--- trunk/mattDisertation/Introduction.tex	2004/02/03 17:41:56	1008
+++ trunk/mattDisertation/Introduction.tex	2004/04/15 02:45:10	1112
@@ -1,37 +1,37 @@
 
+\chapter{\label{chapt:intro}INTRODUCTION AND THEORETICAL BACKGROUND}
 
-\chapter{\label{chapt:intro}Introduction and 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 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 stochastically,
-or randomly. Each configuration is chosen with a given probability
-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.
+Carlo. Molecular Dynamics simulations are carried out by integrating
+the equations of motion for a given system of particles, allowing the
+researcher to gain insight into the time dependent evolution of a
+system. Transport 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 in the
+Monte Carlo class of simulations. In Monte Carlo, the configuration
+space available to the collection of particles is sampled
+stochastically, or randomly. Each configuration is chosen with a given
+probability based on the Boltzmann distribution. These types
+of simulations are best used to probe properties of a system that are
+dependent only on the state of the system. Structural information
+about a system is most readily obtained through these types of
+methods.
 
 Although the two techniques employed seem dissimilar, they are both
-linked by the overarching principles of Statistical
-Thermodynamics. Statistical Thermodynamics governs the behavior of
+linked by the over-arching principles of Statistical
+Mechanics. Statistical Mechanics governs the behavior of
 both classes of simulations and dictates what each method can and
-cannot do. When investigating a system, one most first analyze what
-thermodynamic properties of the system are being probed, then chose
+cannot do. When investigating a system, one must first analyze what
+thermodynamic properties of the system are being probed, then choose
 which method best suits that objective.
 
 \section{\label{introSec:statThermo}Statistical Mechanics}
 
 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
+Statistical Mechanics concepts presented 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
@@ -39,40 +39,61 @@ Consider a system, $\gamma$, with some total energy,, 
 
 \subsection{\label{introSec:boltzman}Boltzmann weighted statistics}
 
-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
+Consider a system, $\gamma$, with total energy $E_{\gamma}$.  Let
+$\Omega(E_{\gamma})$ represent the number of degenerate ways
+$\boldsymbol{\Gamma}\{r_1,r_2,\ldots r_n,p_1,p_2,\ldots p_n\}$, the
+collection of positions and conjugate momenta of system $\gamma$, can
+be configured to give $E_{\gamma}$. Further, if $\gamma$ is a subset
+of a larger system, $\boldsymbol{\Lambda}\{E_1,E_2,\ldots
+E_{\gamma},\ldots E_n\}$, the total degeneracy of the system can be
+expressed as
 \begin{equation}
-\Omega(E) = \Omega(E_{\gamma}) \times \Omega(E - E_{\gamma})
+\Omega(\boldsymbol{\Lambda}) = \Omega(E_1) \times \Omega(E_2) \times \ldots
+	\Omega(E_{\gamma}) \times \ldots \Omega(E_n).
+\label{introEq:SM0.1}
+\end{equation}
+This multiplicative combination of degeneracies is illustrated in
+Fig.~\ref{introFig:degenProd}.
+
+\begin{figure}
+\centering
+\includegraphics[width=\linewidth]{omegaFig.eps}
+\caption[An explanation of the combination of degeneracies]{Systems A and B both have three energy levels and two indistinguishable particles. When the total energy is 2, there are two ways for each system to disperse the energy. However, for system C, the superset of A and B, the total degeneracy is the product of the degeneracy of each system. In this case $\Omega(\text{C})$ is 4.}
+\label{introFig:degenProd}
+\end{figure}
+
+Next, consider the specific case of $\gamma$ in contact with a
+bath. Exchange of energy is allowed between the bath and the system,
+subject to the constraint that the total energy
+($E_{\text{bath}}+E_{\gamma}$) remain constant. $\Omega(E)$, 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 
+Or additively as,
 \begin{equation}
-\ln \Omega(E) = \ln \Omega(E_{\gamma}) + \ln \Omega(E - E_{\gamma})
+\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} 
+The solution of interest to Eq.~\ref{introEq:SM2} maximizes the number of
+degenerate 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}}
+	\frac{\partial E_{\text{bath}}}{\partial E_{\gamma}},
 \label{introEq:SM3}
 \end{equation}
-Where $E_{\text{bath}}$ is $E-E_{\gamma}$, and
+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}}}
+\frac{\partial \ln \Omega(E_{\text{bath}})}{\partial E_{\text{bath}}}.
 \label{introEq:SM4}
 \end{equation}
 
