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# Line 117 | Line 117 | expensive.  A 62 $\AA^3$ box of water solvating a mode
117   higher than desirable, or unreasonable system sizes to avoid this
118   effect.  For example, calculations using typical hydration boxes
119   solvating a protein under periodic boundary conditions are quite
120 < expensive.  A 62 $\AA^3$ box of water solvating a moderately small
120 > expensive.  A 62 \AA$^3$ box of water solvating a moderately small
121   protein like hen egg white lysozyme (PDB code: 1LYZ) yields an
122   effective protein concentration of 100 mg/mL.\cite{Asthagiri20053300}
123  
124 < Typically protein concentrations in the cell are on the order of
125 < 160-310 mg/ml,\cite{Brown1991195} and the factor of 20 difference
126 < between simulations and the cellular environment may have significant
127 < effects on the structure and dynamics of simulated protein structures.
124 > {\it Yotal} protein concentrations in the cell are typically on the
125 > order of 160-310 mg/ml,\cite{Brown1991195} and individual proteins
126 > have concentrations orders of magnitude lower than this in the
127 > cellular environment. The effective concentrations of single proteins
128 > in simulations may have significant effects on the structure and
129 > dynamics of simulated structures.
130  
129
131   \subsection*{Boundary Methods}
132   There have been a number of approaches to handle simulations of
133   explicitly non-periodic systems that focus on constant or
# Line 138 | Line 139 | used widely for protein simulations. [CITATIONS NEEDED
139   fixed regions were defined relative to a central atom.  Brooks and
140   Karplus extended this method to include deformable stochastic
141   boundaries.\cite{iii:6312} The stochastic boundary approach has been
142 < used widely for protein simulations. [CITATIONS NEEDED]
142 > used widely for protein simulations.
143  
144   The electrostatic and dispersive behavior near the boundary has long
145   been a cause for concern when performing simulations of explicitly
# Line 149 | Line 150 | affecting most of molecules in the simulation.  This r
150   simulated clusters of TIPS2 water surrounded by a hydrophobic bounding
151   potential. The spherical hydrophobic boundary induced dangling
152   hydrogen bonds at the surface that propagated deep into the cluster,
153 < affecting most of molecules in the simulation.  This result echoes an
154 < earlier study which showed that an extended planar hydrophobic surface
155 < caused orientational preference at the surface which extended
156 < relatively deep (7 \r{A}) into the liquid simulation
157 < cell.\cite{Lee1984} The surface constrained all-atom solvent (SCAAS)
158 < model \cite{King1989} improved upon its SCSSD predecessor. The SCAAS
159 < model utilizes a polarization constraint which is applied to the
160 < surface molecules to maintain bulk-like structure at the cluster
161 < surface. A radial constraint is used to maintain the desired bulk
162 < density of the liquid. Both constraint forces are applied only to a
163 < pre-determined number of the outermost molecules.
153 > affecting most of the molecules in the simulation.  This result echoes
154 > an earlier study which showed that an extended planar hydrophobic
155 > surface caused orientational preferences at the surface which extended
156 > relatively deep (7 \AA) into the liquid simulation cell.\cite{Lee1984}
157 > The surface constrained all-atom solvent (SCAAS) model \cite{King1989}
158 > improved upon its SCSSD predecessor. The SCAAS model utilizes a
159 > polarization constraint which is applied to the surface molecules to
160 > maintain bulk-like structure at the cluster surface. A radial
161 > constraint is used to maintain the desired bulk density of the
162 > liquid. Both constraint forces are applied only to a pre-determined
163 > number of the outermost molecules.
164  
165   Beglov and Roux have developed a boundary model in which the hard
166   sphere boundary has a radius that varies with the instantaneous
167   configuration of the solute (and solvent) molecules.\cite{beglov:9050}
168   This model contains a clear pressure and surface tension contribution
169 < to the free energy which XXX.
169 > to the free energy.
170  
171   \subsection*{Restraining Potentials}
172   Restraining {\it potentials} introduce repulsive potentials at the
# Line 180 | Line 181 | position of the nearest solute atom.\cite{LiY._jp04685
181   Recently, Krilov {\it et al.} introduced a {\it flexible} boundary
182   model that uses a Lennard-Jones potential between the solvent
183   molecules and a boundary which is determined dynamically from the
184 < position of the nearest solute atom.\cite{LiY._jp046852t,Zhu:xw} This
184 > position of the nearest solute atom.\cite{LiY._jp046852t,Zhu:2008fk} This
185   approach allows the confining potential to prevent solvent molecules
186   from migrating too far from the solute surface, while providing a weak
187   attractive force pulling the solvent molecules towards a fictitious
# Line 196 | Line 197 | force in a direction that is inward-facing relative to
197   into non-periodic simulations.\cite{Kohanoff:2005qm,Baltazar:2006ru}
198   This method is based on standard Langevin dynamics, but the Brownian
199   or random forces are allowed to act only on peripheral atoms and exert
200 < force in a direction that is inward-facing relative to the facets of a
201 < closed bounding surface.  The statistical distribution of the random
200 > forces in a direction that is inward-facing relative to the facets of
201 > a closed bounding surface.  The statistical distribution of the random
202   forces are uniquely tied to the pressure in the external reservoir, so
203   the method can be shown to sample the isobaric-isothermal ensemble.
