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\chapter{\label{chapt:intro}INTRODUCTION AND THEORETICAL BACKGROUND} |
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\Omega(E) = \Omega(E_{\gamma}) \times \Omega(E - E_{\gamma}). |
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\label{introEq:SM1} |
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\end{equation} |
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Or additively as |
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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}). |
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\label{introEq:SM2} |
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\end{equation} |
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The solution to Eq.~\ref{introEq:SM2} maximizes the number of |
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The solution of interest to Eq.~\ref{introEq:SM2} maximizes the number of |
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degenerate configurations in $E$. \cite{Frenkel1996} |
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This gives |
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\begin{equation} |
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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 |
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increase for an irreversible process.\cite{chandler:1987} Here the |
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process is the partitioning of energy among the two systems. This |
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maximization of the degeneracy when partitioning the energy of the system can be likened to the maximization of the entropy for this process. This |
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allows the following definition of entropy: |
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\begin{equation} |
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S = k_B \ln \Omega(E), |
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states the coupled system is able to assume. Namely, |
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\begin{equation} |
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P_{\gamma} \propto \Omega( E_{\text{bath}} ) = |
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e^{\ln \Omega( E - E_{\gamma})} |
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e^{\ln \Omega( E - E_{\gamma})}. |
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\label{introEq:SM11} |
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\end{equation} |
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With $E_{\gamma} \ll E$, $\ln \Omega$ can be expanded in a Taylor series: |
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Because $E_{\gamma} \ll E$, $\ln \Omega$ can be expanded in a Taylor series: |
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\begin{equation} |
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\ln \Omega ( E - E_{\gamma}) = |
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\ln \Omega (E) - |
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E_{\gamma} \frac{\partial \ln \Omega }{\partial E} |
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+ \ldots |
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+ \ldots. |
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\label{introEq:SM12} |
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\end{equation} |
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Higher order terms are omitted as $E$ is an infinite thermal |
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bath. Further, using Eq.~\ref{introEq:SM7}, Eq.~\ref{introEq:SM11} can |
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be rewritten: |
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\begin{equation} |
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P_{\gamma} \propto e^{-\beta E_{\gamma}} |
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P_{\gamma} \propto e^{-\beta E_{\gamma}}, |
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\label{introEq:SM13} |
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\end{equation} |
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Where $\ln \Omega(E)$ has been factored out of the proportionality as a |
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where $\ln \Omega(E)$ has been factored out of the proportionality as a |
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constant. Normalizing the probability ($\int\limits_{\boldsymbol{\Gamma}} |
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d\boldsymbol{\Gamma} P_{\gamma} = 1$) gives |
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\begin{equation} |
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P_{\gamma} = \frac{e^{-\beta E_{\gamma}}} |
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{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}} |
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{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}}. |
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\label{introEq:SM14} |
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\end{equation} |
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This result is the standard Boltzmann statistical distribution. |
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\langle A \rangle = |
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\frac{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} |
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A( \boldsymbol{\Gamma} ) e^{-\beta E_{\gamma}}} |