@@ -80,56 +101,58 @@ process is the partitioning of energy among the two sy
 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
+maximization of the degeneracy when partitioning the energy of the system can be likened to the maximization of the entropy for this process. This
 allows the following definition of entropy:
 \begin{equation}
-S = k_B \ln \Omega(E)
+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
+where $k_B$ is the Boltzmann constant.  Having defined entropy, one can
+also define the temperature of the system using the Maxwell relation,
 \begin{equation}
-\frac{1}{T} = \biggl ( \frac{\partial S}{\partial E} \biggr )_{N,V}
+\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}}
+	\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}} )
+\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}
+Eq.~\ref{introEq:SM8} shows 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
+An application of these results is to formulate the 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 $E_{\gamma}$:
+partitioning of energy between the bath and the 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} )
+\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})
+	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
+where $\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma}$ denotes
+an integration over all accessible points in phase space, $P_{\gamma}$
+is the probability of being in a given phase state and
+$A(\boldsymbol{\Gamma})$ is an observable that is a function of the
 phase state.
 
 Because entropy seeks to maximize the number of degenerate states at a
@@ -138,30 +161,30 @@ P_{\gamma} \propto \Omega( E_{\text{bath}} ) = 
 states the coupled system is able to assume. Namely,
 \begin{equation}
 P_{\gamma} \propto \Omega( E_{\text{bath}} ) = 
-	e^{\ln \Omega( E - E_{\gamma})}
+	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:
+Because $E_{\gamma} \ll E$, $\ln \Omega$ 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
+	+ \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}}
+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
+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}}}
+{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}}.
 \label{introEq:SM14}
 \end{equation}
 This result is the standard Boltzmann statistical distribution.
@@ -170,65 +193,93 @@ Applying it to Eq.~\ref{introEq:SM10} one can obtain t
 \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}}}
+{\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.
+In the case of a Molecular Dynamics simulation, rather than
+calculating an ensemble average integral over phase space as in
+Eq.~\ref{introEq:SM15}, it becomes easier to caclulate the time
+average of an observable. Namely,
+\begin{equation}
+\langle A \rangle_t = \frac{1}{\tau} 
+	\int_0^{\tau} A[\boldsymbol{\Gamma}(t)]\,dt,
+\label{introEq:SM16}
+\end{equation}
+where the value of an observable is averaged over the length of time,
+$\tau$, that the simulation is run. This type of measurement mirrors
+the experimental measurement of an observable. In an experiment, the
+instrument analyzing the system must average its observation over the
+finite time of the measurement. What is required then, is a principle
+to relate the time average to the ensemble average. This is the
+ergodic hypothesis.
 
