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\chapter{\label{chapt:intro}Introduction and Theoretical Background} |
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\section{\label{introSec:theory}Theoretical Background} |
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The techniques used in the course of this research fall under the two |
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main classes of molecular simulation: Molecular Dynamics and Monte |
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Carlo. Molecular Dynamic simulations integrate the equations of motion |
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for a given system of particles, allowing the researher to gain |
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for a given system of particles, allowing the researcher to gain |
| 10 |
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insight into the time dependent evolution of a system. Diffusion |
| 11 |
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phenomena are readily studied with this simulation technique, making |
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Molecular Dynamics the main simulation technique used in this |
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research. Other aspects of the research fall under the Monte Carlo |
| 14 |
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class of simulations. In Monte Carlo, the configuration space |
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available to the collection of particles is sampled stochastichally, |
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available to the collection of particles is sampled stochastically, |
| 16 |
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or randomly. Each configuration is chosen with a given probability |
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based on the Maxwell Boltzman distribution. These types of simulations |
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based on the Maxwell Boltzmann distribution. These types of simulations |
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are best used to probe properties of a system that are only dependent |
| 19 |
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only on the state of the system. Structural information about a system |
| 20 |
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is most readily obtained through these types of methods. |
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thermodynamic properties of the system are being probed, then chose |
| 28 |
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which method best suits that objective. |
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|
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\subsection{\label{introSec:statThermo}Statistical Thermodynamics} |
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\section{\label{introSec:statThermo}Statistical Mechanics} |
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|
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ergodic hypothesis |
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The following section serves as a brief introduction to some of the |
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Statistical Mechanics concepts present in this dissertation. What |
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follows is a brief derivation of Boltzmann weighted statistics, and an |
| 35 |
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explanation of how one can use the information to calculate an |
| 36 |
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observable for a system. This section then concludes with a brief |
| 37 |
> |
discussion of the ergodic hypothesis and its relevance to this |
| 38 |
> |
research. |
| 39 |
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|
| 40 |
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enesemble averages |
| 40 |
> |
\subsection{\label{introSec:boltzman}Boltzmann weighted statistics} |
| 41 |
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|
| 42 |
< |
\subsection{\label{introSec:monteCarlo}Monte Carlo Simulations} |
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> |
Consider a system, $\gamma$, with some total energy,, $E_{\gamma}$. |
| 43 |
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Let $\Omega(E_{\gamma})$ represent the number of degenerate ways |
| 44 |
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$\boldsymbol{\Gamma}$, the collection of positions and conjugate |
| 45 |
> |
momenta of system $\gamma$, can be configured to give |
| 46 |
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$E_{\gamma}$. Further, if $\gamma$ is in contact with a bath system |
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where energy is exchanged between the two systems, $\Omega(E)$, where |
| 48 |
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$E$ is the total energy of both systems, can be represented as |
| 49 |
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\begin{equation} |
| 50 |
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\Omega(E) = \Omega(E_{\gamma}) \times \Omega(E - E_{\gamma}) |
| 51 |
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\label{introEq:SM1} |
| 52 |
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\end{equation} |
| 53 |
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Or additively as |
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\begin{equation} |
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\ln \Omega(E) = \ln \Omega(E_{\gamma}) + \ln \Omega(E - E_{\gamma}) |
| 56 |
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\label{introEq:SM2} |
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\end{equation} |
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|
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The solution to Eq.~\ref{introEq:SM2} maximizes the number of |
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> |
degenerative configurations in $E$. \cite{Frenkel1996} |
| 61 |
