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# Line 146 | Line 146 | comparable models while maintaining the structural beh
146   simulations using this model, Ichiye \emph{et al.} reported a
147   calculation speed up of up to an order of magnitude over other
148   comparable models while maintaining the structural behavior of
149 < water.\cite{Ichiye96} In the original molecular dynamics studies of
150 < SSD, it was shown that it actually improves upon the prediction of
151 < water's dynamical properties 3 and 4-point models.\cite{Ichiye99} This
149 > water.\cite{Ichiye96} In the original molecular dynamics studies, it
150 > was shown that SSD improves on the prediction of many of water's
151 > dynamical properties over TIP3P and SPC/E.\cite{Ichiye99} This
152   attractive combination of speed and accurate depiction of solvent
153   properties makes SSD a model of interest for the simulation of large
154   scale biological systems, such as membrane phase behavior, a specific
# Line 205 | Line 205 | SSD more compatible with a reaction field.
205   to the use of reaction field, simulations were also performed without
206   a surrounding dielectric and suggestions are proposed on how to make
207   SSD more compatible with a reaction field.
208 <
208 >
209   Simulations were performed in both the isobaric-isothermal and
210   microcanonical ensembles. The constant pressure simulations were
211   implemented using an integral thermostat and barostat as outlined by
212 < Hoover.\cite{Hoover85,Hoover86} For the constant pressure
213 < simulations, the \emph{Q} parameter for the was set to 5.0 amu
214 < \(\cdot\)\AA\(^{2}\), and the relaxation time (\(\tau\))\ was set at
215 < 100 ps.
212 > Hoover.\cite{Hoover85,Hoover86} All particles were treated as
213 > non-linear rigid bodies. Vibrational constraints are not necessary in
214 > simulations of SSD, because there are no explicit hydrogen atoms, and
215 > thus no molecular vibrational modes need to be considered.
216  
217   Integration of the equations of motion was carried out using the
218   symplectic splitting method proposed by Dullweber \emph{et
# Line 220 | Line 220 | alternate ensembles, the microcanonical ensemble has a
220   deals with poor energy conservation of rigid body systems using
221   quaternions. While quaternions work well for orientational motion in
222   alternate ensembles, the microcanonical ensemble has a constant energy
223 < requirement that is actually quite sensitive to errors in the
224 < equations of motion. The original implementation of this code utilized
225 < quaternions for rotational motion propagation; however, a detailed
226 < investigation showed that they resulted in a steady drift in the total
227 < energy, something that has been observed by others.\cite{Laird97}
223 > requirement that is quite sensitive to errors in the equations of
224 > motion. The original implementation of this code utilized quaternions
225 > for rotational motion propagation; however, a detailed investigation
226 > showed that they resulted in a steady drift in the total energy,
227 > something that has been observed by others.\cite{Laird97}
228  
229   The key difference in the integration method proposed by Dullweber
230   \emph{et al.} is that the entire rotation matrix is propagated from
# Line 244 | Line 244 | matrix rotations end up being more costly computationa
244   method, the orientational propagation involves a sequence of matrix
245   evaluations to update the rotation matrix.\cite{Dullweber1997} These
246   matrix rotations end up being more costly computationally than the
247 < simpler arithmetic quaternion propagation. On average, a 1000 SSD
248 < particle simulation shows a 7\% increase in computation time using the
249 < symplectic step method in place of quaternions. This cost is more than
250 < justified when comparing the energy conservation of the two methods as
251 < illustrated in figure \ref{timestep}.
247 > simpler arithmetic quaternion propagation. With the same time step, a
248 > 1000 SSD particle simulation shows an average 7\% increase in
249 > computation time using the symplectic step method in place of
250 > quaternions. This cost is more than justified when comparing the
251 > energy conservation of the two methods as illustrated in figure
252 > \ref{timestep}.
253  
254   \begin{figure}
255   \includegraphics[width=61mm, angle=-90]{timeStep.epsi}
# Line 316 | Line 317 | volume fluctuations dampened out in all but the very c
317   increment was decreased from 25 K to 10 and then 5 K. The above
318   equilibration and production times were sufficient in that the system
319   volume fluctuations dampened out in all but the very cold simulations
320 < (below 225 K). In order to further improve statistics, five separate
321 < simulation progressions were performed, and the averaged results from
322 < the $I_h$ melting simulations are shown in figure \ref{dense1}.
322 <
323 < \begin{figure}
324 < \includegraphics[width=65mm, angle=-90]{1hdense.epsi}
325 < \caption{Average density of SSD water at increasing temperatures
326 < starting from ice $I_h$ lattice.}
327 < \label{dense1}
328 < \end{figure}
320 > (below 225 K). In order to further improve statistics, an ensemble
321 > average was accumulated from five separate simulation progressions,
322 > each starting from a different ice crystal.
323  
324   \subsection{Density Behavior}
325   In the initial average density versus temperature plot, the density
# Line 902 | Line 896 | simulations of biochemical systems.
896   simulations of biochemical systems.
897  
898   \section{Acknowledgments}
899 < The authors would like to thank the National Science Foundation for
900 < funding under grant CHE-0134881. Computation time was provided by the
901 < Notre Dame Bunch-of-Boxes (B.o.B) computer cluster under NSF grant DMR
902 < 00 79647.
899 > Support for this project was provided by the National Science
900 > Foundation under grant CHE-0134881. Computation time was provided by
901 > the Notre Dame Bunch-of-Boxes (B.o.B) computer cluster under NSF grant
902 > DMR 00 79647.
903  
904   \bibliographystyle{jcp}
905  

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