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# Line 9 | Line 9 | associated with them, often in the form of a dipole. C
9   element, or be used for collections of atoms such as a methyl
10   group. The atoms are also capable of having a directional component
11   associated with them, often in the form of a dipole. Charges on atoms
12 < are not currently suporrted by {\sc oopse}.
12 > are not currently suported by {\sc oopse}.
13  
14   The second most basic building block of a simulation is the
15 < molecule. The molecule is a way for {\sc oopse} to keep track of the atoms
16 < in a simulation in logical manner. This particular unit will store the
17 < identities of all the atoms associated with itself and is responsible
18 < for the evaluation of its own bonded interaction (i.e.~bonds, bends,
19 < and torsions).
15 > molecule. The molecule is a way for {\sc oopse} to keep track of the
16 > atoms in a simulation in logical manner. This particular unit will
17 > store the identities of all the atoms associated with itself and is
18 > responsible for the evaluation of its own bonded interaction
19 > (i.e.~bonds, bends, and torsions).
20  
21 < As stated in the previously, one of the features that sets OOPSE apart
21 > As stated previously, one of the features that sets {\sc oopse} apart
22   from most of the current molecular simulation packages is the ability
23   to handle rigid body dynamics. Rigid bodies are non-spherical
24   particles or collections of particles that have a constant internal
25   potential and move collectively.\cite{Goldstein01} They are not
26 < included in many standard simulation packages because of the need to
27 < consider orientational degrees of freedom and include an integrator
28 < that accurately propagates these motions in time.
26 > included in most simulation packages because of the requirement to
27 > propagate the orientational degrees of freedom. Until recently,
28 > integrators which propagate orientational motion have been lacking.
29  
30   Moving a rigid body involves determination of both the force and
31   torque applied by the surroundings, which directly affect the
32 < translation and rotation in turn. In order to accumulate the total
33 < force on a rigid body, the external forces must first be calculated
34 < for all the interal particles. The total force on the rigid body is
35 < simply the sum of these external forces.  Accumulation of the total
36 < torque on the rigid body is similar to the force in that it is a sum
37 < of the torque applied on each internal particle, mapped onto the
38 < center of mass of the rigid body.
32 > translational and rotational motion in turn. In order to accumulate
33 > the total force on a rigid body, the external forces and torques must
34 > first be calculated for all the internal particles. The total force on
35 > the rigid body is simply the sum of these external forces.
36 > Accumulation of the total torque on the rigid body is more complex
37 > than the force in that it is the torque applied on the center of mass
38 > that dictates rotational motion. The torque on rigid body {\it i} is
39 > \begin{equation}
40 > \boldsymbol{\tau}_i=
41 >        \sum_{a}(\mathbf{r}_{ia}-\mathbf{r}_i)\times \mathbf{f}_{ia}
42 >        + \boldsymbol{\tau}_{ia},
43 > \label{eq:torqueAccumulate}
44 > \end{equation}
45 > where $\boldsymbol{\tau}_i$ and $\mathbf{r}_i$ are the torque on and
46 > position of the center of mass respectively, while $\mathbf{f}_{ia}$,
47 > $\mathbf{r}_{ia}$, and $\boldsymbol{\tau}_{ia}$ are the force on,
48 > position of, and torque on the component particles of the rigid body.
49  
50 < The application of the total torque is done in the body fixed axis of
50 > The summation of the total torque is done in the body fixed axis of
51   the rigid body. In order to move between the space fixed and body
52 < fixed coordinate axes, parameters describing the orientation be
53 < maintained for each rigid body. At a minimum, the rotation matrix can
54 < be described and propagated by the three Euler
55 < angles.\cite{Goldstein01} In order to avoid rotational limitations
56 < when using Euler angles, the four parameter ``quaternion'' scheme can
57 < be used instead.\cite{allen87:csl} Use of quaternions also leads to
52 > fixed coordinate axes, parameters describing the orientation must be
53 > maintained for each rigid body. At a minimum, the rotation matrix
54 > (\textbf{A}) can be described by the three Euler angles ($\phi,
55 > \theta,$ and $\psi$), where the elements of \textbf{A} are composed of
56 > trigonometric operations involving $\phi, \theta,$ and
57 > $\psi$.\cite{Goldstein01} In order to avoid numerical instabilities
58 > inherent in using the Euler angles, the four parameter ``quaternion''
59 > scheme is often used. The elements of \textbf{A} can be expressed as
60 > arithmetic operations involving the four quaternions ($q_0, q_1, q_2,$
61 > and $q_3$).\cite{allen87:csl} Use of quaternions also leads to
62   performance enhancements, particularly for very small
63 < systems.\cite{Evans77} OOPSE utilizes a relatively new scheme that
50 < propagates the entire nine parameter rotation matrix. Further
51 < discussion on this choice can be found in Sec.~\ref{sec:integrate}.