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This gives |
| 62 |
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\begin{equation} |
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\frac{\partial \ln \Omega(E)}{\partial E_{\gamma}} = 0 = |
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\frac{\partial \ln \Omega(E_{\gamma})}{\partial E_{\gamma}} |
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+ |
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\frac{\partial \ln \Omega(E_{\text{bath}})}{\partial E_{\text{bath}}} |
| 67 |
> |
\frac{\partial E_{\text{bath}}}{\partial E_{\gamma}} |
| 68 |
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\label{introEq:SM3} |
| 69 |
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\end{equation} |
| 70 |
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Where $E_{\text{bath}}$ is $E-E_{\gamma}$, and |
| 71 |
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$\frac{\partial E_{\text{bath}}}{\partial E_{\gamma}}$ is |
| 72 |
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$-1$. Eq.~\ref{introEq:SM3} becomes |
| 73 |
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\begin{equation} |
| 74 |
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\frac{\partial \ln \Omega(E_{\gamma})}{\partial E_{\gamma}} = |
| 75 |
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\frac{\partial \ln \Omega(E_{\text{bath}})}{\partial E_{\text{bath}}} |
| 76 |
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\label{introEq:SM4} |
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\end{equation} |
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|
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At this point, one can draw a relationship between the maximization of |
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degeneracy in Eq.~\ref{introEq:SM3} and the second law of |
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thermodynamics. Namely, that for a closed system, entropy will |
| 82 |
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increase for an irreversible process.\cite{chandler:1987} Here the |
| 83 |
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process is the partitioning of energy among the two systems. This |
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allows the following definition of entropy: |
| 85 |
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\begin{equation} |
| 86 |
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S = k_B \ln \Omega(E) |
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\label{introEq:SM5} |
| 88 |
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\end{equation} |
| 89 |
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Where $k_B$ is the Boltzmann constant. Having defined entropy, one can |
| 90 |
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also define the temperature of the system using the relation |
| 91 |
> |
\begin{equation} |
| 92 |
> |
\frac{1}{T} = \biggl ( \frac{\partial S}{\partial E} \biggr )_{N,V} |
| 93 |
> |
\label{introEq:SM6} |
| 94 |
> |
\end{equation} |
| 95 |
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The temperature in the system $\gamma$ is then |
| 96 |
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\begin{equation} |
| 97 |
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\beta( E_{\gamma} ) = \frac{1}{k_B T} = |
| 98 |
> |
\frac{\partial \ln \Omega(E_{\gamma})}{\partial E_{\gamma}} |
| 99 |
> |
\label{introEq:SM7} |
| 100 |
> |
\end{equation} |
| 101 |
> |
Applying this to Eq.~\ref{introEq:SM4} gives the following |
| 102 |
> |
\begin{equation} |
| 103 |
> |
\beta( E_{\gamma} ) = \beta( E_{\text{bath}} ) |
| 104 |
> |
\label{introEq:SM8} |
| 105 |
> |
\end{equation} |
| 106 |
> |
Showing that the partitioning of energy between the two systems is |
| 107 |
> |
actually a process of thermal equilibration.\cite{Frenkel1996} |
| 108 |
> |
|
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An application of these results is to formulate the form of an |
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expectation value of an observable, $A$, in the canonical ensemble. In |
| 111 |
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the canonical ensemble, the number of particles, $N$, the volume, $V$, |
| 112 |
> |
and the temperature, $T$, are all held constant while the energy, $E$, |
| 113 |
> |
is allowed to fluctuate. Returning to the previous example, the bath |
| 114 |
> |
system is now an infinitely large thermal bath, whose exchange of |
| 115 |
> |
energy with the system $\gamma$ holds the temperature constant. The |
| 116 |
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partitioning of energy in the bath system is then related to the total |
| 117 |
> |
energy of both systems and the fluctuations in $E_{\gamma}$: |
| 118 |
> |
\begin{equation} |
| 119 |
> |
\Omega( E_{\gamma} ) = \Omega( E - E_{\gamma} ) |
| 120 |
> |
\label{introEq:SM9} |
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> |
\end{equation} |
| 122 |
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As for the expectation value, it can be defined |
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\begin{equation} |
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\langle A \rangle = |
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\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} |
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P_{\gamma} A(\boldsymbol{\Gamma}) |
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> |
\label{introEq:SM10} |
| 128 |
> |
\end{equation} |
| 129 |
> |
Where $\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma}$ denotes |
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an integration over all accessible phase space, $P_{\gamma}$ is the |
| 131 |
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probability of being in a given phase state and |
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$A(\boldsymbol{\Gamma})$ is some observable that is a function of the |
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phase state. |
| 134 |
