--- trunk/mattDisertation/Introduction.tex	2004/01/23 02:14:04	978
+++ trunk/mattDisertation/Introduction.tex	2004/02/03 21:30:23	1014
@@ -3,21 +3,18 @@
 \chapter{\label{chapt:intro}Introduction and Theoretical Background}
 
 
-
-\section{\label{introSec:theory}Theoretical Background}
-
 The techniques used in the course of this research fall under the two
 main classes of molecular simulation: Molecular Dynamics and Monte
 Carlo. Molecular Dynamic simulations integrate the equations of motion
-for a given system of particles, allowing the researher to gain
+for a given system of particles, allowing the researcher to gain
 insight into the time dependent evolution of a system. Diffusion
 phenomena are readily studied with this simulation technique, making
 Molecular Dynamics the main simulation technique used in this
 research. Other aspects of the research fall under the Monte Carlo
 class of simulations. In Monte Carlo, the configuration space
-available to the collection of particles is sampled stochastichally,
+available to the collection of particles is sampled stochastically,
 or randomly. Each configuration is chosen with a given probability
-based on the Maxwell Boltzman distribution. These types of simulations
+based on the Maxwell Boltzmann distribution. These types of simulations
 are best used to probe properties of a system that are only dependent
 only on the state of the system. Structural information about a system
 is most readily obtained through these types of methods.
@@ -30,14 +27,174 @@ which method best suits that objective.
 thermodynamic properties of the system are being probed, then chose
 which method best suits that objective.
 
-\subsection{\label{introSec:statThermo}Statistical Thermodynamics}
+\section{\label{introSec:statThermo}Statistical Mechanics}
 
-ergodic hypothesis
+The following section serves as a brief introduction to some of the
+Statistical Mechanics concepts present in this dissertation.  What
+follows is a brief derivation of Boltzmann weighted statistics, and an
+explanation of how one can use the information to calculate an
+observable for a system.  This section then concludes with a brief
+discussion of the ergodic hypothesis and its relevance to this
+research.
 
-enesemble averages
+\subsection{\label{introSec:boltzman}Boltzmann weighted statistics}
 
-\subsection{\label{introSec:monteCarlo}Monte Carlo Simulations}
+Consider a system, $\gamma$, with some total energy,, $E_{\gamma}$.
+Let $\Omega(E_{\gamma})$ represent the number of degenerate ways
+$\boldsymbol{\Gamma}$, the collection of positions and conjugate
+momenta of system $\gamma$, can be configured to give
+$E_{\gamma}$. Further, if $\gamma$ is in contact with a bath system
+where energy is exchanged between the two systems, $\Omega(E)$, where
+$E$ is the total energy of both systems, can be represented as
+\begin{equation}
+\Omega(E) = \Omega(E_{\gamma}) \times \Omega(E - E_{\gamma})
+\label{introEq:SM1}
+\end{equation}
+Or additively as 
+\begin{equation}
+\ln \Omega(E) = \ln \Omega(E_{\gamma}) + \ln \Omega(E - E_{\gamma})
+\label{introEq:SM2}
+\end{equation}
 
