
887   temperature. Beginning at a lower temperature and gradually
888   increasing the temperature by assigning larger random velocities, we
889   end up setting the temperature of the system to a final temperature
890 < at which the simulation will be conducted. In heating phase, we
890 > at which the simulation will be conducted. In the heating phase, we
891   should also keep the system from drifting or rotating as a whole. To
892   do this, the net linear momentum and angular momentum of the system
893   is shifted to zero after each resampling from the Maxwell -Boltzman
# Line 954 | Line 954 | periodicity artifacts in liquid simulations. Taking th
954   in simulations. The Ewald summation, in which the slowly decaying
955   Coulomb potential is transformed into direct and reciprocal sums
956   with rapid and absolute convergence, has proved to minimize the
957 < periodicity artifacts in liquid simulations. Taking the advantages
958 < of the fast Fourier transform (FFT) for calculating discrete Fourier
957 > periodicity artifacts in liquid simulations. Taking advantage
958 > of fast Fourier transform (FFT) techniques for calculating discrete Fourier
959   transforms, the particle mesh-based
960   methods\cite{Hockney1981,Shimada1993, Luty1994} are accelerated from
961   $O(N^{3/2})$ to $O(N logN)$. An alternative approach is the
# Line 1059 | Line 1059 | functions are called \emph{autocorrelation functions}.
1059   \label{introEquation:timeCorrelationFunction}
1060   \end{equation}
1061   If $A$ and $B$ refer to same variable, this kind of correlation
1062 < functions are called \emph{autocorrelation functions}. One example
1063 < of auto correlation function is the velocity auto-correlation
1062 > functions are called \emph{autocorrelation functions}. One typical example is the velocity autocorrelation
1063   function which is directly related to transport properties of
1064   molecular liquids:
1065 < \[
1065 > \begin{equation}
1066   D = \frac{1}{3}\int\limits_0^\infty  {\left\langle {v(t) \cdot v(0)}
1067   \right\rangle } dt
1068 < \]
1068 > \end{equation}
1069   where $D$ is diffusion constant. Unlike the velocity autocorrelation
1070   function, which is averaged over time origins and over all the
1071   atoms, the dipole autocorrelation functions is calculated for the
1072   entire system. The dipole autocorrelation function is given by:
1073 < \[
1073 > \begin{equation}
1074   c_{dipole}  = \left\langle {u_{tot} (t) \cdot u_{tot} (t)}
1075   \right\rangle
1076 < \]
1076 > \end{equation}
1077   Here $u_{tot}$ is the net dipole of the entire system and is given
1078   by
1079 < \[
1079 > \begin{equation}
1080   u_{tot} (t) = \sum\limits_i {u_i (t)}.
1081 < \]
1081 > \end{equation}
1082   In principle, many time correlation functions can be related to
1083   Fourier transforms of the infrared, Raman, and inelastic neutron
1084   scattering spectra of molecular liquids. In practice, one can
1085   extract the IR spectrum from the intensity of the molecular dipole
1086   fluctuation at each frequency using the following relationship:
1087 < \[
1087 > \begin{equation}
1088   \hat c_{dipole} (v) = \int_{ - \infty }^\infty  {c_{dipole} (t)e^{ -
1089   i2\pi vt} dt}.
1090 < \]
1090 > \end{equation}
1091  
1092   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
1093  
1094   Rigid bodies are frequently involved in the modeling of different
1095 < areas, from engineering, physics, to chemistry. For example,
1095 > areas, including engineering, physics and chemistry. For example,
1096   missiles and vehicles are usually modeled by rigid bodies.  The
1097   movement of the objects in 3D gaming engines or other physics
1098   simulators is governed by rigid body dynamics. In molecular
# Line 1135 | Line 1134 | The motion of a rigid body is Hamiltonian with the Ham
1134   Dullweber and his coworkers\cite{Dullweber1997} in depth.
1135  
1136   \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Bodies}
1137 < The motion of a rigid body is Hamiltonian with the Hamiltonian
1139 < function
1137 > The Hamiltonian of a rigid body is given by
1138   \begin{equation}
1139   H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
1140   V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
# Line 1348 | Line 1346 | The non-canonical Lie-Poisson bracket ${F, G}$ of two
1346   \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1347   _1 }.
1348   \]
1349 < The non-canonical Lie-Poisson bracket ${F, G}$ of two function
1352 < $F(\pi )$ and $G(\pi )$ is defined by
1349 > The non-canonical Lie-Poisson bracket $\{F, G\}$ of two functions $F(\pi )$ and $G(\pi )$ is defined by
1350   \[
1351   \{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
1352   ).