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|
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> |
Because entropy seeks to maximize the number of degenerate states at a |
| 136 |
> |
given energy, the probability of being in a particular state in |
| 137 |
> |
$\gamma$ will be directly proportional to the number of allowable |
| 138 |
> |
states the coupled system is able to assume. Namely, |
| 139 |
> |
\begin{equation} |
| 140 |
> |
P_{\gamma} \propto \Omega( E_{\text{bath}} ) = |
| 141 |
> |
e^{\ln \Omega( E - E_{\gamma})} |
| 142 |
> |
\label{introEq:SM11} |
| 143 |
> |
\end{equation} |
| 144 |
> |
With $E_{\gamma} \ll E$, $\ln \Omega$ can be expanded in a Taylor series: |
| 145 |
> |
\begin{equation} |
| 146 |
> |
\ln \Omega ( E - E_{\gamma}) = |
| 147 |
> |
\ln \Omega (E) - |
| 148 |
> |
E_{\gamma} \frac{\partial \ln \Omega }{\partial E} |
| 149 |
> |
+ \ldots |
| 150 |
> |
\label{introEq:SM12} |
| 151 |
> |
\end{equation} |
| 152 |
> |
Higher order terms are omitted as $E$ is an infinite thermal |
| 153 |
> |
bath. Further, using Eq.~\ref{introEq:SM7}, Eq.~\ref{introEq:SM11} can |
| 154 |
> |
be rewritten: |
| 155 |
> |
\begin{equation} |
| 156 |
> |
P_{\gamma} \propto e^{-\beta E_{\gamma}} |
| 157 |
> |
\label{introEq:SM13} |
| 158 |
> |
\end{equation} |
| 159 |
> |
Where $\ln \Omega(E)$ has been factored out of the proportionality as a |
| 160 |
> |
constant. Normalizing the probability ($\int\limits_{\boldsymbol{\Gamma}} |
| 161 |
> |
d\boldsymbol{\Gamma} P_{\gamma} = 1$) gives |
| 162 |
> |
\begin{equation} |
| 163 |
> |
P_{\gamma} = \frac{e^{-\beta E_{\gamma}}} |
| 164 |
> |
{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}} |
| 165 |
> |
\label{introEq:SM14} |
| 166 |
> |
\end{equation} |
| 167 |
> |
This result is the standard Boltzmann statistical distribution. |
| 168 |
> |
Applying it to Eq.~\ref{introEq:SM10} one can obtain the following relation for ensemble averages: |
| 169 |
> |
\begin{equation} |
| 170 |
> |
\langle A \rangle = |
| 171 |
> |
\frac{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} |
| 172 |
> |
A( \boldsymbol{\Gamma} ) e^{-\beta E_{\gamma}}} |
| 173 |
> |
{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}} |
| 174 |
> |
\label{introEq:SM15} |
| 175 |
> |
\end{equation} |
| 176 |
|
|
| 177 |
+ |
\subsection{\label{introSec:ergodic}The Ergodic Hypothesis} |
| 178 |
+ |
|
| 179 |
+ |
One last important consideration is that of ergodicity. Ergodicity is |
| 180 |
+ |
the assumption that given an infinite amount of time, a system will |
| 181 |
+ |
visit every available point in phase space.\cite{Frenkel1996} For most |
| 182 |
+ |
systems, this is a valid assumption, except in cases where the system |
| 183 |
+ |
may be trapped in a local feature (\emph{e.g.}~glasses). When valid, |
| 184 |
+ |
ergodicity allows the unification of a time averaged observation and |
| 185 |
+ |
an ensemble averaged one. If an observation is averaged over a |
| 186 |
+ |
sufficiently long time, the system is assumed to visit all |
| 187 |
+ |
appropriately available points in phase space, giving a properly |
| 188 |
+ |
weighted statistical average. This allows the researcher freedom of |
| 189 |
+ |
choice when deciding how best to measure a given observable. When an |
| 190 |
+ |
ensemble averaged approach seems most logical, the Monte Carlo |
| 191 |
+ |
techniques described in Sec.~\ref{introSec:monteCarlo} can be utilized. |
| 192 |
+ |
Conversely, if a problem lends itself to a time averaging approach, |
| 193 |
+ |
the Molecular Dynamics techniques in Sec.~\ref{introSec:MD} can be |
| 194 |
+ |
employed. |
| 195 |
+ |
|
| 196 |
+ |
\section{\label{introSec:monteCarlo}Monte Carlo Simulations} |
| 197 |
+ |
|
| 198 |
|
The Monte Carlo method was developed by Metropolis and Ulam for their |
| 199 |
|
work in fissionable material.\cite{metropolis:1949} The method is so |
| 200 |
|
named, because it heavily uses random numbers in its |
| 217 |
|
$[a,b]$. The calculation of the integral could then be solved by |
| 218 |
|
randomly choosing points along the interval $[a,b]$ and calculating |
| 219 |
|
the value of $f(x)$ at each point. The accumulated average would then |
| 220 |
< |
approach $I$ in the limit where the number of trials is infintely |
| 220 |
> |
approach $I$ in the limit where the number of trials is infinitely |
| 221 |
|
large. |
| 222 |
|
|
| 223 |
|
However, in Statistical Mechanics, one is typically interested in |
| 229 |
|
\label{eq:mcEnsAvg} |
| 230 |
|
\end{equation} |
| 231 |
|
Where $\mathbf{r}^N$ stands for the coordinates of all $N$ particles |
| 232 |
< |
and $A$ is some observable that is only dependent on |
| 233 |
< |
position. $\langle A \rangle$ is the ensemble average of $A$ as |
| 234 |
< |
presented in Sec.~\ref{introSec:statThermo}. Because $A$ is |
| 235 |
< |
independent of momentum, the momenta contribution of the integral can |
| 236 |
< |
be factored out, leaving the configurational integral. Application of |
| 237 |
< |
the brute force method to this system would yield highly inefficient |
| 238 |
< |
results. Due to the Boltzman weighting of this integral, most random |
| 232 |
> |
and $A$ is some observable that is only dependent on position. This is |
| 233 |
> |
the ensemble average of $A$ as presented in |
| 234 |
> |
Sec.~\ref{introSec:statThermo}, except here $A$ is independent of |
| 235 |
> |
momentum. Therefore the momenta contribution of the integral can be |
| 236 |
> |
factored out, leaving the configurational integral. Application of the |
| 237 |
> |
brute force method to this system would yield highly inefficient |
| 238 |
> |
results. Due to the Boltzmann weighting of this integral, most random |
| 239 |
|
configurations will have a near zero contribution to the ensemble |
| 240 |
< |
average. This is where a importance sampling comes into |
| 240 |
> |
average. This is where importance sampling comes into |
| 241 |
|
play.\cite{allen87:csl} |
| 242 |
|
|
| 243 |
|
Importance Sampling is a method where one selects a distribution from |
| 263 |
|
{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}} |
| 264 |