+The solution to Eq.~\ref{introEq:SM2} maximizes the number of
+degenerative configurations in $E$. \cite{Frenkel1996} 
+This gives
+\begin{equation}
+\frac{\partial \ln \Omega(E)}{\partial E_{\gamma}} = 0 =
+	\frac{\partial \ln \Omega(E_{\gamma})}{\partial E_{\gamma}}
+	 + 
+	\frac{\partial \ln \Omega(E_{\text{bath}})}{\partial E_{\text{bath}}}
+	\frac{\partial E_{\text{bath}}}{\partial E_{\gamma}}
+\label{introEq:SM3}
+\end{equation}
+Where $E_{\text{bath}}$ is $E-E_{\gamma}$, and
+$\frac{\partial E_{\text{bath}}}{\partial E_{\gamma}}$ is
+$-1$. Eq.~\ref{introEq:SM3} becomes
+\begin{equation}
+\frac{\partial \ln \Omega(E_{\gamma})}{\partial E_{\gamma}} = 
+\frac{\partial \ln \Omega(E_{\text{bath}})}{\partial E_{\text{bath}}}
+\label{introEq:SM4}
+\end{equation}
+
+At this point, one can draw a relationship between the maximization of
+degeneracy in Eq.~\ref{introEq:SM3} and the second law of
+thermodynamics.  Namely, that for a closed system, entropy will
+increase for an irreversible process.\cite{chandler:1987} Here the
+process is the partitioning of energy among the two systems.  This
+allows the following definition of entropy:
+\begin{equation}
+S = k_B \ln \Omega(E)
+\label{introEq:SM5}
+\end{equation}
+Where $k_B$ is the Boltzmann constant.  Having defined entropy, one can
+also define the temperature of the system using the relation
+\begin{equation}
+\frac{1}{T} = \biggl ( \frac{\partial S}{\partial E} \biggr )_{N,V}
+\label{introEq:SM6}
+\end{equation}
+The temperature in the system $\gamma$ is then
+\begin{equation}
+\beta( E_{\gamma} ) = \frac{1}{k_B T} = 
+	\frac{\partial \ln \Omega(E_{\gamma})}{\partial E_{\gamma}}
+\label{introEq:SM7}
+\end{equation}
+Applying this to Eq.~\ref{introEq:SM4} gives the following
+\begin{equation}
+\beta( E_{\gamma} ) = \beta( E_{\text{bath}} )
+\label{introEq:SM8}
+\end{equation}
+Showing that the partitioning of energy between the two systems is
+actually a process of thermal equilibration.\cite{Frenkel1996}
+
+An application of these results is to formulate the form of an
+expectation value of an observable, $A$, in the canonical ensemble. In
+the canonical ensemble, the number of particles, $N$, the volume, $V$,
+and the temperature, $T$, are all held constant while the energy, $E$,
+is allowed to fluctuate. Returning to the previous example, the bath
+system is now an infinitely large thermal bath, whose exchange of
+energy with the system $\gamma$ holds the temperature constant.  The
+partitioning of energy in the bath system is then related to the total
+energy of both systems and the fluctuations in $E_{\gamma}$:
+\begin{equation}
+\Omega( E_{\gamma} ) = \Omega( E - E_{\gamma} )
+\label{introEq:SM9}
+\end{equation}
+As for the expectation value, it can be defined
+\begin{equation}
+\langle A \rangle = 
+	\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} 
+	P_{\gamma} A(\boldsymbol{\Gamma})
+\label{introEq:SM10}
+\end{equation}
+Where $\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma}$ denotes
+an integration over all accessible phase space, $P_{\gamma}$ is the
+probability of being in a given phase state and
+$A(\boldsymbol{\Gamma})$ is some observable that is a function of the
+phase state.
+
+Because entropy seeks to maximize the number of degenerate states at a
+given energy, the probability of being in a particular state in
+$\gamma$ will be directly proportional to the number of allowable
+states the coupled system is able to assume. Namely,
+\begin{equation}
+P_{\gamma} \propto \Omega( E_{\text{bath}} ) = 
+	e^{\ln \Omega( E - E_{\gamma})}
+\label{introEq:SM11}
+\end{equation}
+With $E_{\gamma} \ll E$, $\ln \Omega$ can be expanded in a Taylor series:
+\begin{equation}
+\ln \Omega ( E - E_{\gamma}) = 
+	\ln \Omega (E) -
+	E_{\gamma}  \frac{\partial \ln \Omega }{\partial E}
+	+ \ldots
+\label{introEq:SM12}
+\end{equation}
+Higher order terms are omitted as $E$ is an infinite thermal
+bath. Further, using Eq.~\ref{introEq:SM7}, Eq.~\ref{introEq:SM11} can
+be rewritten:
+\begin{equation}
+P_{\gamma} \propto e^{-\beta E_{\gamma}}
+\label{introEq:SM13}
+\end{equation}
+Where $\ln \Omega(E)$ has been factored out of the proportionality as a
+constant.  Normalizing the probability ($\int\limits_{\boldsymbol{\Gamma}}
+d\boldsymbol{\Gamma} P_{\gamma} = 1$) gives 
+\begin{equation}
+P_{\gamma} = \frac{e^{-\beta E_{\gamma}}}
+{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}}
+\label{introEq:SM14}
+\end{equation}
+This result is the standard Boltzmann statistical distribution.
+Applying it to Eq.~\ref{introEq:SM10} one can obtain the following relation for ensemble averages:
+\begin{equation}
+\langle A \rangle = 
+\frac{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} 
+	A( \boldsymbol{\Gamma} ) e^{-\beta E_{\gamma}}}
+{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}}
+\label{introEq:SM15}
+\end{equation}
+
+\subsection{\label{introSec:ergodic}The Ergodic Hypothesis}
+
+One last important consideration is that of ergodicity. Ergodicity is
+the assumption that given an infinite amount of time, a system will
+visit every available point in phase space.\cite{Frenkel1996} For most
+systems, this is a valid assumption, except in cases where the system
+may be trapped in a local feature (\emph{e.g.}~glasses). When valid,
+ergodicity allows the unification of a time averaged observation and
+an ensemble averaged one. If an observation is averaged over a
+sufficiently long time, the system is assumed to visit all
+appropriately available points in phase space, giving a properly
+weighted statistical average. This allows the researcher freedom of
+choice when deciding how best to measure a given observable.  When an
+ensemble averaged approach seems most logical, the Monte Carlo
+techniques described in Sec.~\ref{introSec:monteCarlo} can be utilized.
+Conversely, if a problem lends itself to a time averaging approach,
+the Molecular Dynamics techniques in Sec.~\ref{introSec:MD} can be
+employed.
+
+\section{\label{introSec:monteCarlo}Monte Carlo Simulations}
+
 The Monte Carlo method was developed by Metropolis and Ulam for their
 work in fissionable material.\cite{metropolis:1949} The method is so
 named, because it heavily uses random numbers in its
@@ -60,7 +217,7 @@ the value of $f(x)$ at each point. The accumulated ave
 $[a,b]$. The calculation of the integral could then be solved by
 randomly choosing points along the interval $[a,b]$ and calculating
 the value of $f(x)$ at each point. The accumulated average would then
-approach $I$ in the limit where the number of trials is infintely
+approach $I$ in the limit where the number of trials is infinitely
 large.
 
 However, in Statistical Mechanics, one is typically interested in
@@ -72,15 +229,15 @@ Where $\mathbf{r}^N$ stands for the coordinates of all
 \label{eq:mcEnsAvg}
 \end{equation}
 Where $\mathbf{r}^N$ stands for the coordinates of all $N$ particles
-and $A$ is some observable that is only dependent on
-position. $\langle A \rangle$ is the ensemble average of $A$ as
-presented in Sec.~\ref{introSec:statThermo}. Because $A$ is
-independent of momentum, the momenta contribution of the integral can
-be factored out, leaving the configurational integral. Application of
-the brute force method to this system would yield highly inefficient
-results. Due to the Boltzman weighting of this integral, most random
+and $A$ is some observable that is only dependent on position. This is
+the ensemble average of $A$ as presented in
+Sec.~\ref{introSec:statThermo}, except here $A$ is independent of
+momentum. Therefore the momenta contribution of the integral can be
+factored out, leaving the configurational integral. Application of the
+brute force method to this system would yield highly inefficient
+results. Due to the Boltzmann weighting of this integral, most random
 configurations will have a near zero contribution to the ensemble
-average. This is where a importance sampling comes into
+average. This is where importance sampling comes into
 play.\cite{allen87:csl}
 
 Importance Sampling is a method where one selects a distribution from
@@ -106,7 +263,7 @@ Looking at Eq.~\ref{eq:mcEnsAvg}, and realizing
 	{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}}
 \label{introEq:MCboltzman}
 \end{equation}
-Where $\rho_{kT}$ is the boltzman distribution.  The ensemble average
+Where $\rho_{kT}$ is the Boltzmann distribution.  The ensemble average
 can be rewritten as
 \begin{equation}
 \langle A \rangle = \int d^N \mathbf{r}~A(\mathbf{r}^N) 
@@ -128,11 +285,11 @@ sampled from the distribution $\rho_{kT}(\mathbf{r}^N)
 \end{equation}
 The difficulty is selecting points $\mathbf{r}^N$ such that they are
 sampled from the distribution $\rho_{kT}(\mathbf{r}^N)$.  A solution
-was proposed by Metropolis et al.\cite{metropolis:1953} which involved
+was proposed by Metropolis \emph{et al}.\cite{metropolis:1953} which involved
 the use of a Markov chain whose limiting distribution was
 $\rho_{kT}(\mathbf{r}^N)$.
 