63 > systems.\cite{Evans77}
64  
65 + {\sc oopse} utilizes a relatively new scheme that propagates the
66 + entire nine parameter rotation matrix internally. Further discussion
67 + on this choice can be found in Sec.~\ref{sec:integrate}.
68 +
69   \subsection{\label{sec:LJPot}The Lennard Jones Potential}
70  
71   The most basic force field implemented in OOPSE is the Lennard-Jones
72 < potential. The Lennard-Jones potential mimics the attractive forces
73 < two charge neutral particles experience when spontaneous dipoles are
74 < induced on each other. This is the predominant intermolecular force in
59 < systems of such as noble gases and simple alkanes.
60 <
61 < The Lennard-Jones potential is given by:
72 > potential. The Lennard-Jones potential. Which mimics the Van der Waals
73 > interaction at long distances, and uses an emperical repulsion at
74 > short distances. The Lennard-Jones potential is given by:
75   \begin{equation}
76   V_{\text{LJ}}(r_{ij}) =
77          4\epsilon_{ij} \biggl[
# Line 67 | Line 80 | V_{\text{LJ}}(r_{ij}) =
80          \biggr]
81   \label{eq:lennardJonesPot}
82   \end{equation}
83 < Where $r_ij$ is the distance between particle $i$ and $j$, $\sigma_{ij}$
84 < scales the length of the interaction, and $\epsilon_{ij}$ scales the
85 < energy well depth of the potential.
83 > Where $r_{ij}$ is the distance between particle $i$ and $j$,
84 > $\sigma_{ij}$ scales the length of the interaction, and
85 > $\epsilon_{ij}$ scales the well depth of the potential.
86  
87 < Because the potential is calculated between all pairs, the force
87 > Because this potential is calculated between all pairs, the force
88   evaluation can become computationally expensive for large systems. To
89 < keep the pair evaluation to a manegable number, OOPSE employs the use
90 < of a cut-off radius.\cite{allen87:csl} The cutoff radius is set to be
91 < $2.5\sigma_{ii}$, where $\sigma_{ii}$ is the largest self self length
89 > keep the pair evaluation to a manegable number, OOPSE employs a
90 > cut-off radius.\cite{allen87:csl} The cutoff radius is set to be
91 > $2.5\sigma_{ii}$, where $\sigma_{ii}$ is the largest Lennard-Jones length
92   parameter in the system. Truncating the calculation at
93   $r_{\text{cut}}$ introduces a discontinuity into the potential
94   energy. To offset this discontinuity, the energy value at
95   $r_{\text{cut}}$ is subtracted from the entire potential. This causes
96 < the equation to go to zero at the cut-off radius.
96 > the potential to go to zero at the cut-off radius.
97  
98   Interactions between dissimilar particles requires the generation of
99   cross term parameters for $\sigma$ and $\epsilon$. These are
# Line 99 | Line 112 | and
112  
113   \subsection{\label{sec:DUFF}Dipolar Unified-Atom Force Field}
114  
115 < The \underline{D}ipolar \underline{U}nified-Atom
116 < \underline{F}orce \underline{F}ield ({\sc duff}) was developed to
104 < simulate lipid bilayers. We needed a model capable of forming
115 > The Dipolar Unified-atom Force Field ({\sc duff}) was developed to
116 > simulate lipid bilayers. The systems require a model capable of forming
117   bilayers, while still being sufficiently computationally efficient to
118   allow simulations of large systems ($\approx$100's of phospholipids,
119   $\approx$1000's of waters) for long times ($\approx$10's of
120   nanoseconds).
121  
122 < With this goal in mind, we have eliminated all point charges. Charge
123 < distributions were replaced with dipoles, and charge-neutral
124 < distributions were reduced to Lennard-Jones interaction sites. This
122 > With this goal in mind, {\sc duff} has no point charges. Charge
123 > neutral distributions were replaced with dipoles, while most atoms and
124 > groups of atoms were reduced to Lennard-Jones interaction sites. This
125   simplification cuts the length scale of long range interactions from
126   $\frac{1}{r}$ to $\frac{1}{r^3}$, allowing us to avoid the
127 < computationally expensive Ewald-Sum. Instead, we can use
128 < neighbor-lists and cutoff radii for the dipolar interactions.
127 > computationally expensive Ewald sum. Instead, we can use
128 > neighbor-lists, reaction field, and cutoff radii for the dipolar
129 > interactions.