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{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}} |
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{\int\limits_{\boldsymbol{\Gamma}} d\boldsymbol{\Gamma} e^{-\beta E_{\gamma}}}. |
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\label{introEq:SM15} |
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\end{equation} |
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average of an observable. Namely, |
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\begin{equation} |
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\langle A \rangle_t = \frac{1}{\tau} |
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\int_0^{\tau} A[\boldsymbol{\Gamma}(t)]\,dt |
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\int_0^{\tau} A[\boldsymbol{\Gamma}(t)]\,dt, |
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\label{introEq:SM16} |
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\end{equation} |
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Where the value of an observable is averaged over the length of time, |
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where the value of an observable is averaged over the length of time, |
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$\tau$, that the simulation is run. This type of measurement mirrors |
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the experimental measurement of an observable. In an experiment, the |
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instrument analyzing the system must average its observation over the |
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force method of sampling.\cite{Frenkel1996} Consider the following |
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single dimensional integral: |
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\begin{equation} |
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I = \int_a^b f(x)dx |
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I = \int_a^b f(x)dx. |
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\label{eq:MCex1} |
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\end{equation} |
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The equation can be recast as: |
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\begin{equation} |
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I = \int^b_a \frac{f(x)}{\rho(x)} \rho(x) dx |
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I = \int^b_a \frac{f(x)}{\rho(x)} \rho(x) dx, |
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\label{introEq:Importance1} |
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\end{equation} |
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Where $\rho(x)$ is an arbitrary probability distribution in $x$. If |
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where $\rho(x)$ is an arbitrary probability distribution in $x$. If |
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one conducts $\tau$ trials selecting a random number, $\zeta_\tau$, |
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from the distribution $\rho(x)$ on the interval $[a,b]$, then |
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Eq.~\ref{introEq:Importance1} becomes |
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\begin{equation} |
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I= \lim_{\tau \rightarrow \infty}\biggl \langle \frac{f(x)}{\rho(x)} \biggr \rangle_{\text{trials}[a,b]} |
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I= \lim_{\tau \rightarrow \infty}\biggl \langle \frac{f(x)}{\rho(x)} \biggr \rangle_{\text{trials}[a,b]}. |
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\label{introEq:Importance2} |
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\end{equation} |
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If $\rho(x)$ is uniformly distributed over the interval $[a,b]$, |
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\begin{equation} |
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\rho(x) = \frac{1}{b-a} |
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\rho(x) = \frac{1}{b-a}, |
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\label{introEq:importance2b} |
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\end{equation} |
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then the integral can be rewritten as |
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\begin{equation} |
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I = (b-a)\lim_{\tau \rightarrow \infty} |
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\langle f(x) \rangle_{\text{trials}[a,b]} |
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\langle f(x) \rangle_{\text{trials}[a,b]}. |
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\label{eq:MCex2} |
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\end{equation} |
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|
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\begin{equation} |
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\langle A \rangle = \frac{\int d^N \mathbf{r}~A(\mathbf{r}^N)% |
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e^{-\beta V(\mathbf{r}^N)}}% |
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{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}} |
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{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}}, |
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\label{eq:mcEnsAvg} |