# Line 1358 | Line 1355 | norm of the angular momentum, $\parallel \pi
1355   function $G$ is zero, $F$ is a \emph{Casimir}, which is the
1356   conserved quantity in Poisson system. We can easily verify that the
1357   norm of the angular momentum, $\parallel \pi
1358 < \parallel$, is a \emph{Casimir}\cite{McLachlan1993}. Let$ F(\pi ) = S(\frac{{\parallel
1358 > \parallel$, is a \emph{Casimir}\cite{McLachlan1993}. Let $F(\pi ) = S(\frac{{\parallel
1359   \pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
1360   then by the chain rule
1361   \[
# Line 1379 | Line 1376 | listed in Table~\ref{introTable:rbEquations}
1376   The Hamiltonian of rigid body can be separated in terms of kinetic
1377   energy and potential energy, $H = T(p,\pi ) + V(q,Q)$. The equations
1378   of motion corresponding to potential energy and kinetic energy are
1379 < listed in Table~\ref{introTable:rbEquations}
1379 > listed in Table~\ref{introTable:rbEquations}.
1380   \begin{table}
1381   \caption{EQUATIONS OF MOTION DUE TO POTENTIAL AND KINETIC ENERGIES}
1382   \label{introTable:rbEquations}
# Line 1437 | Line 1434 | the theory of Langevin dynamics. A brief derivation of
1434   As an alternative to newtonian dynamics, Langevin dynamics, which
1435   mimics a simple heat bath with stochastic and dissipative forces,
1436   has been applied in a variety of studies. This section will review
1437 < the theory of Langevin dynamics. A brief derivation of generalized
1437 > the theory of Langevin dynamics. A brief derivation of the generalized
1438   Langevin equation will be given first. Following that, we will
1439 < discuss the physical meaning of the terms appearing in the equation
1443 < as well as the calculation of friction tensor from hydrodynamics
1444 < theory.
1439 > discuss the physical meaning of the terms appearing in the equation.
1440  
1441   \subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation}
1442  
# Line 1450 | Line 1445 | Dynamics (GLE). Lets consider a system, in which the d
1445   environment, has been widely used in quantum chemistry and
1446   statistical mechanics. One of the successful applications of
1447   Harmonic bath model is the derivation of the Generalized Langevin
1448 < Dynamics (GLE). Lets consider a system, in which the degree of
1448 > Dynamics (GLE). Consider a system, in which the degree of
1449   freedom $x$ is assumed to couple to the bath linearly, giving a
1450   Hamiltonian of the form
1451   \begin{equation}
# Line 1461 | Line 1456 | H_B  = \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_
1456   with this degree of freedom, $H_B$ is a harmonic bath Hamiltonian,
1457   \[
1458   H_B  = \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
1459 < }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  \omega _\alpha ^2 }
1459 > }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  x_\alpha ^2 }
1460   \right\}}
1461   \]
1462   where the index $\alpha$ runs over all the bath degrees of freedom,
# Line 1514 | Line 1509 | Operator. Below are some important properties of Lapla
1509   L(f(t)) \equiv F(p) = \int_0^\infty  {f(t)e^{ - pt} dt}
1510   \]
1511   where  $p$ is real and  $L$ is called the Laplace Transform
1512 < Operator. Below are some important properties of Laplace transform
1512 > Operator. Below are some important properties of the Laplace transform
1513   \begin{eqnarray*}
1514   L(x + y)  & = & L(x) + L(y) \\
1515   L(ax)     & = & aL(x) \\
# Line 1583 | Line 1578 | which is known as the \emph{generalized Langevin equat
1578   (t)\dot x(t - \tau )d\tau }  + R(t)
1579   \label{introEuqation:GeneralizedLangevinDynamics}
1580   \end{equation}
1581 < which is known as the \emph{generalized Langevin equation}.
1581 > which is known as the \emph{generalized Langevin equation} (GLE).
1582  
1583   \subsubsection{\label{introSection:randomForceDynamicFrictionKernel}\textbf{Random Force and Dynamic Friction Kernel}}
1584  
1585   One may notice that $R(t)$ depends only on initial conditions, which
1586   implies it is completely deterministic within the context of a
1587   harmonic bath. However, it is easy to verify that $R(t)$ is totally
1588 < uncorrelated to $x$ and $\dot x$,$\left\langle {x(t)R(t)}
1588 > uncorrelated to $x$ and $\dot x$, $\left\langle {x(t)R(t)}
1589   \right\rangle  = 0, \left\langle {\dot x(t)R(t)} \right\rangle  =
1590   0.$ This property is what we expect from a truly random process. As
1591   long as the model chosen for $R(t)$ was a gaussian distribution in
# Line 1619 | Line 1614 | taken as a $delta$ function in time:
1614   infinitely quickly to motions in the system. Thus, $\xi (t)$ can be
1615   taken as a $delta$ function in time:
1616   \[
1617 < \xi (t) = 2\xi _0 \delta (t)
1617 > \xi (t) = 2\xi _0 \delta (t).
1618   \]
1619   Hence, the convolution integral becomes
1620   \[
# Line 1644 | Line 1639 | we can rewrite $R(T)$ as
1639   q_\alpha  (t) = x_\alpha  (t) - \frac{1}{{m_\alpha  \omega _\alpha
1640   ^2 }}x(0),
1641   \]
1642 < we can rewrite $R(T)$ as
1642 > we can rewrite $R(t)$ as
1643   \[
1644   R(t) = \sum\limits_{\alpha  = 1}^N {g_\alpha  q_\alpha  (t)}.
1645   \]

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