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# Line 993 | Line 993 | Schemes}
993  
994   \subsection{\label{introSection:Analysis} Analysis}
995  
996 + Recently, advanced visualization technique are widely applied to
997 + monitor the motions of molecules. Although the dynamics of the
998 + system can be described qualitatively from animation, quantitative
999 + trajectory analysis are more appreciable. According to the
1000 + principles of Statistical Mechanics,
1001 + Sec.~\ref{introSection:statisticalMechanics}, one can compute
1002 + thermodynamics properties, analyze fluctuations of structural
1003 + parameters, and investigate time-dependent processes of the molecule
1004 + from the trajectories.
1005 +
1006 + \subsubsection{\label{introSection:thermodynamicsProperties}Thermodynamics Properties}
1007 +
1008 + \subsubsection{\label{introSection:structuralProperties}Structural Properties}
1009 +
1010 + Structural Properties of a simple fluid can be described by a set of
1011 + distribution functions. Among these functions,\emph{pair
1012 + distribution function}, also known as \emph{radial distribution
1013 + function}, are of most fundamental importance to liquid-state
1014 + theory. Pair distribution function can be gathered by Fourier
1015 + transforming raw data from a series of neutron diffraction
1016 + experiments and integrating over the surface factor \cite{Powles73}.
1017 + The experiment result can serve as a criterion to justify the
1018 + correctness of the theory. Moreover, various equilibrium
1019 + thermodynamic and structural properties can also be expressed in
1020 + terms of radial distribution function \cite{allen87:csl}.
1021 +
1022 + A pair distribution functions $g(r)$ gives the probability that a
1023 + particle $i$ will be located at a distance $r$ from a another
1024 + particle $j$ in the system
1025 + \[
1026 + g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j
1027 + \ne i} {\delta (r - r_{ij} )} } } \right\rangle.
1028 + \]
1029 + Note that the delta function can be replaced by a histogram in
1030 + computer simulation. Figure
1031 + \ref{introFigure:pairDistributionFunction} shows a typical pair
1032 + distribution function for the liquid argon system. The occurrence of
1033 + several peaks in the plot of $g(r)$ suggests that it is more likely
1034 + to find particles at certain radial values than at others. This is a
1035 + result of the attractive interaction at such distances. Because of
1036 + the strong repulsive forces at short distance, the probability of
1037 + locating particles at distances less than about 2.5{\AA} from each
1038 + other is essentially zero.
1039 +
1040 + %\begin{figure}
1041 + %\centering
1042 + %\includegraphics[width=\linewidth]{pdf.eps}
1043 + %\caption[Pair distribution function for the liquid argon
1044 + %]{Pair distribution function for the liquid argon}
1045 + %\label{introFigure:pairDistributionFunction}
1046 + %\end{figure}
1047 +
1048 + \subsubsection{\label{introSection:timeDependentProperties}Time-dependent
1049 + Properties}
1050 +
1051 + Time-dependent properties are usually calculated using \emph{time
1052 + correlation function}, which correlates random variables $A$ and $B$
1053 + at two different time
1054 + \begin{equation}
1055 + C_{AB} (t) = \left\langle {A(t)B(0)} \right\rangle.
1056 + \label{introEquation:timeCorrelationFunction}
1057 + \end{equation}
1058 + If $A$ and $B$ refer to same variable, this kind of correlation
1059 + function is called \emph{auto correlation function}. One example of
1060 + auto correlation function is velocity auto-correlation function
1061 + which is directly related to transport properties of molecular
1062 + liquids. Another example is the calculation of the IR spectrum
1063 + through a Fourier transform of the dipole autocorrelation function.
1064 +
1065   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
1066  
1067   Rigid bodies are frequently involved in the modeling of different
# Line 1854 | Line 1923 | joining center of resistance $R$ and origin $O$.
1923   \]
1924   where $x_OR$, $y_OR$, $z_OR$ are the components of the vector
1925   joining center of resistance $R$ and origin $O$.
1857
1858 %\section{\label{introSection:correlationFunctions}Correlation Functions}

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