|
\label{introEq:MCboltzman} |
| 265 |
|
\end{equation} |
| 266 |
< |
Where $\rho_{kT}$ is the boltzman distribution. The ensemble average |
| 266 |
> |
Where $\rho_{kT}$ is the Boltzmann distribution. The ensemble average |
| 267 |
|
can be rewritten as |
| 268 |
|
\begin{equation} |
| 269 |
|
\langle A \rangle = \int d^N \mathbf{r}~A(\mathbf{r}^N) |
| 285 |
|
\end{equation} |
| 286 |
|
The difficulty is selecting points $\mathbf{r}^N$ such that they are |
| 287 |
|
sampled from the distribution $\rho_{kT}(\mathbf{r}^N)$. A solution |
| 288 |
< |
was proposed by Metropolis et al.\cite{metropolis:1953} which involved |
| 288 |
> |
was proposed by Metropolis \emph{et al}.\cite{metropolis:1953} which involved |
| 289 |
|
the use of a Markov chain whose limiting distribution was |
| 290 |
|
$\rho_{kT}(\mathbf{r}^N)$. |
| 291 |
|
|
| 292 |
< |
\subsubsection{\label{introSec:markovChains}Markov Chains} |
| 292 |
> |
\subsection{\label{introSec:markovChains}Markov Chains} |
| 293 |
|
|
| 294 |
|
A Markov chain is a chain of states satisfying the following |
| 295 |
|
conditions:\cite{leach01:mm} |
| 297 |
|
\item The outcome of each trial depends only on the outcome of the previous trial. |
| 298 |
|
\item Each trial belongs to a finite set of outcomes called the state space. |
| 299 |
|
\end{enumerate} |
| 300 |
< |
If given two configuartions, $\mathbf{r}^N_m$ and $\mathbf{r}^N_n$, |
| 301 |
< |
$\rho_m$ and $\rho_n$ are the probablilities of being in state |
| 300 |
> |
If given two configurations, $\mathbf{r}^N_m$ and $\mathbf{r}^N_n$, |
| 301 |
> |
$\rho_m$ and $\rho_n$ are the probabilities of being in state |
| 302 |
|
$\mathbf{r}^N_m$ and $\mathbf{r}^N_n$ respectively. Further, the two |
| 303 |
|
states are linked by a transition probability, $\pi_{mn}$, which is the |
| 304 |
|
probability of going from state $m$ to state $n$. |
| 339 |
|
\label{introEq:MCmarkovEquil} |
| 340 |
|
\end{equation} |
| 341 |
|
|
| 342 |
< |
\subsubsection{\label{introSec:metropolisMethod}The Metropolis Method} |
| 342 |
> |
\subsection{\label{introSec:metropolisMethod}The Metropolis Method} |
| 343 |
|
|
| 344 |
|
In the Metropolis method\cite{metropolis:1953} |
| 345 |
|
Eq.~\ref{introEq:MCmarkovEquil} is solved such that |
| 346 |
< |
$\boldsymbol{\rho}_{\text{limit}}$ matches the Boltzman distribution |
| 346 |
> |
$\boldsymbol{\rho}_{\text{limit}}$ matches the Boltzmann distribution |
| 347 |
|
of states. The method accomplishes this by imposing the strong |
| 348 |
|
condition of microscopic reversibility on the equilibrium |
| 349 |
|
distribution. Meaning, that at equilibrium the probability of going |
| 352 |
|
\rho_m\pi_{mn} = \rho_n\pi_{nm} |
| 353 |
|
\label{introEq:MCmicroReverse} |
| 354 |
|
\end{equation} |
| 355 |
< |
Further, $\boldsymbol{\alpha}$ is chosen to be a symetric matrix in |
| 355 |
> |
Further, $\boldsymbol{\alpha}$ is chosen to be a symmetric matrix in |
| 356 |
|
the Metropolis method. Using Eq.~\ref{introEq:MCpi}, |
| 357 |
|
Eq.~\ref{introEq:MCmicroReverse} becomes |
| 358 |
|
\begin{equation} |
| 360 |
|
\frac{\rho_n}{\rho_m} |
| 361 |
|
\label{introEq:MCmicro2} |
| 362 |
|
\end{equation} |
| 363 |
< |
For a Boltxman limiting distribution, |
| 363 |
> |
For a Boltzmann limiting distribution, |
| 364 |
|
\begin{equation} |
| 365 |
|
\frac{\rho_n}{\rho_m} = e^{-\beta[\mathcal{U}(n) - \mathcal{U}(m)]} |
| 366 |
|
= e^{-\beta \Delta \mathcal{U}} |
| 368 |
|
\end{equation} |
| 369 |
|
This allows for the following set of acceptance rules be defined: |
| 370 |
|
\begin{equation} |
| 371 |
< |
EQ Here |
| 371 |
> |
\accMe( m \rightarrow n ) = |
| 372 |
> |
\begin{cases} |
| 373 |
> |
1& \text{if $\Delta \mathcal{U} \leq 0$,} \\ |
| 374 |
> |
e^{-\beta \Delta \mathcal{U}}& \text{if $\Delta \mathcal{U} > 0$.} |
| 375 |
> |
\end{cases} |
| 376 |
> |
\label{introEq:accRules} |
| 377 |
|
\end{equation} |
| 378 |
|
|
| 379 |
< |
Using the acceptance criteria from Eq.~\ref{fix} the Metropolis method |
| 380 |
< |
proceeds as follows |
| 381 |
< |
\begin{itemize} |
| 382 |
< |
\item Generate an initial configuration $fix$ which has some finite probability in $fix$. |
| 383 |
< |
\item Modify $fix$, to generate configuratioon $fix$. |
| 384 |
< |
\item If configuration $n$ lowers the energy of the system, accept the move with unity ($fix$ becomes $fix$). Otherwise accept with probability $fix$. |
| 385 |
< |
\item Accumulate the average for the configurational observable of intereest. |
| 386 |
< |
\item Repeat from step 2 until average converges. |
| 387 |
< |
\end{itemize} |
| 379 |
> |
Using the acceptance criteria from Eq.~\ref{introEq:accRules} the |
| 380 |
> |
Metropolis method proceeds as follows |
| 381 |
> |
\begin{enumerate} |
| 382 |
> |
\item Generate an initial configuration $\mathbf{r}^N$ which has some finite probability in $\rho_{kT}$. |
| 383 |
> |
\item Modify $\mathbf{r}^N$, to generate configuration $\mathbf{r^{\prime}}^N$. |
| 384 |
> |
\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 $e^{-\beta \Delta \mathcal{U}}$. |
| 385 |
> |
\item Accumulate the average for the configurational observable of interest. |
| 386 |
> |
\item Repeat from step 2 until the average converges. |
| 387 |
> |
\end{enumerate} |
| 388 |
|
One important note is that the average is accumulated whether the move |
| 389 |
|
is accepted or not, this ensures proper weighting of the average. |
| 390 |
< |
Using Eq.~\ref{fix} it becomes clear that the accumulated averages are |
| 391 |
< |
the ensemble averages, as this method ensures that the limiting |
| 392 |
< |
distribution is the Boltzman distribution. |
| 390 |
> |
Using Eq.~\ref{introEq:Importance4} it becomes clear that the |
| 391 |
> |
accumulated averages are the ensemble averages, as this method ensures |
| 392 |
> |
that the limiting distribution is the Boltzmann distribution. |
| 393 |
|
|
| 394 |
< |
\subsection{\label{introSec:MD}Molecular Dynamics Simulations} |
| 394 |
> |
\section{\label{introSec:MD}Molecular Dynamics Simulations} |
| 395 |
|
|
| 396 |
|
The main simulation tool used in this research is Molecular Dynamics. |
| 397 |
|