130  
131 < As an example, lipid head groups in {\sc duff} are represented as point
132 < dipole interaction sites.PC and PE Lipid head groups are typically
133 < zwitterionic in nature, with charges separated by as much as
134 < 6~$\mbox{\AA}$. By placing a dipole of 20.6~Debye at the head group
135 < center of mass, our model mimics the head group of PC.\cite{Cevc87}
136 < Additionally, a Lennard-Jones site is located at the
137 < pseudoatom's center of mass. The model is illustrated by the dark grey
138 < atom in Fig.~\ref{fig:lipidModel}.
131 > As an example, lipid head-groups in {\sc duff} are represented as
132 > point dipole interaction sites. By placing a dipole of 20.6~Debye at
133 > the head group center of mass, our model mimics the head group of
134 > phosphatidylcholine.\cite{Cevc87} Additionally, a Lennard-Jones site
135 > is located at the pseudoatom's center of mass. The model is
136 > illustrated by the dark grey atom in Fig.~\ref{fig:lipidModel}. The water model we use to complement the dipoles of the lipids is out
137 > repaarameterization of the soft sticky dipole (SSD) model of Ichiye
138 > \emph{et al.}\cite{liu96:new_model}
139  
140   \begin{figure}
141 + \epsfxsize=\linewidth
142   \epsfbox{lipidModel.eps}
143   \caption{A representation of the lipid model. $\phi$ is the torsion angle, $\theta$ %
144 < is the bend angle, $\mu$ is the dipole moment of the head group, and n is the chain length.}
144 > is the bend angle, $\mu$ is the dipole moment of the head group, and n
145 > is the chain length.}
146   \label{fig:lipidModel}
147   \end{figure}
133
134 The water model we use to complement the dipoles of the lipids is
135 the soft sticky dipole (SSD) model of Ichiye \emph{et
136 al.}\cite{liu96:new_model} This model is discussed in greater detail
137 in Sec.~\ref{sec:SSD}. In all cases we reduce water to a single
138 Lennard-Jones interaction site. The site also contains a dipole to
139 mimic the partial charges on the hydrogens and the oxygen. However,
140 what makes the SSD model unique is the inclusion of a tetrahedral
141 short range potential to recover the hydrogen bonding of water, an
142 important factor when modeling bilayers, as it has been shown that
143 hydrogen bond network formation is a leading contribution to the
144 entropic driving force towards lipid bilayer formation.\cite{Cevc87}
148  
146
149   Turning to the tails of the phospholipids, we have used a set of
150   scalable parameters to model the alkyl groups with Lennard-Jones
151   sites. For this, we have used the TraPPE force field of Siepmann
# Line 169 | Line 171 | V_{\text{Total}} = \sum^{N}_{I=1} V^{I}_{\text{Interna
171   The total energy of function in {\sc duff} is given by the following:
172   \begin{equation}
173   V_{\text{Total}} = \sum^{N}_{I=1} V^{I}_{\text{Internal}}
174 <        + \sum^{N}_{I=1} \sum^{N}_{J=1} V^{IJ}_{\text{Cross}}
174 >        + \sum^{N}_{I=1} \sum_{J>I} V^{IJ}_{\text{Cross}}
175   \label{eq:totalPotential}
176   \end{equation}
177   Where $V^{I}_{\text{Internal}}$ is the internal potential of a molecule:
178   \begin{equation}
179   V^{I}_{\text{Internal}} =
180          \sum_{\theta_{ijk} \in I} V_{\text{bend}}(\theta_{ijk})
181 <        + \sum_{\phi_{ijkl} \in I} V_{\text{torsion}}(\theta_{ijkl})
181 >        + \sum_{\phi_{ijkl} \in I} V_{\text{torsion}}(\phi_{ijkl})
182          + \sum_{i \in I} \sum_{(j>i+4) \in I}
183          \biggl[ V_{\text{LJ}}(r_{ij}) +  V_{\text{dipole}}
184          (\mathbf{r}_{ij},\boldsymbol{\Omega}_{i},\boldsymbol{\Omega}_{j})
# Line 184 | Line 186 | Here $V_{\text{bend}}$ is the bend potential for all 1
186   \label{eq:internalPotential}
187   \end{equation}
188   Here $V_{\text{bend}}$ is the bend potential for all 1, 3 bonded pairs
189 < within in the molecule. $V_{\text{torsion}}$ is the torsion The
190 < pairwise portions of the internal potential are excluded for pairs
191 < that ar closer than three bonds, i.e.~atom pairs farther away than a
192 < torsion are included in the pair-wise loop.
189 > within the molecule, and $V_{\text{torsion}}$ is the torsion potential
190 > for all 1, 4 bonded pairs. The pairwise portions of the internal
191 > potential are excluded for pairs that are closer than three bonds,
192 > i.e.~atom pairs farther away than a torsion are included in the
193 > pair-wise loop.
