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Revision 903 by tim, Wed Jan 7 03:53:53 2004 UTC vs.
Revision 968 by tim, Tue Jan 20 16:49:22 2004 UTC

# Line 8 | Line 8
8   %TCIDATA{Version=5.00.0.2552}
9   %TCIDATA{CSTFile=40 LaTeX article.cst}
10   %TCIDATA{Created=Friday, September 19, 2003 08:29:53}
11 < %TCIDATA{LastRevised=Tuesday, January 06, 2004 17:35:24}
11 > %TCIDATA{LastRevised=Tuesday, January 20, 2004 11:37:59}
12   %TCIDATA{<META NAME="GraphicsSave" CONTENT="32">}
13   %TCIDATA{<META NAME="SaveForMode" CONTENT="1">}
14   %TCIDATA{<META NAME="DocumentShell" CONTENT="Standard LaTeX\Standard LaTeX Article">}
# Line 40 | Line 40
40   \begin{document}
41   \section{\label{Sec:pbc}Periodic Boundary Conditions}
42  
43 < \textit{Periodic boundary conditions} are widely used  to simulate
44 < truly macroscopic systems with a relatively small number of
45 < particles. Simulation box is replicated throughout space to form
46 < an infinite lattice. During the simulation, when a particle moves
47 < in the primary cell, its periodic image particles in other boxes
48 < move in exactly the same direction with exactly the same
49 < orientation.Thus, as a particle leaves the primary cell, one of
50 < its images will enter through the opposite face.If the simulation
51 < box is large enough to avoid "feeling" the symmetric of the
52 < periodic lattice,the surface effect could be ignored. For the time
53 < being, Cubic and parallelepiped are the available periodic cells
54 < used in OOPSE.
55 < \bigskip In OOPSE, we use the matrix instead of vector to describe the property
56 < of the simulation box. Therefore, not only the size of the
57 < simulation box could be changed during the simulation, but also
58 < the shape of it.
59 < The transformation from box space vector
60 < $\overrightarrow{s}$to its corresponding real space vector
61 < $\overrightarrow{r}$ is defined by
43 > \textit{Periodic boundary conditions} are widely used to simulate truly
44 > macroscopic systems with a relatively small number of particles. The
45 > simulation box is replicated throughout space to form an infinite lattice.
46 > During the simulation, when a particle moves in the primary cell, its image in
47 > other boxes move in exactly the same direction with exactly the same
48 > orientation.Thus, as a particle leaves the primary cell, one of its images
49 > will enter through the opposite face.If the simulation box is large enough to
50 > avoid \textquotedblleft feeling\textquotedblright\ the symmetries of the
51 > periodic lattice, surface effects can be ignored. Cubic, orthorhombic and
52 > parallelepiped are the available periodic cells In OOPSE. We use a matrix to
53 > describe the property of the simulation box. Therefore, both the size and
54 > shape of the simulation box can be changed during the simulation. The
55 > transformation from box space vector $\mathbf{s}$ to its corresponding real
56 > space vector $\mathbf{r}$ is defined by
57   \begin{equation}
58 < \overrightarrow{r}=H\overrightarrow{s}%
58 > \mathbf{r}=\underline{\mathbf{H}}\cdot\mathbf{s}%
59   \end{equation}
60  
66 where $H=(h_{x},h_{y},h_{z})$ is a transformation matrix made up
67 of the three box axis vector. h_{x},h_{y} and h_{z} represent the
68 sides of the simulation box respectively. Thus H matrix becomes
61  
62 + where $H=(h_{x},h_{y},h_{z})$ is a transformation matrix made up of the three
63 + box axis vectors. $h_{x},h_{y}$ and $h_{z}$ represent the three sides of the
64 + simulation box respectively.
65  
66 < To find the minimum image, we need to convert the real vector to
67 < its corresponding vector in box space first,
73 < \bigskip
66 > To find the minimum image of a vector $\mathbf{r}$, we convert the real vector
67 > to its corresponding vector in box space first, \bigskip%
68   \begin{equation}
69 < \overrightarrow{s}=H^{-1}\overrightarrow{r}%
69 > \mathbf{s}=\underline{\mathbf{H}}^{-1}\cdot\mathbf{r}%
70   \end{equation}
71 < And then, each element of \overrightarrow{s} is casted to lie
78 < between -0.5 to 0.5
71 > And then, each element of $\mathbf{s}$ is wrapped to lie between -0.5 to 0.5,
72   \begin{equation}
73 < \s_{i}=s_{i}-round(s_{i})%
73 > s_{i}^{\prime}=s_{i}-round(s_{i})
74   \end{equation}
75   where
83 \begin{equation}
84 \round(x)=(x+0.5)(x-0.5)%
85 \end{equation}
76  
77 < For example, round(3.6)=4, round(3.1) = 3, round(-3.6) = -4,
88 < round(-3.1)=-3.
77 > %
78  
79   \begin{equation}
80 < \overrightarrow{s}=H^{-1}\overrightarrow{r}%
80 > round(x)=\left\{
81 > \begin{array}
82 > [c]{c}%
83 > \lfloor{x+0.5}\rfloor & \text{if \ }x\geqslant0\\
84 > \lceil{x-0.5}\rceil & \text{otherwise}%
85 > \end{array}
86 > \right.
87   \end{equation}
88  
89  
90 + For example, $round(3.6)=4$,$round(3.1)=3$, $round(-3.6)=-4$, $round(-3.1)=-3$.
91  
92 < \end{document}
92 > Finally, we obtain the minimum image coordinates $\mathbf{r}^{\prime}$ by
93 > transforming back to real space,%
94 >
95 > \begin{equation}
96 > \mathbf{r}^{\prime}=\underline{\mathbf{H}}^{-1}\cdot\mathbf{s}^{\prime}%
97 > \end{equation}
98 >
99 > \end{document}

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