Molecular Dynamics is when the equations of motion for a system are |
| 399 |
|
momentum of a system, allowing the calculation of not only |
| 400 |
|
configurational observables, but momenta dependent ones as well: |
| 401 |
|
diffusion constants, velocity auto correlations, folding/unfolding |
| 402 |
< |
events, etc. Due to the principle of ergodicity, Eq.~\ref{fix}, the |
| 403 |
< |
average of these observables over the time period of the simulation |
| 404 |
< |
are taken to be the ensemble averages for the system. |
| 402 |
> |
events, etc. Due to the principle of ergodicity, |
| 403 |
> |
Sec.~\ref{introSec:ergodic}, the average of these observables over the |
| 404 |
> |
time period of the simulation are taken to be the ensemble averages |
| 405 |
> |
for the system. |
| 406 |
|
|
| 407 |
|
The choice of when to use molecular dynamics over Monte Carlo |
| 408 |
|
techniques, is normally decided by the observables in which the |
| 409 |
< |
researcher is interested. If the observabvles depend on momenta in |
| 409 |
> |
researcher is interested. If the observables depend on momenta in |
| 410 |
|
any fashion, then the only choice is molecular dynamics in some form. |
| 411 |
|
However, when the observable is dependent only on the configuration, |
| 412 |
< |
then most of the time Monte Carlo techniques will be more efficent. |
| 412 |
> |
then most of the time Monte Carlo techniques will be more efficient. |
| 413 |
|
|
| 414 |
|
The focus of research in the second half of this dissertation is |
| 415 |
|
centered around the dynamic properties of phospholipid bilayers, |
| 416 |
|
making molecular dynamics key in the simulation of those properties. |
| 417 |
|
|
| 418 |
< |
\subsubsection{Molecular dynamics Algorithm} |
| 418 |
> |
\subsection{\label{introSec:mdAlgorithm}The Molecular Dynamics Algorithm} |
| 419 |
|
|
| 420 |
|
To illustrate how the molecular dynamics technique is applied, the |
| 421 |
|
following sections will describe the sequence involved in a |
| 422 |
< |
simulation. Sec.~\ref{fix} deals with the initialization of a |
| 423 |
< |
simulation. Sec.~\ref{fix} discusses issues involved with the |
| 424 |
< |
calculation of the forces. Sec.~\ref{fix} concludes the algorithm |
| 425 |
< |
discussion with the integration of the equations of motion. \cite{fix} |
| 422 |
> |
simulation. Sec.~\ref{introSec:mdInit} deals with the initialization |
| 423 |
> |
of a simulation. Sec.~\ref{introSec:mdForce} discusses issues |
| 424 |
> |
involved with the calculation of the forces. |
| 425 |
> |
Sec.~\ref{introSec:mdIntegrate} concludes the algorithm discussion |
| 426 |
> |
with the integration of the equations of motion.\cite{Frenkel1996} |
| 427 |
|
|
| 428 |
< |
\subsubsection{initialization} |
| 428 |
> |
\subsection{\label{introSec:mdInit}Initialization} |
| 429 |
|
|
| 430 |
|
When selecting the initial configuration for the simulation it is |
| 431 |
|
important to consider what dynamics one is hoping to observe. |
| 432 |
< |
Ch.~\ref{fix} deals with the formation and equilibrium dynamics of |
| 432 |
> |
Ch.~\ref{chapt:lipid} deals with the formation and equilibrium dynamics of |
| 433 |
|
phospholipid membranes. Therefore in these simulations initial |
| 434 |
|
positions were selected that in some cases dispersed the lipids in |
| 435 |
< |
water, and in other cases structured the lipids into preformed |
| 435 |
> |
water, and in other cases structured the lipids into performed |
| 436 |
|
bilayers. Important considerations at this stage of the simulation are: |
| 437 |
|
\begin{itemize} |
| 438 |
|
\item There are no major overlaps of molecular or atomic orbitals |
| 439 |
< |
\item Velocities are chosen in such a way as to not gie the system a non=zero total momentum or angular momentum. |
| 440 |
< |
\item It is also sometimes desireable to select the velocities to correctly sample the target temperature. |
| 439 |
> |
\item Velocities are chosen in such a way as to not give the system a non=zero total momentum or angular momentum. |
| 440 |
> |
\item It is also sometimes desirable to select the velocities to correctly sample the target temperature. |
| 441 |
|
\end{itemize} |
| 442 |
|
|
| 443 |
|
The first point is important due to the amount of potential energy |
| 444 |
|
generated by having two particles too close together. If overlap |
| 445 |
|
occurs, the first evaluation of forces will return numbers so large as |
| 446 |
< |
to render the numerical integration of teh motion meaningless. The |
| 446 |
> |
to render the numerical integration of the motion meaningless. The |
| 447 |
|
second consideration keeps the system from drifting or rotating as a |
| 448 |
|
whole. This arises from the fact that most simulations are of systems |
| 449 |
|
in equilibrium in the absence of outside forces. Therefore any net |
| 450 |
|
movement would be unphysical and an artifact of the simulation method |
| 451 |
< |
used. The final point addresses teh selection of the magnitude of the |
| 452 |
< |
initial velocities. For many simulations it is convienient to use |
| 451 |
> |
used. The final point addresses the selection of the magnitude of the |
| 452 |
> |
initial velocities. For many simulations it is convenient to use |
| 453 |
|
this opportunity to scale the amount of kinetic energy to reflect the |
| 454 |
|
desired thermal distribution of the system. However, it must be noted |
| 455 |
|
that most systems will require further velocity rescaling after the |
| 456 |
|
first few initial simulation steps due to either loss or gain of |
| 457 |
|
kinetic energy from energy stored in potential degrees of freedom. |
| 458 |
|
|
| 459 |
< |
\subsubsection{Force Evaluation} |
| 459 |
> |
\subsection{\label{introSec:mdForce}Force Evaluation} |
| 460 |
|
|
| 461 |
|
The evaluation of forces is the most computationally expensive portion |
| 462 |
|
of a given molecular dynamics simulation. This is due entirely to the |
| 463 |
|
evaluation of long range forces in a simulation, typically pair-wise. |
| 464 |
|
These forces are most commonly the Van der Waals force, and sometimes |
| 465 |
< |