194  
192 The cross portion of the total potential, $V^{IJ}_{\text{Cross}}$, is
193 as follows:
194 \begin{equation}
195 V^{IJ}_{\text{Cross}} =
196        \sum_{i \in I} \sum_{j \in J}
197        \biggl[ V_{\text{LJ}}(r_{ij}) +  V_{\text{dipole}}
198        (\mathbf{r}_{ij},\boldsymbol{\Omega}_{i},\boldsymbol{\Omega}_{j})
199        + V_{\text{sticky}}
200        (\mathbf{r}_{ij},\boldsymbol{\Omega}_{i},\boldsymbol{\Omega}_{j})
201        \biggr]
202 \label{eq:crossPotentail}
203 \end{equation}
204 Where $V_{\text{LJ}}$ is the Lennard Jones potential,
205 $V_{\text{dipole}}$ is the dipole dipole potential, and
206 $V_{\text{sticky}}$ is the sticky potential defined by the SSD model.
195  
196   The bend potential of a molecule is represented by the following function:
197   \begin{equation}
# Line 218 | Line 206 | of the form:
206   The torsion potential and parameters are also taken from TraPPE. It is
207   of the form:
208   \begin{equation}
209 < V_{\text{torsion}}(\phi_{ijkl}) = c_1[1 + \cos \phi]
209 > V_{\text{torsion}}(\phi) = c_1[1 + \cos \phi]
210          + c_2[1 + \cos(2\phi)]
211          + c_3[1 + \cos(3\phi)]
212   \label{eq:origTorsionPot}
213   \end{equation}
214   Here $\phi_{ijkl}$ is the angle defined by four bonded neighbors $i$,
215 < $j$, $k$, and $l$ (again, see Fig.~\ref{fig:lipidModel}).  However,
216 < for computaional efficency, the torsion potentail has been recast
217 < after the method of CHARMM\cite{charmm1983} whereby the angle series
218 < is converted to a power series of the form:
215 > $j$, $k$, and $l$ (again, see Fig.~\ref{fig:lipidModel}). For
216 > computaional efficency, the torsion potential has been recast after
217 > the method of CHARMM\cite{charmm1983} whereby the angle series is
218 > converted to a power series of the form:
219   \begin{equation}
220   V_{\text{torsion}}(\phi_{ijkl}) =  
221          k_3 \cos^3 \phi + k_2 \cos^2 \phi + k_1 \cos \phi + k_0
# Line 244 | Line 232 | evaluations are avoided during the calculation of the
232   evaluations are avoided during the calculation of the potential.
233  
234  
235 + The cross portion of the total potential, $V^{IJ}_{\text{Cross}}$, is
236 + as follows:
237 + \begin{equation}
238 + V^{IJ}_{\text{Cross}} =
239 +        \sum_{i \in I} \sum_{j \in J}
240 +        \biggl[ V_{\text{LJ}}(r_{ij}) +  V_{\text{dipole}}
241 +        (\mathbf{r}_{ij},\boldsymbol{\Omega}_{i},\boldsymbol{\Omega}_{j})
242 +        + V_{\text{sticky}}
243 +        (\mathbf{r}_{ij},\boldsymbol{\Omega}_{i},\boldsymbol{\Omega}_{j})
244 +        \biggr]
245 + \label{eq:crossPotentail}
246 + \end{equation}
247 + Where $V_{\text{LJ}}$ is the Lennard Jones potential,
248 + $V_{\text{dipole}}$ is the dipole dipole potential, and
249 + $V_{\text{sticky}}$ is the sticky potential defined by the SSD
250 + model. Note that not all atom types include all interactions.
251  
252   The dipole-dipole potential has the following form:
253   \begin{equation}
254   V_{\text{dipole}}(\mathbf{r}_{ij},\boldsymbol{\Omega}_{i},
255 <        \boldsymbol{\Omega}_{j}) = \frac{1}{4\pi\epsilon_{0}} \biggl[
256 <        \frac{\boldsymbol{\mu}_{i} \cdot \boldsymbol{\mu}_{j}}{r^{3}_{ij}}
255 >        \boldsymbol{\Omega}_{j}) = \frac{|\mu_i||\mu_j|}{4\pi\epsilon_{0}r_{ij}^{3}} \biggl[
256 >        \boldsymbol{\hat{u}}_{i} \cdot \boldsymbol{\hat{u}}_{j}
257          -
258 <        \frac{3(\boldsymbol{\mu}_i \cdot \mathbf{r}_{ij}) %
259 <                (\boldsymbol{\mu}_j \cdot \mathbf{r}_{ij}) }
260 <                {r^{5}_{ij}} \biggr]
258 >        \frac{3(\boldsymbol{\hat{u}}_i \cdot \mathbf{r}_{ij}) %
259 >                (\boldsymbol{\hat{u}}_j \cdot \mathbf{r}_{ij}) }
260 >                {r^{2}_{ij}} \biggr]
261   \label{eq:dipolePot}
262   \end{equation}
263   Here $\mathbf{r}_{ij}$ is the vector starting at atom $i$ pointing
264   towards $j$, and $\boldsymbol{\Omega}_i$ and $\boldsymbol{\Omega}_j$
265 < are the Euler angles of atom $i$ and $j$
266 < respectively. $\boldsymbol{\mu}_i$ is the dipole vector of atom
267 < $i$ it takes its orientation from $\boldsymbol{\Omega}_i$.