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\end{equation} |
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Where $\mathbf{r}^N$ stands for the coordinates of all $N$ particles |
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where $\mathbf{r}^N$ stands for the coordinates of all $N$ particles |
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and $A$ is some observable that is only dependent on position. This is |
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the ensemble average of $A$ as presented in |
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Sec.~\ref{introSec:statThermo}, except here $A$ is independent of |
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\begin {equation} |
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\rho_{kT}(\mathbf{r}^N) = |
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\frac{e^{-\beta V(\mathbf{r}^N)}} |
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{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}} |
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{\int d^N \mathbf{r}~e^{-\beta V(\mathbf{r}^N)}}, |
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\label{introEq:MCboltzman} |
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\end{equation} |
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Where $\rho_{kT}$ is the Boltzmann distribution. The ensemble average |
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where $\rho_{kT}$ is the Boltzmann distribution. The ensemble average |
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can be rewritten as |
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\begin{equation} |
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\langle A \rangle = \int d^N \mathbf{r}~A(\mathbf{r}^N) |
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\rho_{kT}(\mathbf{r}^N) |
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\rho_{kT}(\mathbf{r}^N). |
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\label{introEq:Importance3} |
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\end{equation} |
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Applying Eq.~\ref{introEq:Importance1} one obtains |
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\begin{equation} |
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\langle A \rangle = \biggl \langle |
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\frac{ A \rho_{kT}(\mathbf{r}^N) } |
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{\rho(\mathbf{r}^N)} \biggr \rangle_{\text{trials}} |
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{\rho(\mathbf{r}^N)} \biggr \rangle_{\text{trials}}. |
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\label{introEq:Importance4} |
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\end{equation} |
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By selecting $\rho(\mathbf{r}^N)$ to be $\rho_{kT}(\mathbf{r}^N)$ |
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Eq.~\ref{introEq:Importance4} becomes |
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\begin{equation} |
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\langle A \rangle = \langle A(\mathbf{r}^N) \rangle_{kT} |
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\langle A \rangle = \langle A(\mathbf{r}^N) \rangle_{kT}. |
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\label{introEq:Importance5} |
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\end{equation} |
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The difficulty is selecting points $\mathbf{r}^N$ such that they are |
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|
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The transition probability is given by the following: |
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\begin{equation} |
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\pi_{mn} = \alpha_{mn} \times \accMe(m \rightarrow n) |
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\pi_{mn} = \alpha_{mn} \times \accMe(m \rightarrow n), |
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\label{introEq:MCpi} |
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\end{equation} |
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Where $\alpha_{mn}$ is the probability of attempting the move $m |
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where $\alpha_{mn}$ is the probability of attempting the move $m |
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\rightarrow n$, and $\accMe$ is the probability of accepting the move |
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$m \rightarrow n$. Defining a probability vector, |
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$\boldsymbol{\rho}$, such that |
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\begin{equation} |
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\boldsymbol{\rho} = \{\rho_1, \rho_2, \ldots \rho_m, \rho_n, |
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\ldots \rho_N \} |
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\ldots \rho_N \}, |
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\label{introEq:MCrhoVector} |
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\end{equation} |
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a transition matrix $\boldsymbol{\Pi}$ can be defined, |
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whose elements are $\pi_{mn}$, for each given transition. The |
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limiting distribution of the Markov chain can then be found by |