Coulombic forces as well. For a pair-wise force, there are $fix$ |
| 466 |
< |
pairs to be evaluated, where $n$ is the number of particles in the |
| 467 |
< |
system. This leads to the calculations scaling as $fix$, making large |
| 465 |
> |
Coulombic forces as well. For a pair-wise force, there are $N(N-1)/ 2$ |
| 466 |
> |
pairs to be evaluated, where $N$ is the number of particles in the |
| 467 |
> |
system. This leads to the calculations scaling as $N^2$, making large |
| 468 |
|
simulations prohibitive in the absence of any computation saving |
| 469 |
|
techniques. |
| 470 |
|
|
| 471 |
|
Another consideration one must resolve, is that in a given simulation |
| 472 |
|
a disproportionate number of the particles will feel the effects of |
| 473 |
< |
the surface. \cite{fix} For a cubic system of 1000 particles arranged |
| 474 |
< |
in a $10x10x10$ cube, 488 particles will be exposed to the surface. |
| 475 |
< |
Unless one is simulating an isolated particle group in a vacuum, the |
| 476 |
< |
behavior of the system will be far from the desired bulk |
| 477 |
< |
charecteristics. To offset this, simulations employ the use of |
| 478 |
< |
periodic boundary images. \cite{fix} |
| 473 |
> |
the surface.\cite{allen87:csl} For a cubic system of 1000 particles |
| 474 |
> |
arranged in a $10 \times 10 \times 10$ cube, 488 particles will be |
| 475 |
> |
exposed to the surface. Unless one is simulating an isolated particle |
| 476 |
> |
group in a vacuum, the behavior of the system will be far from the |
| 477 |
> |
desired bulk characteristics. To offset this, simulations employ the |
| 478 |
> |
use of periodic boundary images.\cite{born:1912} |
| 479 |
|
|
| 480 |
|
The technique involves the use of an algorithm that replicates the |
| 481 |
< |
simulation box on an infinite lattice in cartesian space. Any given |
| 481 |
> |
simulation box on an infinite lattice in Cartesian space. Any given |
| 482 |
|
particle leaving the simulation box on one side will have an image of |
| 483 |
< |
itself enter on the opposite side (see Fig.~\ref{fix}). |
| 484 |
< |
\begin{equation} |
| 485 |
< |
EQ Here |
| 486 |
< |
\end{equation} |
| 487 |
< |
In addition, this sets that any given particle pair has an image, real |
| 324 |
< |
or periodic, within $fix$ of each other. A discussion of the method |
| 325 |
< |
used to calculate the periodic image can be found in Sec.\ref{fix}. |
| 483 |
> |
itself enter on the opposite side (see Fig.~\ref{introFig:pbc}). In |
| 484 |
> |
addition, this sets that any two particles have an image, real or |
| 485 |
> |
periodic, within $\text{box}/2$ of each other. A discussion of the |
| 486 |
> |
method used to calculate the periodic image can be found in |
| 487 |
> |
Sec.\ref{oopseSec:pbc}. |
| 488 |
|
|
| 489 |
+ |
\begin{figure} |
| 490 |
+ |
\centering |
| 491 |
+ |
\includegraphics[width=\linewidth]{pbcFig.eps} |
| 492 |
+ |
\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.} |
| 493 |
+ |
\label{introFig:pbc} |
| 494 |
+ |
\end{figure} |
| 495 |
+ |
|
| 496 |
|
Returning to the topic of the computational scale of the force |
| 497 |
|
evaluation, the use of periodic boundary conditions requires that a |
| 498 |
|
cutoff radius be employed. Using a cutoff radius improves the |
| 499 |
|
efficiency of the force evaluation, as particles farther than a |
| 500 |
< |
predetermined distance, $fix$, are not included in the |
| 501 |
< |
calculation. \cite{fix} In a simultation with periodic images, $fix$ |
| 502 |
< |
has a maximum value of $fix$. Fig.~\ref{fix} illustrates how using an |
| 503 |
< |
$fix$ larger than this value, or in the extreme limit of no $fix$ at |
| 504 |
< |
all, the corners of the simulation box are unequally weighted due to |
| 505 |
< |
the lack of particle images in the $x$, $y$, or $z$ directions past a |
| 506 |
< |
disance of $fix$. |
| 500 |
> |
predetermined distance, $r_{\text{cut}}$, are not included in the |
| 501 |
> |
calculation.\cite{Frenkel1996} In a simulation with periodic images, |
| 502 |
> |
$r_{\text{cut}}$ has a maximum value of $\text{box}/2$. |
| 503 |
> |
Fig.~\ref{introFig:rMax} illustrates how when using an |
| 504 |
> |
$r_{\text{cut}}$ larger than this value, or in the extreme limit of no |
| 505 |
> |
$r_{\text{cut}}$ at all, the corners of the simulation box are |
| 506 |
> |
unequally weighted due to the lack of particle images in the $x$, $y$, |
| 507 |
> |
or $z$ directions past a distance of $\text{box} / 2$. |
| 508 |
|
|
| 509 |
< |
With the use of an $fix$, however, comes a discontinuity in the |
| 510 |
< |
potential energy curve (Fig.~\ref{fix}). To fix this discontinuity, |
| 511 |
< |
one calculates the potential energy at the $r_{\text{cut}}$, and add |
| 512 |
< |
that value to the potential. This causes the function to go smoothly |
| 513 |
< |
to zero at the cutoff radius. This ensures conservation of energy |
| 514 |
< |
when integrating the Newtonian equations of motion. |
| 509 |
> |
\begin{figure} |
| 510 |
> |
\centering |
| 511 |
> |
\includegraphics[width=\linewidth]{rCutMaxFig.eps} |
| 512 |
> |
\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).} |
| 513 |
> |
\label{introFig:rMax} |
| 514 |
> |
\end{figure} |
| 515 |
> |
|
| 516 |
> |
With the use of an $r_{\text{cut}}$, however, comes a discontinuity in |
| 517 |
> |
the potential energy curve (Fig.~\ref{introFig:shiftPot}). To fix this |
| 518 |
> |
discontinuity, one calculates the potential energy at the |
| 519 |
> |
$r_{\text{cut}}$, and adds that value to the potential, causing |
| 520 |
> |
the function to go smoothly to zero at the cutoff radius. This |
| 521 |
> |
shifted potential ensures conservation of energy when integrating the |
| 522 |
> |
Newtonian equations of motion. |
| 523 |
> |
|
| 524 |
> |
\begin{figure} |
| 525 |
> |
\centering |
| 526 |
> |
\includegraphics[width=\linewidth]{shiftedPot.eps} |
| 527 |
> |
\caption[Shifting the Lennard-Jones Potential]{The Lennard-Jones potential (blue line) is shifted (red line) to remove the discontinuity at $r_{\text{cut}}$.} |
| 528 |
> |
\label{introFig:shiftPot} |
| 529 |
> |
\end{figure} |