265 > are the Euler angles of atom $i$ and $j$ respectively. $|\mu_i|$ is
266 > the magnitude of the dipole moment of atom $i$ and
267 > $\boldsymbol{\hat{u}}_i$ is the standard unit orientation vector of
268 > $\boldsymbol{\Omega}_i$.
269  
270  
271 < \subsection{\label{sec:SSD}Water Model: SSD and Derivatives}
271 > \subsection{\label{sec:SSD}The {\sc DUFF} Water Models: SSD/E and SSD/RF}
272  
273 < In the interest of computational efficiency, the native solvent used
273 > In the interest of computational efficiency, the default solvent used
274   in {\sc oopse} is the Soft Sticky Dipole (SSD) water model. SSD was
275   developed by Ichiye \emph{et al.} as a modified form of the
276   hard-sphere water model proposed by Bratko, Blum, and
# Line 275 | Line 280 | solvation shell. Thus, the interaction between two SSD
280   solvation shell. Thus, the interaction between two SSD water molecules
281   \emph{i} and \emph{j} is given by the potential
282   \begin{equation}
283 < u_{ij} = u_{ij}^{LJ} (r_{ij})\ + u_{ij}^{dp}
284 < (\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)\ +
285 < u_{ij}^{sp}
286 < (\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j),
283 > V_{ij} =
284 >        V_{ij}^{LJ} (r_{ij})\ + V_{ij}^{dp}
285 >        (\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)\ +
286 >        V_{ij}^{sp}
287 >        (\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j),
288 > \label{eq:ssdPot}
289   \end{equation}
290   where the $\mathbf{r}_{ij}$ is the position vector between molecules
291 < \emph{i} and \emph{j} with magnitude equal to the distance $r_ij$, and
291 > \emph{i} and \emph{j} with magnitude equal to the distance $r_{ij}$, and
292   $\boldsymbol{\Omega}_i$ and $\boldsymbol{\Omega}_j$ represent the
293 < orientations of the respective molecules. The Lennard-Jones, dipole,
294 < and sticky parts of the potential are giving by the following
295 < equations,
293 > orientations of the respective molecules. The Lennard-Jones and dipole
294 > parts of the potential are given by equations \ref{eq:lennardJonesPot}
295 > and \ref{eq:dipolePot} respectively. The sticky part is described by
296 > the following,
297   \begin{equation}
298 < u_{ij}^{LJ}(r_{ij}) = 4\epsilon \left[\left(\frac{\sigma}{r_{ij}}\right)^{12}-\left(\frac{\sigma}{r_{ij}}\right)^{6}\right],
298 > u_{ij}^{sp}(\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)=
299 >        \frac{\nu_0}{2}[s(r_{ij})w(\mathbf{r}_{ij},
300 >        \boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j) +
301 >        s^\prime(r_{ij})w^\prime(\mathbf{r}_{ij},
302 >        \boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)]\ ,
303 > \label{eq:stickyPot}
304   \end{equation}
305 < \begin{equation}
306 < u_{ij}^{dp} = \frac{\boldsymbol{\mu}_i\cdot\boldsymbol{\mu}_j}{r_{ij}^3}-\frac{3(\boldsymbol{\mu}_i\cdot\mathbf{r}_{ij})(\boldsymbol{\mu}_j\cdot\mathbf{r}_{ij})}{r_{ij}^5}\ ,
307 < \end{equation}
305 > where $\nu_0$ is a strength parameter for the sticky potential, and
306 > $s$ and $s^\prime$ are cubic switching functions which turn off the
307 > sticky interaction beyond the first solvation shell. The $w$ function
308 > can be thought of as an attractive potential with tetrahedral
309 > geometry:
310   \begin{equation}
311 < \begin{split}
312 < u_{ij}^{sp}
313 < (\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)
299 < &=
300 < \frac{\nu_0}{2}[s(r_{ij})w(\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)\\
301 < & \quad \ +
302 < s^\prime(r_{ij})w^\prime(\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)]\ ,
303 < \end{split}
311 > w({\bf r}_{ij},{\bf \Omega}_i,{\bf \Omega}_j)=
312 >        \sin\theta_{ij}\sin2\theta_{ij}\cos2\phi_{ij},
313 > \label{eq:stickyW}
314   \end{equation}
315 < where $\boldsymbol{\mu}_i$ and $\boldsymbol{\mu}_j$ are the dipole
316 < unit vectors of particles \emph{i} and \emph{j} with magnitude 2.35 D,
307 < $\nu_0$ scales the strength of the overall sticky potential, $s$ and
308 < $s^\prime$ are cubic switching functions. The $w$ and $w^\prime$
309 < functions take the following forms,
315 > while the $w^\prime$ function counters the normal aligned and
316 > anti-aligned structures favored by point dipoles:
317   \begin{equation}
318 < w(\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j)=\sin\theta_{ij}\sin2\theta_{ij}\cos2\phi_{ij},