-\subsubsection{\label{introSec:markovChains}Markov Chains}
+\subsection{\label{introSec:markovChains}Markov Chains}
 
 A Markov chain is a chain of states satisfying the following
 conditions:\cite{leach01:mm}
@@ -140,8 +297,8 @@ conditions:\cite{leach01:mm}
 \item The outcome of each trial depends only on the outcome of the previous trial.
 \item Each trial belongs to a finite set of outcomes called the state space.
 \end{enumerate}
-If given two configuartions, $\mathbf{r}^N_m$ and $\mathbf{r}^N_n$,
-$\rho_m$ and $\rho_n$ are the probablilities of being in state
+If given two configurations, $\mathbf{r}^N_m$ and $\mathbf{r}^N_n$,
+$\rho_m$ and $\rho_n$ are the probabilities of being in state
 $\mathbf{r}^N_m$ and $\mathbf{r}^N_n$ respectively.  Further, the two
 states are linked by a transition probability, $\pi_{mn}$, which is the
 probability of going from state $m$ to state $n$.
@@ -182,11 +339,11 @@ will only yield the limiting distribution again.
 \label{introEq:MCmarkovEquil}
 \end{equation}
 
-\subsubsection{\label{introSec:metropolisMethod}The Metropolis Method}
+\subsection{\label{introSec:metropolisMethod}The Metropolis Method}
 
 In the Metropolis method\cite{metropolis:1953}
 Eq.~\ref{introEq:MCmarkovEquil} is solved such that
-$\boldsymbol{\rho}_{\text{limit}}$ matches the Boltzman distribution
+$\boldsymbol{\rho}_{\text{limit}}$ matches the Boltzmann distribution
 of states.  The method accomplishes this by imposing the strong
 condition of microscopic reversibility on the equilibrium
 distribution.  Meaning, that at equilibrium the probability of going
@@ -195,7 +352,7 @@ from $m$ to $n$ is the same as going from $n$ to $m$.
 \rho_m\pi_{mn} = \rho_n\pi_{nm}
 \label{introEq:MCmicroReverse}
 \end{equation}
-Further, $\boldsymbol{\alpha}$ is chosen to be a symetric matrix in
+Further, $\boldsymbol{\alpha}$ is chosen to be a symmetric matrix in
 the Metropolis method.  Using Eq.~\ref{introEq:MCpi},
 Eq.~\ref{introEq:MCmicroReverse} becomes
 \begin{equation}
@@ -203,7 +360,7 @@ Eq.~\ref{introEq:MCmicroReverse} becomes
 	\frac{\rho_n}{\rho_m}
 \label{introEq:MCmicro2}
 \end{equation}
-For a Boltxman limiting distribution,
+For a Boltzmann limiting distribution,
 \begin{equation}
 \frac{\rho_n}{\rho_m} = e^{-\beta[\mathcal{U}(n) - \mathcal{U}(m)]}
 	= e^{-\beta \Delta \mathcal{U}}
@@ -211,25 +368,30 @@ This allows for the following set of acceptance rules 
 \end{equation}
 This allows for the following set of acceptance rules be defined:
 \begin{equation}
-EQ Here
+\accMe( m \rightarrow n ) = 
+	\begin{cases}
+	1& \text{if $\Delta \mathcal{U} \leq 0$,} \\
+	e^{-\beta \Delta \mathcal{U}}& \text{if $\Delta \mathcal{U} > 0$.}
+	\end{cases}
+\label{introEq:accRules}
 \end{equation}
 
-Using the acceptance criteria from Eq.~\ref{fix} the Metropolis method
-proceeds as follows
-\begin{itemize}
-\item Generate an initial configuration $fix$ which has some finite probability in $fix$.
-\item Modify $fix$, to generate configuratioon $fix$.
-\item If configuration $n$ lowers the energy of the system, accept the move with unity ($fix$ becomes $fix$).  Otherwise accept with probability $fix$.
-\item Accumulate the average for the configurational observable of intereest.
-\item Repeat from step 2 until average converges.
-\end{itemize}
+Using the acceptance criteria from Eq.~\ref{introEq:accRules} the
+Metropolis method proceeds as follows
+\begin{enumerate}
+\item Generate an initial configuration $\mathbf{r}^N$ which has some finite probability in $\rho_{kT}$.
+\item Modify $\mathbf{r}^N$, to generate configuration $\mathbf{r^{\prime}}^N$.
+\item If the new configuration lowers the energy of the system, accept the move with unity ($\mathbf{r}^N$ becomes $\mathbf{r^{\prime}}^N$).  Otherwise accept with probability $e^{-\beta \Delta \mathcal{U}}$.
+\item Accumulate the average for the configurational observable of interest.
+\item Repeat from step 2 until the average converges.
+\end{enumerate}
 One important note is that the average is accumulated whether the move
 is accepted or not, this ensures proper weighting of the average.
-Using Eq.~\ref{fix} it becomes clear that the accumulated averages are
-the ensemble averages, as this method ensures that the limiting
-distribution is the Boltzman distribution.
+Using Eq.~\ref{introEq:Importance4} it becomes clear that the
+accumulated averages are the ensemble averages, as this method ensures
+that the limiting distribution is the Boltzmann distribution.
 