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applying the transition matrix an infinite number of times to the |
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distribution vector. |
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distribution vector, |
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\begin{equation} |
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\boldsymbol{\rho}_{\text{limit}} = |
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\lim_{N \rightarrow \infty} \boldsymbol{\rho}_{\text{initial}} |
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\boldsymbol{\Pi}^N |
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\boldsymbol{\Pi}^N. |
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\label{introEq:MCmarkovLimit} |
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\end{equation} |
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The limiting distribution of the chain is independent of the starting |
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distribution, and successive applications of the transition matrix |
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will only yield the limiting distribution again. |
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will only yield the limiting distribution again, |
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\begin{equation} |
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\boldsymbol{\rho}_{\text{limit}} = \boldsymbol{\rho}_{\text{limit}} |
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\boldsymbol{\Pi} |
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\boldsymbol{\Pi}. |
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\label{introEq:MCmarkovEquil} |
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\end{equation} |
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|
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Eq.~\ref{introEq:MCmarkovEquil} is solved such that |
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$\boldsymbol{\rho}_{\text{limit}}$ matches the Boltzmann distribution |
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of states. The method accomplishes this by imposing the strong |
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condition of microscopic reversibility on the equilibrium |
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distribution. This means that at equilibrium, the probability of going |
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from $m$ to $n$ is the same as going from $n$ to $m$. |
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condition of detailed balance on the equilibrium |
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distribution. This means that the probability of going |
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from $m$ to $n$ is the same as going from $n$ to $m$, |
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\begin{equation} |
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\rho_m\pi_{mn} = \rho_n\pi_{nm} |
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\rho_m\pi_{mn} = \rho_n\pi_{nm}. |
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\label{introEq:MCmicroReverse} |
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\end{equation} |
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Further, $\boldsymbol{\alpha}$ is chosen to be a symmetric matrix in |
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Eq.~\ref{introEq:MCmicroReverse} becomes |
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\begin{equation} |
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\frac{\accMe(m \rightarrow n)}{\accMe(n \rightarrow m)} = |
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\frac{\rho_n}{\rho_m} |
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\frac{\rho_n}{\rho_m}. |
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\label{introEq:MCmicro2} |
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\end{equation} |
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For a Boltzmann limiting distribution, |
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\begin{equation} |
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\frac{\rho_n}{\rho_m} = e^{-\beta[\mathcal{U}(n) - \mathcal{U}(m)]} |
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= e^{-\beta \Delta \mathcal{U}} |
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= e^{-\beta \Delta \mathcal{U}}, |
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\label{introEq:MCmicro3} |
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\end{equation} |
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This allows for the following set of acceptance rules be defined: |
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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: |
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\begin{equation} |
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\accMe( m \rightarrow n ) = |
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\begin{cases} |
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calculate time correlation functions of the form\cite{Hansen86} |
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\begin{equation} |
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\langle A(t)\,A(0)\rangle = \lim_{\tau\rightarrow\infty} \frac{1}{\tau} |
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\int_0^{\tau} A(t+t^{\prime})\,A(t^{\prime})\,dt^{\prime} |
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\int_0^{\tau} A(t+t^{\prime})\,A(t^{\prime})\,dt^{\prime}. |