+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 energetically accessible 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
-solution.\cite{allen87:csl} The Monte Carlo method allows for the
-solution of integrals through the stochastic sampling of the values
-within the integral. In the simplest case, the evaluation of an
-integral would follow a brute force method of
-sampling.\cite{Frenkel1996} Consider the following single dimensional
-integral:
+named, because it uses random numbers extensively.\cite{allen87:csl}
+The Monte Carlo method allows for the solution of integrals through
+the stochastic sampling of the values within the integral. In the
+simplest case, the evaluation of an integral would follow a brute
+force method of sampling.\cite{Frenkel1996} Consider the following
+single dimensional integral:
 \begin{equation}
-I = f(x)dx
+I = \int_a^b f(x)dx.
 \label{eq:MCex1}
 \end{equation}
 The equation can be recast as:
 \begin{equation}
-I = (b-a)\langle f(x) \rangle
+I = \int^b_a \frac{f(x)}{\rho(x)} \rho(x) dx,
+\label{introEq:Importance1}
+\end{equation}
+where $\rho(x)$ is an arbitrary probability distribution in $x$.  If
+one conducts $\tau$ trials selecting a random number, $\zeta_\tau$,
+from the distribution $\rho(x)$ on the interval $[a,b]$, then
+Eq.~\ref{introEq:Importance1} becomes
+\begin{equation}
+I= \lim_{\tau \rightarrow \infty}\biggl \langle \frac{f(x)}{\rho(x)} \biggr \rangle_{\text{trials}[a,b]}.
+\label{introEq:Importance2}
+\end{equation}
+If $\rho(x)$ is uniformly distributed over the interval $[a,b]$,
+\begin{equation}
+\rho(x) = \frac{1}{b-a},
+\label{introEq:importance2b}
+\end{equation}
+then the integral can be rewritten as 
+\begin{equation}
+I = (b-a)\lim_{\tau \rightarrow \infty}
+	\langle f(x) \rangle_{\text{trials}[a,b]}.
 \label{eq:MCex2}
 \end{equation}
-Where $\langle f(x) \rangle$ is the unweighted average over the interval
-$[a,b]$. 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 infinitely
-large.
 
 However, in Statistical Mechanics, one is typically interested in
 integrals of the form:
 \begin{equation}
 \langle A \rangle = \frac{\int d^N \mathbf{r}~A(\mathbf{r}^N)% 
 	e^{-\beta V(\mathbf{r}^N)}}%
-	{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}}
+	{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}},
 \label{eq:mcEnsAvg}
 \end{equation}
-Where $\mathbf{r}^N$ stands for the coordinates of all $N$ particles
+where $\mathbf{r}^N$ stands for the coordinates of all $N$ particles
 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
@@ -240,52 +291,39 @@ Importance Sampling is a method where one selects a di
 average. This is where importance sampling comes into
 play.\cite{allen87:csl}
 
-Importance Sampling is a method where one selects a distribution from
-which the random configurations are chosen in order to more
-efficiently calculate the integral.\cite{Frenkel1996} Consider again
-Eq.~\ref{eq:MCex1} rewritten to be:
-\begin{equation}
-I = \int^b_a \frac{f(x)}{\rho(x)} \rho(x) dx
-\label{introEq:Importance1}
-\end{equation}
-Where $\rho(x)$ is an arbitrary probability distribution in $x$.  If
-one conducts $\tau$ trials selecting a random number, $\zeta_\tau$,
-from the distribution $\rho(x)$ on the interval $[a,b]$, then
-Eq.~\ref{introEq:Importance1} becomes
-\begin{equation}
-I= \biggl \langle \frac{f(x)}{\rho(x)} \biggr \rangle_{\text{trials}}
-\label{introEq:Importance2}
-\end{equation}
-Looking at Eq.~\ref{eq:mcEnsAvg}, and realizing
+Importance sampling is a method where the distribution, from which the
+random configurations are chosen, is selected in such a way as to
+efficiently sample the integral in question.  Looking at
+Eq.~\ref{eq:mcEnsAvg}, and realizing
 \begin {equation}
 \rho_{kT}(\mathbf{r}^N) = 
 	\frac{e^{-\beta V(\mathbf{r}^N)}}
-	{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}}
+	{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}},
 \label{introEq:MCboltzman}
 \end{equation}
-Where $\rho_{kT}$ is the Boltzmann 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) 
-	\rho_{kT}(\mathbf{r}^N)
+	\rho_{kT}(\mathbf{r}^N).
 \label{introEq:Importance3}
 \end{equation}
 Applying Eq.~\ref{introEq:Importance1} one obtains
 \begin{equation}
 \langle A \rangle = \biggl \langle
 	\frac{ A \rho_{kT}(\mathbf{r}^N) }
-	{\rho(\mathbf{r}^N)} \biggr \rangle_{\text{trials}}
+	{\rho(\mathbf{r}^N)} \biggr \rangle_{\text{trials}}.
 \label{introEq:Importance4}
 \end{equation}
 By selecting $\rho(\mathbf{r}^N)$ to be $\rho_{kT}(\mathbf{r}^N)$
 Eq.~\ref{introEq:Importance4} becomes
 \begin{equation}
-\langle A \rangle = \langle A(\mathbf{r}^N) \rangle_{\text{trials}}
+\langle A \rangle = \langle A(\mathbf{r}^N) \rangle_{kT}.
 \label{introEq:Importance5}
 \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
 $\rho_{kT}(\mathbf{r}^N)$.
 