-\subsection{\label{introSec:MD}Molecular Dynamics Simulations}
+\section{\label{introSec:MD}Molecular Dynamics Simulations}
 
 The main simulation tool used in this research is Molecular Dynamics.
 Molecular Dynamics is when the equations of motion for a system are
@@ -237,112 +399,135 @@ diffusion constants, velocity auto correlations, foldi
 momentum of a system, allowing the calculation of not only
 configurational observables, but momenta dependent ones as well:
 diffusion constants, velocity auto correlations, folding/unfolding
-events, etc.  Due to the principle of ergodicity, Eq.~\ref{fix}, the
-average of these observables over the time period of the simulation
-are taken to be the ensemble averages for the system.
+events, etc.  Due to the principle of ergodicity,
+Sec.~\ref{introSec:ergodic}, the average of these observables over the
+time period of the simulation are taken to be the ensemble averages
+for the system.
 
 The choice of when to use molecular dynamics over Monte Carlo
 techniques, is normally decided by the observables in which the
-researcher is interested.  If the observabvles depend on momenta in
+researcher is interested.  If the observables depend on momenta in
 any fashion, then the only choice is molecular dynamics in some form.
 However, when the observable is dependent only on the configuration,
-then most of the time Monte Carlo techniques will be more efficent.
+then most of the time Monte Carlo techniques will be more efficient.
 
 The focus of research in the second half of this dissertation is
 centered around the dynamic properties of phospholipid bilayers,
 making molecular dynamics key in the simulation of those properties.
 
-\subsubsection{Molecular dynamics Algorithm}
+\subsection{\label{introSec:mdAlgorithm}The Molecular Dynamics Algorithm}
 
 To illustrate how the molecular dynamics technique is applied, the
 following sections will describe the sequence involved in a
-simulation.  Sec.~\ref{fix} deals with the initialization of a
-simulation.  Sec.~\ref{fix} discusses issues involved with the
-calculation of the forces.  Sec.~\ref{fix} concludes the algorithm
-discussion with the integration of the equations of motion. \cite{fix}
+simulation.  Sec.~\ref{introSec:mdInit} deals with the initialization
+of a simulation.  Sec.~\ref{introSec:mdForce} discusses issues
+involved with the calculation of the forces.
+Sec.~\ref{introSec:mdIntegrate} concludes the algorithm discussion
+with the integration of the equations of motion.\cite{Frenkel1996}
 
-\subsubsection{initialization}
+\subsection{\label{introSec:mdInit}Initialization}
 
 When selecting the initial configuration for the simulation it is
 important to consider what dynamics one is hoping to observe.
-Ch.~\ref{fix} deals with the formation and equilibrium dynamics of
+Ch.~\ref{chapt:lipid} deals with the formation and equilibrium dynamics of
 phospholipid membranes.  Therefore in these simulations initial
 positions were selected that in some cases dispersed the lipids in
-water, and in other cases structured the lipids into preformed
+water, and in other cases structured the lipids into performed
 bilayers.  Important considerations at this stage of the simulation are:
 \begin{itemize}
 \item There are no major overlaps of molecular or atomic orbitals
-\item Velocities are chosen in such a way as to not gie the system a non=zero total momentum or angular momentum.
-\item It is also sometimes desireable to select the velocities to correctly sample the target temperature.
+\item Velocities are chosen in such a way as to not give the system a non=zero total momentum or angular momentum.
+\item It is also sometimes desirable to select the velocities to correctly sample the target temperature.
 \end{itemize}
 
 The first point is important due to the amount of potential energy
 generated by having two particles too close together.  If overlap
 occurs, the first evaluation of forces will return numbers so large as
-to render the numerical integration of teh motion meaningless.  The
+to render the numerical integration of the motion meaningless.  The
 second consideration keeps the system from drifting or rotating as a
 whole.  This arises from the fact that most simulations are of systems
 in equilibrium in the absence of outside forces.  Therefore any net
 movement would be unphysical and an artifact of the simulation method
-used.  The final point addresses teh selection of the magnitude of the
-initial velocities.  For many simulations it is convienient to use
+used.  The final point addresses the selection of the magnitude of the
+initial velocities.  For many simulations it is convenient to use
 this opportunity to scale the amount of kinetic energy to reflect the
 desired thermal distribution of the system.  However, it must be noted
 that most systems will require further velocity rescaling after the
 first few initial simulation steps due to either loss or gain of
 kinetic energy from energy stored in potential degrees of freedom.
 
-\subsubsection{Force Evaluation}
+\subsection{\label{introSec:mdForce}Force Evaluation}
 
 The evaluation of forces is the most computationally expensive portion
 of a given molecular dynamics simulation.  This is due entirely to the
 evaluation of long range forces in a simulation, typically pair-wise.
 These forces are most commonly the Van der Waals force, and sometimes
-Coulombic forces as well.  For a pair-wise force, there are $fix$
-pairs to be evaluated, where $n$ is the number of particles in the
-system.  This leads to the calculations scaling as $fix$, making large
+Coulombic forces as well.  For a pair-wise force, there are $N(N-1)/ 2$
+pairs to be evaluated, where $N$ is the number of particles in the
+system.  This leads to the calculations scaling as $N^2$, making large
 simulations prohibitive in the absence of any computation saving
 techniques.
 
 Another consideration one must resolve, is that in a given simulation
 a disproportionate number of the particles will feel the effects of
-the surface. \cite{fix} For a cubic system of 1000 particles arranged
-in a $10x10x10$ cube, 488 particles will be exposed to the surface.
-Unless one is simulating an isolated particle group in a vacuum, the
-behavior of the system will be far from the desired bulk
-charecteristics.  To offset this, simulations employ the use of
-periodic boundary images. \cite{fix}
+the surface.\cite{allen87:csl} For a cubic system of 1000 particles
+arranged in a $10 \times 10 \times 10$ cube, 488 particles will be
+exposed to the surface.  Unless one is simulating an isolated particle
+group in a vacuum, the behavior of the system will be far from the
+desired bulk characteristics.  To offset this, simulations employ the
+use of periodic boundary images.\cite{born:1912}
 
 The technique involves the use of an algorithm that replicates the
-simulation box on an infinite lattice in cartesian space.  Any given
+simulation box on an infinite lattice in Cartesian space.  Any given
 particle leaving the simulation box on one side will have an image of
-itself enter on the opposite side (see Fig.~\ref{fix}).
-\begin{equation}
-EQ Here
-\end{equation}
-In addition, this sets that any given particle pair has an image, real
-or periodic, within $fix$ of each other.  A discussion of the method
-used to calculate the periodic image can be found in Sec.\ref{fix}.
+itself enter on the opposite side (see Fig.~\ref{introFig:pbc}).  In
+addition, this sets that any two particles have an image, real or
+periodic, within $\text{box}/2$ of each other.  A discussion of the
+method used to calculate the periodic image can be found in
+Sec.\ref{oopseSec:pbc}.
 