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\label{introEq:timeCorr} |
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\end{equation} |
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These correlations can be used to measure fundamental time constants |
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subsequent force evaluations, pair calculations are only calculated |
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from the neighbor lists. The lists are updated if any particle |
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in the system moves farther than $r_{\text{list}}-r_{\text{cut}}$, |
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which indeicates the possibility that a particle has left or joined the |
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which indicates the possibility that a particle has left or joined the |
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neighbor list. |
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|
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\subsection{\label{introSec:mdIntegrate} Integration of the Equations of Motion} |
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\begin{equation} |
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q(t+\Delta t)= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 + |
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\frac{\Delta t^3}{3!}\frac{\partial^3 q(t)}{\partial t^3} + |
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\mathcal{O}(\Delta t^4) |
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\mathcal{O}(\Delta t^4) . |
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\label{introEq:verletForward} |
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\end{equation} |
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As well as, |
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\begin{equation} |
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q(t-\Delta t)= q(t) - v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 - |
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\frac{\Delta t^3}{3!}\frac{\partial^3 q(t)}{\partial t^3} + |
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\mathcal{O}(\Delta t^4) |
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\mathcal{O}(\Delta t^4) , |
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\label{introEq:verletBack} |
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\end{equation} |
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Where $m$ is the mass of the particle, $q(t)$ is the position at time |
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where $m$ is the mass of the particle, $q(t)$ is the position at time |
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$t$, $v(t)$ the velocity, and $F(t)$ the force acting on the |
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particle. Adding together Eq.~\ref{introEq:verletForward} and |
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Eq.~\ref{introEq:verletBack} results in, |
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\begin{equation} |
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q(t+\Delta t)+q(t-\Delta t) = |
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2q(t) + \frac{F(t)}{m}\Delta t^2 + \mathcal{O}(\Delta t^4) |
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> |
2q(t) + \frac{F(t)}{m}\Delta t^2 + \mathcal{O}(\Delta t^4) , |
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\label{introEq:verletSum} |
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\end{equation} |
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Or equivalently, |
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> |
or equivalently, |
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\begin{equation} |
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q(t+\Delta t) \approx |
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< |
2q(t) - q(t-\Delta t) + \frac{F(t)}{m}\Delta t^2 |
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> |
2q(t) - q(t-\Delta t) + \frac{F(t)}{m}\Delta t^2. |
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\label{introEq:verletFinal} |
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\end{equation} |
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Which contains an error in the estimate of the new positions on the |
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In practice, however, the simulations in this research were integrated |
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with the velocity reformulation of the Verlet method.\cite{allen87:csl} |
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\begin{align} |
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< |
q(t+\Delta t) &= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 % |
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> |
q(t+\Delta t) &= q(t) + v(t)\Delta t + \frac{F(t)}{2m}\Delta t^2 ,% |
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\label{introEq:MDvelVerletPos} \\% |
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% |
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< |
v(t+\Delta t) &= v(t) + \frac{\Delta t}{2m}[F(t) + F(t+\Delta t)] % |
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> |
v(t+\Delta t) &= v(t) + \frac{\Delta t}{2m}[F(t) + F(t+\Delta t)] .% |
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\label{introEq:MDvelVerletVel} |
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\end{align} |
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The original Verlet algorithm can be regained by substituting the |