@@ -307,35 +345,35 @@ The transition probability is given by the following:
 
 The transition probability is given by the following:
 \begin{equation}
-\pi_{mn} = \alpha_{mn} \times \accMe(m \rightarrow n)
+\pi_{mn} = \alpha_{mn} \times \accMe(m \rightarrow n),
 \label{introEq:MCpi}
 \end{equation}
-Where $\alpha_{mn}$ is the probability of attempting the move $m
+where $\alpha_{mn}$ is the probability of attempting the move $m
 \rightarrow n$, and $\accMe$ is the probability of accepting the move
 $m \rightarrow n$.  Defining a probability vector,
 $\boldsymbol{\rho}$, such that
 \begin{equation}
 \boldsymbol{\rho} = \{\rho_1, \rho_2, \ldots \rho_m, \rho_n, 
-	\ldots \rho_N \}
+	\ldots \rho_N \},
 \label{introEq:MCrhoVector}
 \end{equation}
 a transition matrix $\boldsymbol{\Pi}$ can be defined,
 whose elements are $\pi_{mn}$, for each given transition.  The
 limiting distribution of the Markov chain can then be found by
 applying the transition matrix an infinite number of times to the
-distribution vector.
+distribution vector,
 \begin{equation}
 \boldsymbol{\rho}_{\text{limit}} = 
 	\lim_{N \rightarrow \infty} \boldsymbol{\rho}_{\text{initial}}
-	\boldsymbol{\Pi}^N
+	\boldsymbol{\Pi}^N.
 \label{introEq:MCmarkovLimit}
 \end{equation}
 The limiting distribution of the chain is independent of the starting
 distribution, and successive applications of the transition matrix
-will only yield the limiting distribution again.
+will only yield the limiting distribution again,
 \begin{equation}
-\boldsymbol{\rho}_{\text{limit}} = \boldsymbol{\rho}_{\text{initial}}
-	\boldsymbol{\Pi}
+\boldsymbol{\rho}_{\text{limit}} = \boldsymbol{\rho}_{\text{limit}}
+	\boldsymbol{\Pi}.
 \label{introEq:MCmarkovEquil}
 \end{equation}
 