| 530 |
|
|
| 531 |
|
The second main simplification used in this research is the Verlet |
| 532 |
|
neighbor list. \cite{allen87:csl} In the Verlet method, one generates |
| 539 |
|
giving rise to the possibility that a particle has left or joined a |
| 540 |
|
neighbor list. |
| 541 |
|
|
| 542 |
< |
\subsection{\label{introSec:MDintegrate} Integration of the equations of motion} |
| 542 |
> |
\subsection{\label{introSec:mdIntegrate} Integration of the equations of motion} |
| 543 |
|
|
| 544 |
|
A starting point for the discussion of molecular dynamics integrators |
| 545 |
< |
is the Verlet algorithm. \cite{Frenkel1996} It begins with a Taylor |
| 545 |
> |
is the Verlet algorithm.\cite{Frenkel1996} It begins with a Taylor |
| 546 |
|
expansion of position in time: |
| 547 |
|
\begin{equation} |
| 548 |
< |
eq here |
| 548 |
> |
q(t+\Delta t)= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 + |
| 549 |
> |
\frac{\Delta t^3}{3!}\frac{\partial q(t)}{\partial t} + |
| 550 |
> |
\mathcal{O}(\Delta t^4) |
| 551 |
|
\label{introEq:verletForward} |
| 552 |
|
\end{equation} |
| 553 |
|
As well as, |
| 554 |
|
\begin{equation} |
| 555 |
< |
eq here |
| 555 |
> |
q(t-\Delta t)= q(t) - v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 - |
| 556 |
> |
\frac{\Delta t^3}{3!}\frac{\partial q(t)}{\partial t} + |
| 557 |
> |
\mathcal{O}(\Delta t^4) |
| 558 |
|
\label{introEq:verletBack} |
| 559 |
|
\end{equation} |
| 560 |
< |
Adding together Eq.~\ref{introEq:verletForward} and |
| 560 |
> |
Where $m$ is the mass of the particle, $q(t)$ is the position at time |
| 561 |
> |
$t$, $v(t)$ the velocity, and $F(t)$ the force acting on the |
| 562 |
> |
particle. Adding together Eq.~\ref{introEq:verletForward} and |
| 563 |
|
Eq.~\ref{introEq:verletBack} results in, |
| 564 |
|
\begin{equation} |
| 565 |
< |
eq here |
| 565 |
> |
q(t+\Delta t)+q(t-\Delta t) = |
| 566 |
> |
2q(t) + \frac{F(t)}{m}\Delta t^2 + \mathcal{O}(\Delta t^4) |
| 567 |
|
\label{introEq:verletSum} |
| 568 |
|
\end{equation} |
| 569 |
|
Or equivalently, |
| 570 |
|
\begin{equation} |
| 571 |
< |
eq here |
| 571 |
> |
q(t+\Delta t) = |
| 572 |
> |
2q(t) - q(t-\Delta t) + \frac{F(t)}{m}\Delta t^2 + |
| 573 |
> |
\mathcal{O}(\Delta t^4) |
| 574 |
|
\label{introEq:verletFinal} |
| 575 |
|
\end{equation} |
| 576 |
|
Which contains an error in the estimate of the new positions on the |
| 577 |
|
order of $\Delta t^4$. |
| 578 |
|
|
| 579 |
|
In practice, however, the simulations in this research were integrated |
| 580 |
< |
with a velocity reformulation of teh Verlet method. \cite{allen87:csl} |
| 580 |
> |
with a velocity reformulation of the Verlet method.\cite{allen87:csl} |
| 581 |
|
\begin{equation} |
| 582 |
< |
eq here |
| 582 |
> |
q(t+\Delta t)= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 |
| 583 |
|
\label{introEq:MDvelVerletPos} |
| 584 |
|
\end{equation} |
| 585 |
|
\begin{equation} |
| 586 |
< |
eq here |
| 586 |
> |
v(t+\Delta t) = v(t) + \frac{\Delta t}{2m}[F(t) + F(t+\Delta t)] |
| 587 |
|
\label{introEq:MDvelVerletVel} |
| 588 |
|
\end{equation} |
| 589 |
|
The original Verlet algorithm can be regained by substituting the |
| 592 |
|
very little long term drift in energy conservation. Energy |
| 593 |
|
conservation in a molecular dynamics simulation is of extreme |
| 594 |
|
importance, as it is a measure of how closely one is following the |
| 595 |
< |
``true'' trajectory wtih the finite integration scheme. An exact |
| 595 |
> |
``true'' trajectory with the finite integration scheme. An exact |
| 596 |
|
solution to the integration will conserve area in phase space, as well |
| 597 |
|
as be reversible in time, that is, the trajectory integrated forward |
| 598 |
|
or backwards will exactly match itself. Having a finite algorithm |
| 600 |
|
therefore increases, but does not guarantee the ``correctness'' or the |
| 601 |
|
integrated trajectory. |
| 602 |
|
|
| 603 |
< |
It can be shown, \cite{Frenkel1996} that although the Verlet algorithm |
| 603 |
> |
It can be shown,\cite{Frenkel1996} that although the Verlet algorithm |
| 604 |
|
does not rigorously preserve the actual Hamiltonian, it does preserve |
| 605 |
|
a pseudo-Hamiltonian which shadows the real one in phase space. This |
| 606 |
< |
pseudo-Hamiltonian is proveably area-conserving as well as time |
| 606 |
> |
pseudo-Hamiltonian is provably area-conserving as well as time |
| 607 |
|
reversible. The fact that it shadows the true Hamiltonian in phase |
| 608 |
|
space is acceptable in actual simulations as one is interested in the |
| 609 |
|
ensemble average of the observable being measured. From the ergodic |
| 614 |
|
\subsection{\label{introSec:MDfurther}Further Considerations} |
| 615 |
|
In the simulations presented in this research, a few additional |
| 616 |
|
parameters are needed to describe the motions. The simulations |
| 617 |
< |
involving water and phospholipids in Chapt.~\ref{chaptLipids} are |
| 617 |
> |
involving water and phospholipids in Ch.~\ref{chaptLipids} are |
| 618 |
|
required to integrate the equations of motions for dipoles on atoms. |
| 619 |
|
This involves an additional three parameters be specified for each |
| 620 |
|
dipole atom: $\phi$, $\theta$, and $\psi$. These three angles are |
| 636 |
|
\label{introEq:MDeuleeerPsi} |
| 637 |
|
\end{equation} |
| 638 |
|
Where $\omega^s_i$ is the angular velocity in the lab space frame |
| 639 |
< |
along cartesian coordinate $i$. However, a difficulty arises when |
| 639 |
> |
along Cartesian coordinate $i$. However, a difficulty arises when |
| 640 |
|
attempting to integrate Eq.~\ref{introEq:MDeulerPhi} and |
| 641 |
|
Eq.~\ref{introEq:MDeulerPsi}. The $\frac{1}{\sin \theta}$ present in |
| 642 |
|
both equations means there is a non-physical instability present when |
| 666 |
|
\end{equation} |
| 667 |
|
Here, $r_j$ and $p_j$ are the position and conjugate momenta of a |
| 668 |
|
degree of freedom, and $f_j$ is the force on that degree of freedom. |
| 669 |
< |
$\Gamma$ is defined as the set of all positions nad conjugate momenta, |
| 669 |
> |