318 > w^\prime({\bf r}_{ij},{\bf \Omega}_i,{\bf \Omega}_j)=
319 >        (\cos\theta_{ij}-0.6)^2(\cos\theta_{ij}+0.8)^2-w^0,
320 > \label{eq:stickyWprime}
321   \end{equation}
322 < \begin{equation}
323 < w^\prime(\mathbf{r}_{ij},\boldsymbol{\Omega}_i,\boldsymbol{\Omega}_j) = (\cos\theta_{ij}-0.6)^2(\cos\theta_{ij}+0.8)^2-w^0,
324 < \end{equation}
325 < where $w^0 = 0.07715$. The $w$ function is the tetrahedral attractive
326 < term that promotes hydrogen bonding orientations within the first
327 < solvation shell, and $w^\prime$ is a dipolar repulsion term that
328 < repels unrealistic dipolar arrangements within the first solvation
320 < shell. A more detailed description of the functional parts and
321 < variables in this potential can be found in other
322 < articles.\cite{liu96:new_model,chandra99:ssd_md}
322 > It should be noted that $w$ is proportional to the sum of the $Y_3^2$
323 > and $Y_3^{-2}$ spherical harmonics (a linear combination which
324 > enhances the tetrahedral geometry for hydrogen bonded structures),
325 > while $w^\prime$ is a purely empirical function.  A more detailed
326 > description of the functional parts and variables in this potential
327 > can be found in the original SSD
328 > articles.\cite{Ichiye96,Ichiye96b,Ichiye99,Ichiye03}
329  
330 < Since SSD is a one-site point dipole model, the force calculations are
331 < simplified significantly from a computational standpoint, in that the
332 < number of long-range interactions is dramatically reduced. In the
333 < original Monte Carlo simulations using this model, Ichiye \emph{et
334 < al.} reported a calculation speed up of up to an order of magnitude
335 < over other comparable models while maintaining the structural behavior
336 < of water.\cite{liu96:new_model} In the original molecular dynamics studies of
337 < SSD, it was shown that it actually improves upon the prediction of
338 < water's dynamical properties over TIP3P and SPC/E.\cite{chandra99:ssd_md}
330 > Since SSD is a single-point {\it dipolar} model, the force
331 > calculations are simplified significantly relative to the standard
332 > {\it charged} multi-point models. In the original Monte Carlo
333 > simulations using this model, Ichiye {\it et al.} reported that using
334 > SSD decreased computer time by a factor of 6-7 compared to other
335 > models.\cite{Ichiye96} What is most impressive is that this savings
336 > did not come at the expense of accurate depiction of the liquid state
337 > properties.  Indeed, SSD maintains reasonable agreement with the Soper
338 > data for the structural features of liquid
339 > water.\cite{Soper86,Ichiye96} Additionally, the dynamical properties
340 > exhibited by SSD agree with experiment better than those of more
341 > computationally expensive models (like TIP3P and
342 > SPC/E).\cite{Ichiye99} The combination of speed and accurate depiction
343 > of solvent properties makes SSD a very attractive model for the
344 > simulation of large scale biochemical simulations.
345  
346   Recent constant pressure simulations revealed issues in the original
347   SSD model that led to lower than expected densities at all target
348 < pressures.\cite{Ichiye03,Gezelter04} Reparameterizations of the
349 < original SSD have resulted in improved density behavior, as well as
350 < alterations in the water structure and transport behavior. {\sc oopse} is
351 < easily modified to impliment these new potential parameter sets for
352 < the derivative water models: SSD1, SSD/E, and SSD/RF. All of the
353 < variable parameters are listed in the accompanying BASS file, and
354 < these parameters simply need to be changed to the updated values.
348 > pressures.\cite{Ichiye03,Gezelter04} The default model in {\sc oopse}
349 > is SSD/E, a density corrected derivative of SSD that exhibits improved
350 > liquid structure and transport behavior. If the use of a reaction
351 > field long-range interaction correction is desired, it is recommended
352 > that the parameters be modified to those of the SSD/RF model. Solvent
353 > parameters can be easily modified in an accompanying {\sc BASS} file
354 > as illustrated in the scheme below. A table of the parameter values
355 > and the drawbacks and benefits of the different density corrected SSD
356 > models can be found in reference \ref{Gezelter04}.