@@ -345,11 +383,11 @@ condition of microscopic reversibility on the equilibr
 Eq.~\ref{introEq:MCmarkovEquil} is solved such that
 $\boldsymbol{\rho}_{\text{limit}}$ 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
-from $m$ to $n$ is the same as going from $n$ to $m$.
+condition of detailed balance on the equilibrium
+distribution.  This means that the probability of going
+from $m$ to $n$ is the same as going from $n$ to $m$,
 \begin{equation}
-\rho_m\pi_{mn} = \rho_n\pi_{nm}
+\rho_m\pi_{mn} = \rho_n\pi_{nm}.
 \label{introEq:MCmicroReverse}
 \end{equation}
 Further, $\boldsymbol{\alpha}$ is chosen to be a symmetric matrix in
@@ -357,16 +395,16 @@ Eq.~\ref{introEq:MCmicroReverse} becomes
 Eq.~\ref{introEq:MCmicroReverse} becomes
 \begin{equation}
 \frac{\accMe(m \rightarrow n)}{\accMe(n \rightarrow m)} =
-	\frac{\rho_n}{\rho_m}
+	\frac{\rho_n}{\rho_m}.
 \label{introEq:MCmicro2}
 \end{equation}
 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}}
+	= e^{-\beta \Delta \mathcal{U}},
 \label{introEq:MCmicro3}
 \end{equation}
-This allows for the following set of acceptance rules be defined:
+where $\Delta\mathcal{U}$ is the change in the total energy of the system. This allows for the following set of acceptance rules be defined:
 \begin{equation}
 \accMe( m \rightarrow n ) = 
 	\begin{cases}
@@ -381,7 +419,7 @@ 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 If the new configuration lowers the energy of the system, accept the move ($\mathbf{r}^N$ 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}
@@ -394,29 +432,40 @@ Molecular Dynamics is when the equations of motion for
 \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
-integrated in order to obtain information about both the positions and
-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,
-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.
+Molecular Dynamics is the numerical integration of the equations of
+motion for a system in order to obtain information about the dynamic
+changes in the positions and momentum of the constituent particles.
+This allows the calculation of not only configurational observables,
+but momentum dependent ones as well: diffusion constants, relaxation
+events, folding/unfolding events, etc. With the time dependent
+information gained from a Molecular Dynamics simulation, one can also
+calculate time correlation functions of the form\cite{Hansen86}
+\begin{equation}
+\langle A(t)\,A(0)\rangle = \lim_{\tau\rightarrow\infty} \frac{1}{\tau}
+	\int_0^{\tau} A(t+t^{\prime})\,A(t^{\prime})\,dt^{\prime}.
+\label{introEq:timeCorr}
+\end{equation}
+These correlations can be used to measure fundamental time constants
+of a system, such as diffusion constants from the velocity
+autocorrelation or dipole relaxation times from the dipole
+autocorrelation.  Due to the principle of ergodicity,
+Sec.~\ref{introSec:ergodic}, the average of static observables over
+the length 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 observables depend on momenta in
-any fashion, then the only choice is molecular dynamics in some form.
+researcher is interested.  If the observables depend on time 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 efficient.
+then for most small systems, Monte Carlo techniques will be more
+efficient.  The focus of research in the second half of this
+dissertation is centered on the dynamic properties of phospholipid
+bilayers, making molecular dynamics key in the simulation of those
+properties.
 
-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.
+\subsection{\label{introSec:mdAlgorithm}Molecular Dynamics Algorithms}
 
-\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{introSec:mdInit} deals with the initialization
@@ -432,59 +481,59 @@ water, and in other cases structured the lipids into p
 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 performed
+water, and in other cases structured the lipids into preformed
 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 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.
+\item There are no overlapping molecules or atoms.
+\item Velocities are chosen in such a way as to give the system zero total momentum and angular momentum.
+\item It is also sometimes desirable to select the velocities to correctly sample the ensemble at a particular 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 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
+The first point is important due to the forces generated when two
+particles are too close together.  If overlap occurs, the first
+evaluation of forces will return numbers so large as to render the
+numerical integration of the equations 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 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
+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.
+that due to equipartition, 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 as potential energy.
 
 \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 $N(N-1)/ 2$
-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.
+of any molecular dynamics simulation.  This is due entirely to the
+evaluation of long range forces in a simulation, which are typically
+pair potentials.  These forces are most commonly the van der Waals
+force, and sometimes Coulombic forces as well.  For a pair-wise
+interaction, there are $N(N-1)/ 2$ pairs to be evaluated, where $N$ is
+the number of particles in the system.  This leads to calculations which
+scale 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
+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{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}
+exposed to the surface.  Unless one is simulating an isolated cluster
+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 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
 particle leaving the simulation box on one side will have an image of
-itself enter on the opposite side (see Fig.~\ref{introFig:pbc}).  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{oopseSec:pbc}.
+itself enter on the opposite side (see
+Fig.~\ref{introFig:pbc}). Therefore, a pair of particles have an
+image of each other, real or periodic, within $\text{box}/2$.  A
+discussion of the method used to calculate the periodic image can be
+found in Sec.\ref{oopseSec:pbc}.
 