$\Gamma$ is defined as the set of all positions and conjugate momenta, |
| 670 |
|
$\{r_j,p_j\}$, and the propagator, $U(t)$, is defined |
| 671 |
|
\begin {equation} |
| 672 |
|
eq here |
| 722 |
|
This is the velocity Verlet formulation presented in |
| 723 |
|
Sec.~\ref{introSec:MDintegrate}. Because this integration scheme is |
| 724 |
|
comprised of unitary propagators, it is symplectic, and therefore area |
| 725 |
< |
preserving in phase space. From the preceeding fatorization, one can |
| 725 |
> |
preserving in phase space. From the preceding factorization, one can |
| 726 |
|
see that the integration of the equations of motion would follow: |
| 727 |
|
\begin{enumerate} |
| 728 |
|
\item calculate the velocities at the half step, $\frac{\Delta t}{2}$, from the forces calculated at the initial position. |
| 746 |
|
eq here |
| 747 |
|
\label{introEq:SR1} |
| 748 |
|
\end{equation} |
| 749 |
< |
Where $\boldsymbol{\tau}(\mathbf{A})$ are the tourques of the system |
| 749 |
> |
Where $\boldsymbol{\tau}(\mathbf{A})$ are the torques of the system |
| 750 |
|
due to the configuration, and $\boldsymbol{/pi}$ are the conjugate |
| 751 |
|
angular momenta of the system. The propagator, $G(\Delta t)$, becomes |
| 752 |
|
\begin{equation} |
| 753 |
|
eq here |
| 754 |
|
\label{introEq:SR2} |
| 755 |
|
\end{equation} |
| 756 |
< |
Propagation fo the linear and angular momenta follows as in the Verlet |
| 757 |
< |
scheme. The propagation of positions also follows the verlet scheme |
| 756 |
> |
Propagation of the linear and angular momenta follows as in the Verlet |
| 757 |
> |
scheme. The propagation of positions also follows the Verlet scheme |
| 758 |
|
with the addition of a further symplectic splitting of the rotation |
| 759 |
|
matrix propagation, $\mathcal{G}_{\text{rot}}(\Delta t)$. |
| 760 |
|
\begin{equation} |
| 766 |
|
unitary and symplectic, the entire integration scheme is also |
| 767 |
|
symplectic and time reversible. |
| 768 |
|
|
| 769 |
< |
\section{\label{introSec:chapterLayout}Chapter Layout} |
| 769 |
> |
\section{\label{introSec:layout}Dissertation Layout} |
| 770 |
> |
|
| 771 |
> |
This dissertation is divided as follows:Chapt.~\ref{chapt:RSA} |
| 772 |
> |
presents the random sequential adsorption simulations of related |
| 773 |
> |
pthalocyanines on a gold (111) surface. Ch.~\ref{chapt:OOPSE} |
| 774 |
> |
is about the writing of the molecular dynamics simulation package |
| 775 |
> |
{\sc oopse}, Ch.~\ref{chapt:lipid} regards the simulations of |
| 776 |
> |
phospholipid bilayers using a mesoscale model, and lastly, |
| 777 |
> |
Ch.~\ref{chapt:conclusion} concludes this dissertation with a |
| 778 |
> |
summary of all results. The chapters are arranged in chronological |
| 779 |
> |
order, and reflect the progression of techniques I employed during my |
| 780 |
> |
research. |
| 781 |
|
|
| 782 |
< |
\subsection{\label{introSec:RSA}Random Sequential Adsorption} |
| 782 |
> |
The chapter concerning random sequential adsorption |
| 783 |
> |
simulations is a study in applying the principles of theoretical |
| 784 |
> |
research in order to obtain a simple model capable of explaining the |
| 785 |
> |
results. My advisor, Dr. Gezelter, and I were approached by a |
| 786 |
> |
colleague, Dr. Lieberman, about possible explanations for partial |
| 787 |
> |
coverage of a gold surface by a particular compound of hers. We |
| 788 |
> |
suggested it might be due to the statistical packing fraction of disks |
| 789 |
> |
on a plane, and set about to simulate this system. As the events in |
| 790 |
> |
our model were not dynamic in nature, a Monte Carlo method was |
| 791 |
> |
employed. Here, if a molecule landed on the surface without |
| 792 |
> |
overlapping another, then its landing was accepted. However, if there |
| 793 |
> |
was overlap, the landing we rejected and a new random landing location |
| 794 |
> |
was chosen. This defined our acceptance rules and allowed us to |
| 795 |
> |
construct a Markov chain whose limiting distribution was the surface |
| 796 |
> |
coverage in which we were interested. |
| 797 |
|
|
| 798 |
< |
\subsection{\label{introSec:OOPSE}The OOPSE Simulation Package} |
| 798 |
> |
The following chapter, about the simulation package {\sc oopse}, |
| 799 |
> |
describes in detail the large body of scientific code that had to be |
| 800 |
> |
written in order to study phospholipid bilayer. Although there are |
| 801 |
> |
pre-existing molecular dynamic simulation packages available, none |
| 802 |
> |
were capable of implementing the models we were developing.{\sc oopse} |
| 803 |
> |
is a unique package capable of not only integrating the equations of |
| 804 |
> |
motion in Cartesian space, but is also able to integrate the |
| 805 |
> |
rotational motion of rigid bodies and dipoles. Add to this the |
| 806 |
> |
ability to perform calculations across parallel processors and a |
| 807 |
> |
flexible script syntax for creating systems, and {\sc oopse} becomes a |
| 808 |
> |
very powerful scientific instrument for the exploration of our model. |
| 809 |
|
|
| 810 |
< |
\subsection{\label{introSec:bilayers}A Mesoscale Model for |
| 811 |
< |
Phospholipid Bilayers} |
| 810 |
> |
Bringing us to Ch.~\ref{chapt:lipid}. Using {\sc oopse}, I have been |
| 811 |
> |
able to parameterize a mesoscale model for phospholipid simulations. |
| 812 |
> |
This model retains information about solvent ordering about the |
| 813 |
> |
bilayer, as well as information regarding the interaction of the |
| 814 |
> |
phospholipid head groups' dipole with each other and the surrounding |
| 815 |
> |
solvent. These simulations give us insight into the dynamic events |
| 816 |
> |
that lead to the formation of phospholipid bilayers, as well as |
| 817 |
> |
provide the foundation for future exploration of bilayer phase |
| 818 |
> |
behavior with this model. |
| 819 |
> |
|
| 820 |
> |
Which leads into the last chapter, where I discuss future directions |
| 821 |
> |
for both{\sc oopse} and this mesoscale model. Additionally, I will |
| 822 |
> |
give a summary of results for this dissertation. |
| 823 |
> |
|
| 824 |
> |
|