+\begin{figure}
+\centering
+\includegraphics[width=\linewidth]{pbcFig.eps}
+\caption[An illustration of periodic boundary conditions]{A 2-D illustration of periodic boundary conditions. As one particle leaves the right of the simulation box, an image of it enters the left.}
+\label{introFig:pbc}
+\end{figure}
+
 Returning to the topic of the computational scale of the force
 evaluation, the use of periodic boundary conditions requires that a
 cutoff radius be employed.  Using a cutoff radius improves the
 efficiency of the force evaluation, as particles farther than a
-predetermined distance, $fix$, are not included in the
-calculation. \cite{fix} In a simultation with periodic images, $fix$
-has a maximum value of $fix$.  Fig.~\ref{fix} illustrates how using an
-$fix$ larger than this value, or in the extreme limit of no $fix$ at
-all, the corners of the simulation box are unequally weighted due to
-the lack of particle images in the $x$, $y$, or $z$ directions past a
-disance of $fix$.
+predetermined distance, $r_{\text{cut}}$, are not included in the
+calculation.\cite{Frenkel1996} In a simulation with periodic images,
+$r_{\text{cut}}$ has a maximum value of $\text{box}/2$.
+Fig.~\ref{introFig:rMax} illustrates how when using an
+$r_{\text{cut}}$ larger than this value, or in the extreme limit of no
+$r_{\text{cut}}$ at all, the corners of the simulation box are
+unequally weighted due to the lack of particle images in the $x$, $y$,
+or $z$ directions past a distance of $\text{box} / 2$.
 
-With the use of an $fix$, however, comes a discontinuity in the
-potential energy curve (Fig.~\ref{fix}). To fix this discontinuity,
-one calculates the potential energy at the $r_{\text{cut}}$, and add
-that value to the potential.  This causes the function to go smoothly
-to zero at the cutoff radius.  This ensures conservation of energy
-when integrating the Newtonian equations of motion.
+\begin{figure}
+\centering
+\includegraphics[width=\linewidth]{rCutMaxFig.eps}
+\caption[An explanation of $r_{\text{cut}}$]{The yellow atom has all other images wrapped to itself as the center. If $r_{\text{cut}}=\text{box}/2$, then the distribution is uniform (blue atoms). However, when $r_{\text{cut}}>\text{box}/2$ the corners are disproportionately weighted (green atoms) vs the axial directions (shaded regions).}
+\label{introFig:rMax}
+\end{figure}
+
+With the use of an $r_{\text{cut}}$, however, comes a discontinuity in
+the potential energy curve (Fig.~\ref{introFig:shiftPot}). To fix this
+discontinuity, one calculates the potential energy at the
+$r_{\text{cut}}$, and adds that value to the potential, causing
+the function to go smoothly to zero at the cutoff radius.  This
+shifted potential ensures conservation of energy when integrating the
+Newtonian equations of motion.
 
+\begin{figure}
+\centering
+\includegraphics[width=\linewidth]{shiftedPot.eps}
+\caption[Shifting the Lennard-Jones Potential]{The Lennard-Jones potential (blue line) is shifted (red line) to remove the discontinuity at $r_{\text{cut}}$.}
+\label{introFig:shiftPot}
+\end{figure}
+
 The second main simplification used in this research is the Verlet
 neighbor list. \cite{allen87:csl} In the Verlet method, one generates
 a list of all neighbor atoms, $j$, surrounding atom $i$ within some
@@ -354,51 +539,58 @@ neighbor list.
 giving rise to the possibility that a particle has left or joined a
 neighbor list.
 
-\subsection{\label{introSec:MDintegrate} Integration of the equations of motion}
+\subsection{\label{introSec:mdIntegrate} Integration of the equations of motion}
 
 A starting point for the discussion of molecular dynamics integrators
-is the Verlet algorithm. \cite{Frenkel1996} It begins with a Taylor
+is the Verlet algorithm.\cite{Frenkel1996} It begins with a Taylor
 expansion of position in time:
 \begin{equation}
-eq here
+q(t+\Delta t)= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 +
+	\frac{\Delta t^3}{3!}\frac{\partial q(t)}{\partial t} +
+	\mathcal{O}(\Delta t^4) 
 \label{introEq:verletForward}
 \end{equation}
 As well as,
 \begin{equation}
-eq here
+q(t-\Delta t)= q(t) - v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 -
+	\frac{\Delta t^3}{3!}\frac{\partial q(t)}{\partial t} +
+	\mathcal{O}(\Delta t^4) 
 \label{introEq:verletBack}
 \end{equation}
-Adding together Eq.~\ref{introEq:verletForward} and
+Where $m$ is the mass of the particle, $q(t)$ is the position at time
+$t$, $v(t)$ the velocity, and $F(t)$ the force acting on the
+particle. Adding together Eq.~\ref{introEq:verletForward} and
 Eq.~\ref{introEq:verletBack} results in,
 \begin{equation}
-eq here
+q(t+\Delta t)+q(t-\Delta t) = 
+	2q(t) + \frac{F(t)}{m}\Delta t^2 + \mathcal{O}(\Delta t^4) 
 \label{introEq:verletSum}
 \end{equation}
 Or equivalently,
 \begin{equation}
-eq here
+q(t+\Delta t) \approx 
+	2q(t) - q(t-\Delta t) + \frac{F(t)}{m}\Delta t^2
 \label{introEq:verletFinal}
 \end{equation}
 Which contains an error in the estimate of the new positions on the
 order of $\Delta t^4$.
 