 \begin{figure}
 \centering
@@ -493,27 +542,44 @@ Returning to the topic of the computational scale of t
 \label{introFig:pbc}
 \end{figure}
 
-Returning to the topic of the computational scale of the force
+Returning to the topic of the computational scaling 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, $r_{\text{cut}}$, 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 $\text{box} / 2$.
+there are two methods to choose from, both with their own cutoff
+limits. In the minimum image convention, $r_{\text{cut}}$ has a
+maximum value of $\text{box}/2$. This is because each atom has only
+one image that is seen by other atoms. The image used is the one that
+minimizes the distance between the two atoms. A system of wrapped
+images about a central atom therefore has a maximum length scale of
+box on a side (Fig.~\ref{introFig:rMaxMin}). The second convention,
+multiple image convention, has a maximum $r_{\text{cut}}$ of the box
+length itself. Here multiple images of each atom are replicated in the
+periodic cells surrounding the central atom, this causes the atom to
+see multiple copies of several atoms. If the cutoff radius is larger
+than box, however, then the atom will see an image of itself, and
+attempt to calculate an unphysical self-self force interaction
+(Fig.~\ref{introFig:rMaxMult}). Due to the increased complexity and
+commputaional ineffeciency of the multiple image method, the minimum
+image method is the used throughout this research.
 
 \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}
+\caption[An explanation of minimum image convention]{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:rMaxMin}
 \end{figure}
 
-With the use of an $r_{\text{cut}}$, however, comes a discontinuity in
+\begin{figure}
+\centering
+\includegraphics[width=\linewidth]{rCutMaxMultFig.eps}
+\caption[An explanation of multiple image convention]{The yellow atom is the central wrapping point. The blue atoms are the minimum images of the system about the central atom. The boxes with the green atoms are multiple images of the central box. If $r_{\text{cut}} \geq \text{box}$ then the central atom sees multiple images of itself (red atom), creating a self-self force evaluation.}
+\label{introFig:rMaxMult}
+\end{figure}
+
+With the use of a cutoff radius, 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
@@ -529,71 +595,74 @@ neighbor list. \cite{allen87:csl} In the Verlet method
 \end{figure}
 
 The second main simplification used in this research is the Verlet
-neighbor list. \cite{allen87:csl} In the Verlet method, one generates
+neighbor list.\cite{allen87:csl} In the Verlet method, one generates
 a list of all neighbor atoms, $j$, surrounding atom $i$ within some
 cutoff $r_{\text{list}}$, where $r_{\text{list}}>r_{\text{cut}}$.
 This list is created the first time forces are evaluated, then on
 subsequent force evaluations, pair calculations are only calculated
-from the neighbor lists.  The lists are updated if any given particle
+from the neighbor lists.  The lists are updated if any particle
 in the system moves farther than $r_{\text{list}}-r_{\text{cut}}$,
-giving rise to the possibility that a particle has left or joined a
+which indicates the possibility that a particle has left or joined the
 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
 expansion of position in time:
 \begin{equation}
 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) 
+	\frac{\Delta t^3}{3!}\frac{\partial^3 q(t)}{\partial t^3} +
+	\mathcal{O}(\Delta t^4) .
 \label{introEq:verletForward}
 \end{equation}
 As well as,
 \begin{equation}
 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) 
+	\frac{\Delta t^3}{3!}\frac{\partial^3 q(t)}{\partial t^3} +
+	\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,
+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 the Verlet method.\cite{allen87:csl}
-\begin{equation}
-eq here
-\label{introEq:MDvelVerletPos}
-\end{equation}
-\begin{equation}
-eq here
+with the 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 with the finite integration scheme.  An exact
-solution to the integration will conserve area in phase space, as well
+very little long time drift in energy.  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 with
+the finite integration scheme.  An exact solution to the integration
+will conserve area in phase space (i.e.~will be symplectic), as well
 as be reversible in time, that is, the trajectory integrated forward
 or backwards will exactly match itself.  Having a finite algorithm
 that both conserves area in phase space and is time reversible,
-therefore increases, but does not guarantee the ``correctness'' or the
-integrated trajectory.
+therefore increases the reliability, but does not guarantee the
+``correctness'' of the integrated trajectory.
 