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\sin\phi\sin\theta &% |
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-\cos\phi\sin\theta &% |
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\cos\theta |
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< |
\end{bmatrix} |
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> |
\end{bmatrix}. |
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\label{introEq:EulerRotMat} |
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\end{equation} |
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|
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\begin{align} |
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\dot{\phi} &= -\omega^s_x \frac{\sin\phi\cos\theta}{\sin\theta} + |
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\omega^s_y \frac{\cos\phi\cos\theta}{\sin\theta} + |
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< |
\omega^s_z |
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> |
\omega^s_z, |
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\label{introEq:MDeulerPhi} \\% |
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% |
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< |
\dot{\theta} &= \omega^s_x \cos\phi + \omega^s_y \sin\phi |
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> |
\dot{\theta} &= \omega^s_x \cos\phi + \omega^s_y \sin\phi, |
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\label{introEq:MDeulerTheta} \\% |
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% |
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\dot{\psi} &= \omega^s_x \frac{\sin\phi}{\sin\theta} - |
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< |
\omega^s_y \frac{\cos\phi}{\sin\theta} |
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> |
\omega^s_y \frac{\cos\phi}{\sin\theta}, |
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\label{introEq:MDeulerPsi} |
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\end{align} |
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< |
Where $\omega^s_{\alpha}$ is the angular velocity in the lab space frame |
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> |
where $\omega^s_{\alpha}$ is the angular velocity in the lab space frame |
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along Cartesian coordinate $\alpha$. However, a difficulty arises when |
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attempting to integrate Eq.~\ref{introEq:MDeulerPhi} and |
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Eq.~\ref{introEq:MDeulerPsi}. The $\frac{1}{\sin \theta}$ present in |
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defined as, |
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\begin{equation} |
| 757 |
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iL=\sum^f_{j=1} \biggl [\dot{q}_j\frac{\partial}{\partial q_j} + |
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< |
F_j\frac{\partial}{\partial p_j} \biggr ] |
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> |
F_j\frac{\partial}{\partial p_j} \biggr ]. |
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\label{introEq:LiouvilleOperator} |
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\end{equation} |
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Here, $q_j$ and $p_j$ are the position and conjugate momenta of a |
| 763 |
|
$\Gamma$ is defined as the set of all positions and conjugate momenta, |
| 764 |
|
$\{q_j,p_j\}$, and the propagator, $U(t)$, is defined |
| 765 |
|
\begin {equation} |
| 766 |
< |
U(t) = e^{iLt} |
| 766 |
> |
U(t) = e^{iLt}. |
| 767 |
|
\label{introEq:Lpropagator} |
| 768 |
|
\end{equation} |
| 769 |
|
This allows the specification of $\Gamma$ at any time $t$ as |
| 770 |
|
\begin{equation} |
| 771 |
< |
\Gamma(t) = U(t)\Gamma(0) |
| 771 |
> |
\Gamma(t) = U(t)\Gamma(0). |
| 772 |
|
\label{introEq:Lp2} |
| 773 |
|
\end{equation} |
| 774 |
|
It is important to note, $U(t)$ is a unitary operator meaning |
| 775 |
|
\begin{equation} |
| 776 |
< |
U(-t)=U^{-1}(t) |
| 776 |
> |
U(-t)=U^{-1}(t). |
| 777 |
|
\label{introEq:Lp3} |
| 778 |
|
\end{equation} |
| 779 |
|
|
| 780 |
|
Decomposing $L$ into two parts, $iL_1$ and $iL_2$, one can use the |
| 781 |
|
Trotter theorem to yield |
| 782 |
|
\begin{align} |
| 783 |
< |
e^{iLt} &= e^{i(L_1 + L_2)t} \notag \\% |
| 783 |
> |
e^{iLt} &= e^{i(L_1 + L_2)t}, \notag \\% |
| 784 |
|
% |
| 785 |
< |
&= \biggl [ e^{i(L_1 +L_2)\frac{t}{P}} \biggr]^P \notag \\% |
| 785 |
> |
&= \biggl [ e^{i(L_1 +L_2)\frac{t}{P}} \biggr]^P, \notag \\% |
| 786 |
|
% |
| 787 |
|
&= \biggl [ e^{iL_1\frac{\Delta t}{2}}\, e^{iL_2\Delta t}\, |
| 788 |
|
e^{iL_1\frac{\Delta t}{2}} \biggr ]^P + |
| 789 |
< |
\mathcal{O}\biggl (\frac{t^3}{P^2} \biggr ) \label{introEq:Lp4} |
| 789 |
> |
\mathcal{O}\biggl (\frac{t^3}{P^2} \biggr ), \label{introEq:Lp4} |
| 790 |
|
\end{align} |
| 791 |
< |
Where $\Delta t = t/P$. |
| 791 |
> |
where $\Delta t = t/P$. |
| 792 |
|
With this, a discrete time operator $G(\Delta t)$ can be defined: |
| 793 |
|
\begin{align} |
| 794 |
|
G(\Delta t) &= e^{iL_1\frac{\Delta t}{2}}\, e^{iL_2\Delta t}\, |
| 795 |
< |
e^{iL_1\frac{\Delta t}{2}} \notag \\% |
| 795 |
> |
e^{iL_1\frac{\Delta t}{2}}, \notag \\% |
| 796 |
|
% |
| 797 |
|
&= U_1 \biggl ( \frac{\Delta t}{2} \biggr )\, U_2 ( \Delta t )\, |
| 798 |
< |
U_1 \biggl ( \frac{\Delta t}{2} \biggr ) |
| 798 |
> |
U_1 \biggl ( \frac{\Delta t}{2} \biggr ). |
| 799 |
|
\label{introEq:Lp5} |
| 800 |
|
\end{align} |
| 801 |
|
Because $U_1(t)$ and $U_2(t)$ are unitary, $G(\Delta t)$ is also |
| 804 |
|
|