 In practice, however, the simulations in this research were integrated
-with a velocity reformulation of teh Verlet method. \cite{allen87:csl}
-\begin{equation}
-eq here
-\label{introEq:MDvelVerletPos}
-\end{equation}
-\begin{equation}
-eq here
+with a velocity reformulation of the Verlet method.\cite{allen87:csl}
+\begin{align}
+q(t+\Delta t) &= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 %
+\label{introEq:MDvelVerletPos} \\%
+%
+v(t+\Delta t) &= v(t) + \frac{\Delta t}{2m}[F(t) + F(t+\Delta t)] %
 \label{introEq:MDvelVerletVel}
-\end{equation}
+\end{align}
 The original Verlet algorithm can be regained by substituting the
 velocity back into Eq.~\ref{introEq:MDvelVerletPos}.  The Verlet
 formulations are chosen in this research because the algorithms have
 very little long term drift in energy conservation.  Energy
 conservation in a molecular dynamics simulation is of extreme
 importance, as it is a measure of how closely one is following the
-``true'' trajectory wtih the finite integration scheme.  An exact
+``true'' trajectory with the finite integration scheme.  An exact
 solution to the integration will conserve area in phase space, as well
 as be reversible in time, that is, the trajectory integrated forward
 or backwards will exactly match itself.  Having a finite algorithm
@@ -406,62 +598,300 @@ integrated trajectory.
 therefore increases, but does not guarantee the ``correctness'' or the
 integrated trajectory.
 
-It can be shown, \cite{Frenkel1996} that although the Verlet algorithm
+It can be shown,\cite{Frenkel1996} that although the Verlet algorithm
 does not rigorously preserve the actual Hamiltonian, it does preserve
 a pseudo-Hamiltonian which shadows the real one in phase space.  This
-pseudo-Hamiltonian is proveably area-conserving as well as time
+pseudo-Hamiltonian is provably area-conserving as well as time
 reversible.  The fact that it shadows the true Hamiltonian in phase
 space is acceptable in actual simulations as one is interested in the
 ensemble average of the observable being measured.  From the ergodic
-hypothesis (Sec.~\ref{introSec:StatThermo}), it is known that the time
+hypothesis (Sec.~\ref{introSec:statThermo}), it is known that the time
 average will match the ensemble average, therefore two similar
 trajectories in phase space should give matching statistical averages.
 
-\subsection{\label{introSec:MDfurtheeeeer}Further Considerations}
+\subsection{\label{introSec:MDfurther}Further Considerations}
+
 In the simulations presented in this research, a few additional
 parameters are needed to describe the motions.  The simulations
-involving water and phospholipids in Chapt.~\ref{chaptLipids} are
+involving water and phospholipids in Ch.~\ref{chapt:lipid} are
 required to integrate the equations of motions for dipoles on atoms.
 This involves an additional three parameters be specified for each
 dipole atom: $\phi$, $\theta$, and $\psi$.  These three angles are
 taken to be the Euler angles, where $\phi$ is a rotation about the
 $z$-axis, and $\theta$ is a rotation about the new $x$-axis, and
 $\psi$ is a final rotation about the new $z$-axis (see
-Fig.~\ref{introFig:euleerAngles}).  This sequence of rotations can be
-accumulated into a single $3\time3$ matrix $\underline{\mathbf{A}}$
+Fig.~\ref{introFig:eulerAngles}).  This sequence of rotations can be
+accumulated into a single $3 \times 3$ matrix, $\mathbf{A}$,
 defined as follows:
 \begin{equation}
-eq here
+\mathbf{A} = 
+\begin{bmatrix}
+	\cos\phi\cos\psi-\sin\phi\cos\theta\sin\psi &%
+	\sin\phi\cos\psi+\cos\phi\cos\theta\sin\psi &%
+	\sin\theta\sin\psi \\%
+	%
+	-\cos\phi\sin\psi-\sin\phi\cos\theta\cos\psi &%
+	-\sin\phi\sin\psi+\cos\phi\cos\theta\cos\psi &%
+	\sin\theta\cos\psi \\%
+	%
+	\sin\phi\sin\theta &%
+	-\cos\phi\sin\theta &%
+	\cos\theta
+\end{bmatrix}
 \label{introEq:EulerRotMat}
 \end{equation}
 
-The equations of motion for Euler angles can be written down as
-\cite{allen87:csl}
-\begin{equation}
-eq here
-\label{introEq:MDeuleeerPsi}
-\end{equation}
+\begin{figure}
+\centering
+\includegraphics[width=\linewidth]{eulerRotFig.eps}
+\caption[Euler rotation of Cartesian coordinates]{The rotation scheme for Euler angles. First is a rotation of $\phi$ about the $z$ axis (blue rotation). Next is a rotation of $\theta$ about the new $x\prime$ axis (green rotation). Lastly is a final rotation of $\psi$ about the new $z\prime$ axis (red rotation).}
+\label{introFig:eulerAngles}
+\end{figure}
+
+The equations of motion for Euler angles can be written down
+as\cite{allen87:csl}
+\begin{align}
+\dot{\phi} &= -\omega^s_x \frac{\sin\phi\cos\theta}{\sin\theta} +
+	\omega^s_y \frac{\cos\phi\cos\theta}{\sin\theta} +
+	\omega^s_z
+\label{introEq:MDeulerPhi} \\%
+%
+\dot{\theta} &= \omega^s_x \cos\phi + \omega^s_y \sin\phi
+\label{introEq:MDeulerTheta} \\%
+%
+\dot{\psi} &= \omega^s_x \frac{\sin\phi}{\sin\theta} - 
+	\omega^s_y \frac{\cos\phi}{\sin\theta}
+\label{introEq:MDeulerPsi}
+\end{align}
 Where $\omega^s_i$ is the angular velocity in the lab space frame
-along cartesian coordinate $i$.  However, a difficulty arises when
-attempting to integrate Eq.~\ref{introEq:MDeuleerPhi} and
+along Cartesian coordinate $i$.  However, a difficulty arises when
+attempting to integrate Eq.~\ref{introEq:MDeulerPhi} and
 Eq.~\ref{introEq:MDeulerPsi}. The $\frac{1}{\sin \theta}$ present in
 both equations means there is a non-physical instability present when
-$\theta$ is 0 or $\pi$.
-
-To correct for this, the simulations integrate the rotation matrix,
-$\underline{\mathbf{A}}$, directly, thus avoiding the instability.
-This method was proposed by Dullwebber
-\emph{et. al.}\cite{Dullwebber:1997}, and is presented in
+$\theta$ is 0 or $\pi$. To correct for this, the simulations integrate
+the rotation matrix, $\mathbf{A}$, directly, thus avoiding the
+instability.  This method was proposed by Dullweber
+\emph{et. al.}\cite{Dullweber1997}, and is presented in
 Sec.~\ref{introSec:MDsymplecticRot}.
 