 It can be shown,\cite{Frenkel1996} that although the Verlet algorithm
 does not rigorously preserve the actual Hamiltonian, it does preserve
@@ -602,120 +671,180 @@ hypothesis (Sec.~\ref{introSec:StatThermo}), it is kno
 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: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 Ch.~\ref{chaptLipids} are
+parameters are needed to describe the motions.  In the simulations
+involving water and phospholipids in Ch.~\ref{chapt:lipid}, we 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 $3 \times 3$ matrix $\mathbf{A}$
+Fig.~\ref{introFig:eulerAngles}).  This sequence of rotations can be
+accumulated into a single $3 \times 3$ matrix, $\mathsf{A}$,
 defined as follows:
 \begin{equation}
-eq here
+\mathsf{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}
-Where $\omega^s_i$ is the angular velocity in the lab space frame
-along Cartesian coordinate $i$.  However, a difficulty arises when
+\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 $x$ axis (green rotation). Lastly is a final rotation of $\psi$ about the new $z$ 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 $\omega^s_{\alpha}$ is the angular velocity in the lab space frame
+along Cartesian coordinate $\alpha$.  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,
-$\mathbf{A}$, 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, $\mathsf{A}$, 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
+scheme.  It has been previously discussed
+(Sec.~\ref{introSec:mdIntegrate}) how it is desirable for an
+integrator to be symplectic and time reversible.  The following is an
 outline of the Trotter factorization of the Liouville Propagator as a
-scheme for generating symplectic integrators. \cite{Tuckerman:1992}
+scheme for generating symplectic, time-reversible
+integrators.\cite{Tuckerman92}
 
 For a system with $f$ degrees of freedom the Liouville operator can be
 defined as,
 \begin{equation}
-eq here
+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, $r_j$ 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.
+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,
-$\{r_j,p_j\}$, and the propagator, $U(t)$, is defined
+$\{q_j,p_j\}$, and the propagator, $U(t)$, is defined
 \begin {equation}
-eq here
+U(t) = e^{iLt}.
 \label{introEq:Lpropagator}
 \end{equation}
 This allows the specification of $\Gamma$ at any time $t$ as 
 \begin{equation}
-eq here
+\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)
+U(-t)=U^{-1}(t).
 \label{introEq:Lp3}
 \end{equation}
 
 Decomposing $L$ into two parts, $iL_1$ and $iL_2$, one can use the
 Trotter theorem to yield
-\begin{equation}
-eq here
-\label{introEq:Lp4}
-\end{equation}
-Where $\Delta t = \frac{t}{P}$.
+\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{equation}
-eq here
+\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{equation}
-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
+\end{align}
+Because $U_1(t)$ and $U_2(t)$ are unitary, $G(\Delta t)$ is also
+unitary.  This means that an integrator based on this factorization will be
 reversible in time.
 
 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}
-eq here
-\label{introEq:Lp6}
+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
+Operating $G(\Delta t)$ on $\Gamma(0)$, and utilizing the operator property
 \begin{equation}
-eq here
+e^{c\frac{\partial}{\partial x}}\, f(x) = f(x+c),
 \label{introEq:Lp8}
 \end{equation}
-Where $c$ is independent of $q$.  One obtains the following:  
-\begin{equation}
-eq here
-\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{equation}
-eq here
-\label{intorEq:Lp9}
-\end{equation}
+\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
+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:
@@ -729,62 +858,78 @@ see that the integration of the equations of motion wo
 \item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values.
 \end{enumerate}
 