357  
358 + !!!Place a {\sc BASS} scheme showing SSD parameters around here!!!
359  
360 < \subsection{\label{sec:eam}Embedded Atom Model}
360 > \subsection{\label{sec:eam}Embedded Atom Method}
361  
362 < Several molecular dynamics codes\cite{dynamo86} exist which have the
362 > Several other molecular dynamics packages\cite{dynamo86} exist which have the
363   capacity to simulate metallic systems, including some that have
364   parallel computational abilities\cite{plimpton93}. Potentials that
365   describe bonding transition metal
366   systems\cite{Finnis84,Ercolessi88,Chen90,Qi99,Ercolessi02} have a
367 < attractive interaction which models the stabilization of ``Embedding''
368 < a positively charged metal ion in an electron density created by the
367 > attractive interaction which models  ``Embedding''
368 > a positively charged metal ion in the electron density due to the
369   free valance ``sea'' of electrons created by the surrounding atoms in
370   the system. A mostly repulsive pairwise part of the potential
371   describes the interaction of the positively charged metal core ions
372   with one another. A particular potential description called the
373 < Embedded Atom Method\cite{Daw84,FBD86,johnson89,Lu97}(EAM) that has
373 > Embedded Atom Method\cite{Daw84,FBD86,johnson89,Lu97}({\sc eam}) that has
374   particularly wide adoption has been selected for inclusion in OOPSE. A
375 < good review of EAM and other metallic potential formulations was done
375 > good review of {\sc eam} and other metallic potential formulations was done
376   by Voter.\cite{voter}
377  
378   The {\sc eam} potential has the form:
# Line 365 | Line 380 | V & = & \sum_{i} F_{i}\left[\rho_{i}\right] + \sum_{i}
380   V & = & \sum_{i} F_{i}\left[\rho_{i}\right] + \sum_{i} \sum_{j \neq i}
381   \phi_{ij}({\bf r}_{ij})  \\
382   \rho_{i}  & = & \sum_{j \neq i} f_{j}({\bf r}_{ij})
383 < \end{eqnarray}
383 > \end{eqnarray}S
384  
385 < where $\phi_{ij}$ is a primarily repulsive pairwise interaction
386 < between atoms $i$ and $j$.In the origional formulation of
372 < EAM\cite{Daw84}, $\phi_{ij}$ was an entirely repulsive term, however
373 < in later refinements to EAM have shown that nonuniqueness between $F$
374 < and $\phi$ allow for more general forms for $\phi$.\cite{Daw89} The
375 < embedding function $F_{i}$ is the energy required to embedded an
376 < positively-charged core ion $i$ into a linear supeposition of
385 > where $F_{i} $ is the embedding function that equates the energy required to embed a
386 > positively-charged core ion $i$ into a linear superposition of
387   sperically averaged atomic electron densities given by
388 < $\rho_{i}$. There is a cutoff distance, $r_{cut}$, which limits the
388 > $\rho_{i}$.  $\phi_{ij}$ is a primarily repulsive pairwise interaction
389 > between atoms $i$ and $j$. In the original formulation of
390 > {\sc eam} cite{Daw84}, $\phi_{ij}$ was an entirely repulsive term, however
391 > in later refinements to EAM have shown that non-uniqueness between $F$
392 > and $\phi$ allow for more general forms for $\phi$.\cite{Daw89}
393 > There is a cutoff distance, $r_{cut}$, which limits the
394   summations in the {\sc eam} equation to the few dozen atoms
395   surrounding atom $i$ for both the density $\rho$ and pairwise $\phi$
396 < interactions.
396 > interactions. Foiles et al. fit EAM potentials for fcc metals Cu, Ag, Au, Ni, Pd, Pt and alloys of these metals\cite{FDB86}. These potential fits are in the DYNAMO 86 format and are included with {\sc oopse}.