| 805 |
|
As an example, consider the following decomposition of $L$: |
| 806 |
|
\begin{align} |
| 807 |
< |
iL_1 &= \dot{q}\frac{\partial}{\partial q}% |
| 807 |
> |
iL_1 &= \dot{q}\frac{\partial}{\partial q},% |
| 808 |
|
\label{introEq:Lp6a} \\% |
| 809 |
|
% |
| 810 |
< |
iL_2 &= F(q)\frac{\partial}{\partial p}% |
| 810 |
> |
iL_2 &= F(q)\frac{\partial}{\partial p}.% |
| 811 |
|
\label{introEq:Lp6b} |
| 812 |
|
\end{align} |
| 813 |
|
This leads to propagator $G( \Delta t )$ as, |
| 814 |
|
\begin{equation} |
| 815 |
|
G(\Delta t) = e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}} \, |
| 816 |
|
e^{\Delta t\,\dot{q}\frac{\partial}{\partial q}} \, |
| 817 |
< |
e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}} |
| 817 |
> |
e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}}. |
| 818 |
|
\label{introEq:Lp7} |
| 819 |
|
\end{equation} |
| 820 |
|
Operating $G(\Delta t)$ on $\Gamma(0)$, and utilizing the operator property |
| 821 |
|
\begin{equation} |
| 822 |
< |
e^{c\frac{\partial}{\partial x}}\, f(x) = f(x+c) |
| 822 |
> |
e^{c\frac{\partial}{\partial x}}\, f(x) = f(x+c), |
| 823 |
|
\label{introEq:Lp8} |
| 824 |
|
\end{equation} |
| 825 |
< |
Where $c$ is independent of $x$. One obtains the following: |
| 825 |
> |
where $c$ is independent of $x$. One obtains the following: |
| 826 |
|
\begin{align} |
| 827 |
|
\dot{q}\biggl (\frac{\Delta t}{2}\biggr ) &= |
| 828 |
< |
\dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)] \label{introEq:Lp9a}\\% |
| 828 |
> |
\dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)], \label{introEq:Lp9a}\\% |
| 829 |
|
% |
| 830 |
< |
q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr )% |
| 830 |
> |
q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr ),% |
| 831 |
|
\label{introEq:Lp9b}\\% |
| 832 |
|
% |
| 833 |
|
\dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) + |
| 834 |
< |
\frac{\Delta t}{2m}\, F[q(0)] \label{introEq:Lp9c} |
| 834 |
> |
\frac{\Delta t}{2m}\, F[q(0)]. \label{introEq:Lp9c} |
| 835 |
|
\end{align} |
| 836 |
|
Or written another way, |
| 837 |
|
\begin{align} |
| 838 |
|
q(t+\Delta t) &= q(0) + \dot{q}(0)\Delta t + |
| 839 |
< |
\frac{F[q(0)]}{m}\frac{\Delta t^2}{2} % |
| 839 |
> |
\frac{F[q(0)]}{m}\frac{\Delta t^2}{2}, % |
| 840 |
|
\label{introEq:Lp10a} \\% |
| 841 |
|
% |
| 842 |
|
\dot{q}(\Delta t) &= \dot{q}(0) + \frac{\Delta t}{2m} |
| 843 |
< |
\biggl [F[q(0)] + F[q(\Delta t)] \biggr] % |
| 843 |
> |
\biggl [F[q(0)] + F[q(\Delta t)] \biggr]. % |
| 844 |
|
\label{introEq:Lp10b} |
| 845 |
|
\end{align} |
| 846 |
|
This is the velocity Verlet formulation presented in |
| 868 |
|
Liouville operator: |
| 869 |
|
\begin{align} |
| 870 |
|
iL_{\text{pos}} &= \dot{q}\frac{\partial}{\partial q} + |
| 871 |
< |
\mathsf{\dot{A}}\frac{\partial}{\partial \mathsf{A}} |
| 871 |
> |
\mathsf{\dot{A}}\frac{\partial}{\partial \mathsf{A}} , |
| 872 |
|
\label{introEq:SR1a} \\% |
| 873 |
|
% |
| 874 |
< |
iL_F &= F(q)\frac{\partial}{\partial p} |
| 874 |
> |
iL_F &= F(q)\frac{\partial}{\partial p}, |
| 875 |
|
\label{introEq:SR1b} \\% |
| 876 |
< |
iL_{\tau} &= \tau(\mathsf{A})\frac{\partial}{\partial j} |
| 876 |
> |
iL_{\tau} &= \tau(\mathsf{A})\frac{\partial}{\partial j}, |
| 877 |
|
\label{introEq:SR1b} \\% |
| 878 |
|
\end{align} |
| 879 |
< |
Where $\tau(\mathsf{A})$ is the torque of the system |
| 879 |
> |
where $\tau(\mathsf{A})$ is the torque of the system |
| 880 |
|
due to the configuration, and $j$ is the conjugate |
| 881 |
|
angular momenta of the system. The propagator, $G(\Delta t)$, becomes |
| 882 |
|
\begin{equation} |
| 884 |
|
e^{\frac{\Delta t}{2} \tau(\mathsf{A})\frac{\partial}{\partial j}} \, |
| 885 |
|
e^{\Delta t\,iL_{\text{pos}}} \, |
| 886 |
|
e^{\frac{\Delta t}{2} \tau(\mathsf{A})\frac{\partial}{\partial j}} \, |
| 887 |
< |
e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}} |
| 887 |
> |
e^{\frac{\Delta t}{2} F(q)\frac{\partial}{\partial p}}. |
| 888 |
|
\label{introEq:SR2} |
| 889 |
|
\end{equation} |
| 890 |
|
Propagation of the linear and angular momenta follows as in the Verlet |
| 898 |
|
\mathcal{U}_y \biggl(\frac{\Delta t}{2}\biggr)\, |
| 899 |
|
\mathcal{U}_z (\Delta t)\, |
| 900 |
|
\mathcal{U}_y \biggl(\frac{\Delta t}{2}\biggr)\, |
| 901 |
< |
\mathcal{U}_x \biggl(\frac{\Delta t}{2}\biggr)\, |
| 901 |
> |
\mathcal{U}_x \biggl(\frac{\Delta t}{2}\biggr), |
| 902 |
|
\label{introEq:SR3} |
| 903 |
|
\end{equation} |
| 904 |
< |
Where $\mathcal{U}_{\alpha}$ is a unitary rotation of $\mathsf{A}$ and |
| 904 |
> |
where $\mathcal{U}_{\alpha}$ is a unitary rotation of $\mathsf{A}$ and |
| 905 |
|
$j$ about each axis $\alpha$. As all propagations are now |
| 906 |
|
unitary and symplectic, the entire integration scheme is also |
| 907 |
|
symplectic and time reversible. |
| 921 |
|
|
| 922 |
|
The chapter concerning random sequential adsorption simulations is a |
| 923 |
|
study in applying Statistical Mechanics simulation techniques in order |
| 924 |
< |
to obtain a simple model capable of explaining the results. My |
| 924 |
> |
to obtain a simple model capable of explaining experimental observations. My |
| 925 |
|
advisor, Dr. Gezelter, and I were approached by a colleague, |
| 926 |
|
Dr. Lieberman, about possible explanations for the partial coverage of |
| 927 |
|
a gold surface by a particular compound synthesized in her group. We |
| 961 |
|
In the last chapter, I discuss future directions |
| 962 |
|
for both {\sc oopse} and this mesoscale model. Additionally, I will |
| 963 |
|
give a summary of the results found in this dissertation. |
| 965 |
– |
|
| 966 |
– |
|