-\subsubsection{\label{introSec:MDsymplecticRot} Symplectic Propagation of the Rotation Matrix}
+\subsection{\label{introSec:MDsymplecticRot} Symplectic Propagation of the Rotation Matrix}
 
 Based on the factorization from the previous section,
-Dullweber\emph{et al.}\cite{Dullweber:1997}~ proposed a scheme for the
-symplectic propagation of the rotation matrix, $\mathbf{A}$, as an
+Dullweber\emph{et al}.\cite{Dullweber1997}~ proposed a scheme for the
+symplectic propagation of the rotation matrix, $\mathsf{A}$, as an
 alternative method for the integration of orientational degrees of
 freedom. The method starts with a straightforward splitting of the
 Liouville operator:
-\begin{equation}
-eq here
-\label{introEq:SR1}
-\end{equation}
-Where $\boldsymbol{\tau}(\mathbf{A})$ are the torques of the system
-due to the configuration, and $\boldsymbol{/pi}$ are the conjugate
+\begin{align}
+iL_{\text{pos}} &= \dot{q}\frac{\partial}{\partial q} +
+	\mathsf{\dot{A}}\frac{\partial}{\partial \mathsf{A}} ,
+\label{introEq:SR1a} \\%
+%
+iL_F &= F(q)\frac{\partial}{\partial p},
+\label{introEq:SR1b} \\%
+iL_{\tau} &= \tau(\mathsf{A})\frac{\partial}{\partial j},
+\label{introEq:SR1b} \\%
+\end{align}
+where $\tau(\mathsf{A})$ is the torque of the system
+due to the configuration, and $j$ is the conjugate
 angular momenta of the system. The propagator, $G(\Delta t)$, becomes
 \begin{equation}
-eq here
+G(\Delta t) = e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}} \,
+	e^{\frac{\Delta t}{2} \tau(\mathsf{A})\frac{\partial}{\partial j}} \,
+	e^{\Delta t\,iL_{\text{pos}}} \,
+	e^{\frac{\Delta t}{2} \tau(\mathsf{A})\frac{\partial}{\partial j}} \,
+	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, $\mathcal{G}_{\text{rot}}(\Delta t)$.
+matrix propagation, $\mathcal{U}_{\text{rot}}(\Delta t)$, within
+$U_{\text{pos}}(\Delta t)$.
 \begin{equation}
-eq here
+\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 $\mathcal{G}_j$ is a unitary rotation of $\mathbf{A}$ and
-$\boldsymbol{\pi}$ about each axis $j$.  As all propagations are now
+where $\mathcal{U}_{\alpha}$ is a unitary rotation of $\mathsf{A}$ and
+$j$ about each axis $\alpha$.  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:Chapt.~\ref{chapt:RSA}
+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,
+pthalocyanines on a gold (111) surface. Ch.~\ref{chapt:oopse}
+is about our 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 the principles of theoretical
-research 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 partial
-coverage of a gold surface by a particular compound of hers. We
+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 experimental observations.  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 synthesized in her group. 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
+on a plane, and set about simulating 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
+overlapping with 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
@@ -792,28 +937,27 @@ written in order to study phospholipid bilayer.  Altho
 
 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 bilayer.  Although there are
+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.
+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.  It also has the
+ability to perform calculations across distributed parallel processors
+and contains a flexible script syntax for creating systems.  {\sc
+oopse} has become a very powerful scientific instrument for the
+exploration of our model.
 
-Bringing us to Ch.~\ref{chapt:lipid}. Using {\sc oopse}, I have been
+In Ch.~\ref{chapt:lipid}, utilizing {\sc oopse}, I have been
 able to parameterize a mesoscale model for phospholipid simulations.
-This model retains information about solvent ordering about the
+This model retains information about solvent ordering around the
 bilayer, as well as information regarding the interaction of the
-phospholipid head groups' dipole with each other and the surrounding
+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.
-
-
+In the last chapter, I discuss future directions
+for both {\sc oopse} and this mesoscale model.  Additionally, I will
+give a summary of the results found in this dissertation.