-\subsubsection{\label{introSec:MDliouville}Liouville Propagator}
+\subsection{\label{introSec:MDliouville}Liouville Propagator}
 
+Before discussing the integration of the rotation matrix, it is
+necessary to understand the construction of a ``good'' integration
+scheme.  It has been previously
+discussed(Sec.~\ref{introSec:mdIntegrate}) how it is desirable for an
+integrator to be symplectic, or time reversible.  The following is an
+outline of the Trotter factorization of the Liouville Propagator as a
+scheme for generating symplectic integrators.\cite{Tuckerman92}
 
-\section{\label{introSec:chapterLayout}Chapter Layout}
+For a system with $f$ degrees of freedom the Liouville operator can be
+defined as,
+\begin{equation}
+iL=\sum^f_{j=1} \biggl [\dot{q}_j\frac{\partial}{\partial q_j} +
+	F_j\frac{\partial}{\partial p_j} \biggr ]
+\label{introEq:LiouvilleOperator}
+\end{equation}
+Here, $q_j$ and $p_j$ are the position and conjugate momenta of a
+degree of freedom, and $F_j$ is the force on that degree of freedom.
+$\Gamma$ is defined as the set of all positions and conjugate momenta,
+$\{q_j,p_j\}$, and the propagator, $U(t)$, is defined
+\begin {equation}
+U(t) = e^{iLt}
+\label{introEq:Lpropagator}
+\end{equation}
+This allows the specification of $\Gamma$ at any time $t$ as 
+\begin{equation}
+\Gamma(t) = U(t)\Gamma(0)
+\label{introEq:Lp2}
+\end{equation}
+It is important to note, $U(t)$ is a unitary operator meaning
+\begin{equation}
+U(-t)=U^{-1}(t)
+\label{introEq:Lp3}
+\end{equation}
 
-\subsection{\label{introSec:RSA}Random Sequential Adsorption}
+Decomposing $L$ into two parts, $iL_1$ and $iL_2$, one can use the
+Trotter theorem to yield
+\begin{align}
+e^{iLt} &= e^{i(L_1 + L_2)t} \notag \\%
+%
+	&= \biggl [ e^{i(L_1 +L_2)\frac{t}{P}} \biggr]^P \notag \\%
+%
+	&= \biggl [ e^{iL_1\frac{\Delta t}{2}}\, e^{iL_2\Delta t}\,
+	e^{iL_1\frac{\Delta t}{2}} \biggr ]^P +
+	\mathcal{O}\biggl (\frac{t^3}{P^2} \biggr ) \label{introEq:Lp4}
+\end{align}
+Where $\Delta t = t/P$.
+With this, a discrete time operator $G(\Delta t)$ can be defined:
+\begin{align}
+G(\Delta t) &= e^{iL_1\frac{\Delta t}{2}}\, e^{iL_2\Delta t}\,
+	e^{iL_1\frac{\Delta t}{2}} \notag \\%
+%
+	&= U_1 \biggl ( \frac{\Delta t}{2} \biggr )\, U_2 ( \Delta t )\,
+	U_1 \biggl ( \frac{\Delta t}{2} \biggr )
+\label{introEq:Lp5}
+\end{align}
+Because $U_1(t)$ and $U_2(t)$ are unitary, $G(\Delta t)$ is also
+unitary.  Meaning an integrator based on this factorization will be
+reversible in time.
 
-\subsection{\label{introSec:OOPSE}The OOPSE Simulation Package}
+As an example, consider the following decomposition of $L$:
+\begin{align}
+iL_1 &= \dot{q}\frac{\partial}{\partial q}%
+\label{introEq:Lp6a} \\%
+%
+iL_2 &= F(q)\frac{\partial}{\partial p}%
+\label{introEq:Lp6b}
+\end{align}
+This leads to propagator $G( \Delta t )$ as,
+\begin{equation}
+G(\Delta t) =  e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}} \,
+	e^{\Delta t\,\dot{q}\frac{\partial}{\partial q}} \,
+	e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}}
+\label{introEq:Lp7}
+\end{equation}
+Operating $G(\Delta t)$ on $\Gamma(0)$, and utilizing the operator property
+\begin{equation}
+e^{c\frac{\partial}{\partial x}}\, f(x) = f(x+c)
+\label{introEq:Lp8}
+\end{equation}
+Where $c$ is independent of $x$.  One obtains the following:  
+\begin{align}
+\dot{q}\biggl (\frac{\Delta t}{2}\biggr ) &=
+	\dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)] \label{introEq:Lp9a}\\%
+%
+q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr )%
+	\label{introEq:Lp9b}\\%
+%
+\dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) +
+	\frac{\Delta t}{2m}\, F[q(0)] \label{introEq:Lp9c}
+\end{align}
+Or written another way,
+\begin{align}
+q(t+\Delta t) &= q(0) + \dot{q}(0)\Delta t + 
+	\frac{F[q(0)]}{m}\frac{\Delta t^2}{2} %
+\label{introEq:Lp10a} \\%
+%
+\dot{q}(\Delta t) &= \dot{q}(0) + \frac{\Delta t}{2m}
+	\biggl [F[q(0)] + F[q(\Delta t)] \biggr] %
+\label{introEq:Lp10b}
+\end{align}
+This is the velocity Verlet formulation presented in
+Sec.~\ref{introSec:mdIntegrate}.  Because this integration scheme is
+comprised of unitary propagators, it is symplectic, and therefore area
+preserving in phase space.  From the preceding factorization, 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.
 