397  
398 +
399   \subsection{\label{Sec:pbc}Periodic Boundary Conditions}
400 +
401 + \newcommand{\roundme}{\operatorname{round}}
402  
403 < \textit{Periodic boundary conditions} are widely used to simulate truly
404 < macroscopic systems with a relatively small number of particles. Simulation
405 < box is replicated throughout space to form an infinite lattice. During the
406 < simulation, when a particle moves in the primary cell, its periodic image
407 < particles in other boxes move in exactly the same direction with exactly the
408 < same orientation.Thus, as a particle leaves the primary cell, one of its
409 < images will enter through the opposite face.If the simulation box is large
410 < enough to avoid "feeling" the symmetric of the periodic lattice,the surface
411 < effect could be ignored. Cubic and parallelepiped are the available periodic
412 < cells. \bigskip In OOPSE, we use the matrix instead of the vector to describe
413 < the property of the simulation box. Therefore, not only the size of the
414 < simulation box could be changed during the simulation, but also the shape of
415 < it. The transformation from box space vector $\overrightarrow{s}$ to its
416 < corresponding real space vector $\overrightarrow{r}$ is defined by
417 < \begin{equation}
418 < \overrightarrow{r}=H\overrightarrow{s}%
419 < \end{equation}
420 <
421 <
422 < where $H=(h_{x},h_{y},h_{z})$ is a transformation matrix made up of the three
423 < box axis vectors. $h_{x},h_{y}$ and $h_{z}$ represent the sides of the
424 < simulation box respectively.
425 <
426 < To find the minimum image, we need to convert the real vector to its
427 < corresponding vector in box space first, \bigskip%
428 < \begin{equation}
429 < \overrightarrow{s}=H^{-1}\overrightarrow{r}%
430 < \end{equation}
431 < And then, each element of $\overrightarrow{s}$ is casted to lie between -0.5
432 < to 0.5,
433 < \begin{equation}
434 < s_{i}^{\prime}=s_{i}-round(s_{i})
435 < \end{equation}
436 < where%
437 <
438 < \begin{equation}
439 < round(x)=\lfloor{x+0.5}\rfloor\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }if\text{
440 < }x\geqslant0
441 < \end{equation}
442 < %
443 <
444 < \begin{equation}
445 < round(x)=\lceil{x-0.5}\rceil\text{ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }if\text{ }x<0
446 < \end{equation}
447 <
448 <
449 < For example, $round(3.6)=4$,$round(3.1)=3$, $round(-3.6)=-4$, $round(-3.1)=-3$.
450 <
451 < Finally, we could get the minimum image by transforming back to real space,%
452 <
453 < \begin{equation}
454 < \overrightarrow{r^{\prime}}=H^{-1}\overrightarrow{s^{\prime}}%
455 < \end{equation}
403 > \textit{Periodic boundary conditions} are widely used to simulate truly
404 > macroscopic systems with a relatively small number of particles. The
405 > simulation box is replicated throughout space to form an infinite
406 > lattice.  During the simulation, when a particle moves in the primary
407 > cell, its image in other boxes move in exactly the same direction with
408 > exactly the same orientation.Thus, as a particle leaves the primary
409 > cell, one of its images will enter through the opposite face.If the
410 > simulation box is large enough to avoid "feeling" the symmetries of
411 > the periodic lattice, surface effects can be ignored. Cubic,
412 > orthorhombic and parallelepiped are the available periodic cells In
413 > OOPSE. We use a matrix to describe the property of the simulation
414 > box. Therefore, both the size and shape of the simulation box can be
415 > changed during the simulation. The transformation from box space
416 > vector $\mathbf{s}$ to its corresponding real space vector
417 > $\mathbf{r}$ is defined by
418 > \begin{equation}
419 > \mathbf{r}=\underline{\underline{H}}\cdot\mathbf{s}%
420 > \end{equation}
421 >
422 >
423 > where $H=(h_{x},h_{y},h_{z})$ is a transformation matrix made up of
424 > the three box axis vectors. $h_{x},h_{y}$ and $h_{z}$ represent the
425 > three sides of the simulation box respectively.
426 >
427 > To find the minimum image, we convert the real vector to its
428 > corresponding vector in box space first, \bigskip%
429 > \begin{equation}
430 > \mathbf{s}=\underline{\underline{H}}^{-1}\cdot\mathbf{r}%
431 > \end{equation}
432 > And then, each element of $\mathbf{s}$ is wrapped to lie between -0.5 to 0.5,
433 > \begin{equation}
434 > s_{i}^{\prime}=s_{i}-\roundme(s_{i})
435 > \end{equation}
436 > where
437 >
438 > %
439 >
440 > \begin{equation}
441 > \roundme(x)=\left\{
442 > \begin{array}{cc}
443 > \lfloor{x+0.5}\rfloor & \text{if \ }x\geqslant0\\
444 > \lceil{x-0.5}\rceil & \text{otherwise}%
445 > \end{array}
446 > \right.
447 > \end{equation}
448 > For example, $\roundme(3.6)=4$, $\roundme(3.1)=3$, $\roundme(-3.6)=-4$,
449 > $\roundme(-3.1)=-3$.
450 >
451 > Finally, we obtain the minimum image coordinates by transforming back
452 > to real space,%
453 >
454 > \begin{equation}
455 > \mathbf{r}^{\prime}=\underline{\underline{H}}^{-1}\cdot\mathbf{s}^{\prime}%
456 > \end{equation}
457 >

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