-\subsection{\label{introSec:bilayers}A Mesoscale Model for
-Phospholipid Bilayers}
+\item Use the half step velocities to move positions one whole step, $\Delta t$.
+
+\item Evaluate the forces at the new positions, $\mathbf{r}(\Delta t)$, 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}
+
+\subsection{\label{introSec:MDsymplecticRot} Symplectic Propagation of the Rotation Matrix}
+
+Based on the factorization from the previous section,
+Dullweber\emph{et al}.\cite{Dullweber1997}~ proposed a scheme for the
+symplectic propagation of the rotation matrix, $\mathbf{A}$, as an
+alternative method for the integration of orientational degrees of
+freedom. The method starts with a straightforward splitting of the
+Liouville operator:
+\begin{align}
+iL_{\text{pos}} &= \dot{q}\frac{\partial}{\partial q} +
+	\mathbf{\dot{A}}\frac{\partial}{\partial \mathbf{A}} 
+\label{introEq:SR1a} \\%
+%
+iL_F &= F(q)\frac{\partial}{\partial p}
+\label{introEq:SR1b} \\%
+iL_{\tau} &= \tau(\mathbf{A})\frac{\partial}{\partial \pi}
+\label{introEq:SR1b} \\%
+\end{align}
+Where $\tau(\mathbf{A})$ is the torque of the system
+due to the configuration, and $\pi$ is the conjugate
+angular momenta of the system. The propagator, $G(\Delta t)$, becomes
+\begin{equation}
+G(\Delta t) = e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}} \,
+	e^{\frac{\Delta t}{2} \tau(\mathbf{A})\frac{\partial}{\partial \pi}} \,
+	e^{\Delta t\,iL_{\text{pos}}} \,
+	e^{\frac{\Delta t}{2} \tau(\mathbf{A})\frac{\partial}{\partial \pi}} \,
+	e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}}
+\label{introEq:SR2}
+\end{equation}
+Propagation of the linear and angular momenta follows as in the Verlet
+scheme.  The propagation of positions also follows the Verlet scheme
+with the addition of a further symplectic splitting of the rotation
+matrix propagation, $\mathcal{U}_{\text{rot}}(\Delta t)$, within
+$U_{\text{pos}}(\Delta t)$.
+\begin{equation}
+\mathcal{U}_{\text{rot}}(\Delta t) = 
+	\mathcal{U}_x \biggl(\frac{\Delta t}{2}\biggr)\,
+	\mathcal{U}_y \biggl(\frac{\Delta t}{2}\biggr)\,
+	\mathcal{U}_z (\Delta t)\,
+	\mathcal{U}_y \biggl(\frac{\Delta t}{2}\biggr)\,
+	\mathcal{U}_x \biggl(\frac{\Delta t}{2}\biggr)\,
+\label{introEq:SR3}
+\end{equation}
+Where $\mathcal{U}_j$ is a unitary rotation of $\mathbf{A}$ and
+$\pi$ about each axis $j$.  As all propagations are now
+unitary and symplectic, the entire integration scheme is also
+symplectic and time reversible.
+
+\section{\label{introSec:layout}Dissertation Layout}
+
+This dissertation is divided as follows:Ch.~\ref{chapt:RSA}
+presents the random sequential adsorption simulations of related
+pthalocyanines on a gold (111) surface. Ch.~\ref{chapt:OOPSE}
+is about the writing of the molecular dynamics simulation package
+{\sc oopse}. Ch.~\ref{chapt:lipid} regards the simulations of
+phospholipid bilayers using a mesoscale model. And lastly,
+Ch.~\ref{chapt:conclusion} concludes this dissertation with a
+summary of all results. The chapters are arranged in chronological
+order, and reflect the progression of techniques I employed during my
+research.  
+
+The chapter concerning random sequential adsorption simulations is a
+study in applying Statistical Mechanics simulation techniques in order
+to obtain a simple model capable of explaining the results.  My
+advisor, Dr. Gezelter, and I were approached by a colleague,
+Dr. Lieberman, about possible explanations for the  partial coverage of a
+gold surface by a particular compound of hers. We suggested it might
+be due to the statistical packing fraction of disks on a plane, and
+set about to simulate this system.  As the events in our model were
+not dynamic in nature, a Monte Carlo method was employed.  Here, if a
+molecule landed on the surface without overlapping another, then its
+landing was accepted.  However, if there was overlap, the landing we
+rejected and a new random landing location was chosen.  This defined
+our acceptance rules and allowed us to construct a Markov chain whose
+limiting distribution was the surface coverage in which we were
+interested.
+
+The following chapter, about the simulation package {\sc oopse},
+describes in detail the large body of scientific code that had to be
+written in order to study phospholipid bilayers.  Although there are
+pre-existing molecular dynamic simulation packages available, none
+were capable of implementing the models we were developing.{\sc oopse}
+is a unique package capable of not only integrating the equations of
+motion in Cartesian space, but is also able to integrate the
+rotational motion of rigid bodies and dipoles.  Add to this the
+ability to perform calculations across parallel processors and a
+flexible script syntax for creating systems, and {\sc oopse} becomes a
+very powerful scientific instrument for the exploration of our model.
+
+Bringing us to Ch.~\ref{chapt:lipid}. Using {\sc oopse}, I have been
+able to parameterize a mesoscale model for phospholipid simulations.
+This model retains information about solvent ordering around the
+bilayer, as well as information regarding the interaction of the
+phospholipid head groups' dipoles with each other and the surrounding
+solvent.  These simulations give us insight into the dynamic events
+that lead to the formation of phospholipid bilayers, as well as
+provide the foundation for future exploration of bilayer phase
+behavior with this model.  
+
+Which leads into the last chapter, where I discuss future directions
+for both{\sc oopse} and this mesoscale model.  Additionally, I will
+give a summary of results for this dissertation.
+
+