204   Kohanoff {\it et al.} used a Delaunay tessellation to generate a
# Line 221 | Line 222 | The Langevin Hull uses an external bath at a fixed con
222   \label{sec:meth}
223  
224   The Langevin Hull uses an external bath at a fixed constant pressure
225 < ($P$) and temperature ($T$).  This bath interacts only with the
226 < objects on the exterior hull of the system.  Defining the hull of the
227 < simulation is done in a manner similar to the approach of Kohanoff,
228 < Caro and Finnis.\cite{Kohanoff:2005qm} That is, any instantaneous
229 < configuration of the atoms in the system is considered as a point
230 < cloud in three dimensional space.  Delaunay triangulation is used to
231 < find all facets between coplanar
232 < neighbors.\cite{delaunay,springerlink:10.1007/BF00977785}  In highly
225 > ($P$) and temperature ($T$) with an effective solvent viscosity
226 > ($\eta$).  This bath interacts only with the objects on the exterior
227 > hull of the system.  Defining the hull of the atoms in a simulation is
228 > done in a manner similar to the approach of Kohanoff, Caro and
229 > Finnis.\cite{Kohanoff:2005qm} That is, any instantaneous configuration
230 > of the atoms in the system is considered as a point cloud in three
231 > dimensional space.  Delaunay triangulation is used to find all facets
232 > between coplanar
233 > neighbors.\cite{delaunay,springerlink:10.1007/BF00977785} In highly
234   symmetric point clouds, facets can contain many atoms, but in all but
235 < the most symmetric of cases the facets are simple triangles in 3-space
236 < that contain exactly three atoms.
235 > the most symmetric of cases, the facets are simple triangles in
236 > 3-space which contain exactly three atoms.
237  
238   The convex hull is the set of facets that have {\it no concave
239    corners} at an atomic site.\cite{Barber96,EDELSBRUNNER:1994oq} This
# Line 245 | Line 247 | simulation.
247   simulation.
248  
249   \begin{figure}
250 < \includegraphics[width=\linewidth]{hullSample}
250 > \includegraphics[width=\linewidth]{solvatedNano}
251   \caption{The external temperature and pressure bath interacts only
252    with those atoms on the convex hull (grey surface).  The hull is
253    computed dynamically at each time step, and molecules can move
254 <  between the interior (Newtonian) region and the Langevin hull.}
254 >  between the interior (Newtonian) region and the Langevin Hull.}
255   \label{fig:hullSample}
256   \end{figure}
257  
# Line 264 | Line 266 | equation of motion is modified with an external force,
266   potential energy.  For atoms on the exterior of the cluster
267   (i.e. those that occupy one of the vertices of the convex hull), the
268   equation of motion is modified with an external force, ${\mathbf
269 <  F}_i^{\mathrm ext}$,
269 >  F}_i^{\mathrm ext}$:
270   \begin{equation}
271   m_i \dot{\mathbf v}_i(t)=-{\mathbf \nabla}_i U + {\mathbf F}_i^{\mathrm ext}.
272   \end{equation}
# Line 300 | Line 302 | viscosity of the fluid.  The resistance tensor is rela
302   \end{equation}
303   and $\Xi_f(t)$ is an approximate ($3 \times 3$) resistance tensor that
304   depends on the geometry and surface area of facet $f$ and the
305 < viscosity of the fluid.  The resistance tensor is related to the
305 > viscosity of the bath.  The resistance tensor is related to the
306   fluctuations of the random force, $\mathbf{R}(t)$, by the
307   fluctuation-dissipation theorem,
308   \begin{eqnarray}
# Line 330 | Line 332 | Our treatment of the resistance tensor is approximate.
332   random force, ${\bf R}_{f} = {\bf S} {\bf Z}$, can be shown to
333   have the correct properties required by Eq. (\ref{eq:randomForce}).
334  
335 < Our treatment of the resistance tensor is approximate.  $\Xi$ for a
335 > Our treatment of the resistance tensor is approximate.  $\Xi_f$ for a
336   rigid triangular plate would normally be treated as a $6 \times 6$
337   tensor that includes translational and rotational drag as well as
338   translational-rotational coupling. The computation of resistance
# Line 385 | Line 387 | molecular dynamics time step, the following process is
387   \item The convex hull is computed and facets are identified.
388   \item For each facet:
389   \begin{itemize}
390 < \item[a.] The force from the pressure bath ($-PA_f\hat{n}_f$) is
390 > \item[a.] The force from the pressure bath ($-\hat{n}_fPA_f$) is
391    computed.
392   \item[b.] The resistance tensor ($\Xi_f(t)$) is computed using the
393    viscosity ($\eta$) of the bath.
# Line 410 | Line 412 | heterogeneous mixture (gold nanoparticles in a water d
412   To test the new method, we have carried out simulations using the
413   Langevin Hull on: 1) a crystalline system (gold nanoparticles), 2) a
414   liquid droplet (SPC/E water),\cite{Berendsen1987} and 3) a
415 < heterogeneous mixture (gold nanoparticles in a water droplet). In each
414 < case, we have computed properties that depend on the external applied
415 < pressure.  Of particular interest for the single-phase systems is the
416 < isothermal compressibility,
415 > heterogeneous mixture (gold nanoparticles in an SPC/E water droplet). In each case, we have computed properties that depend on the external applied pressure. Of particular interest for the single-phase systems is the isothermal compressibility,
416   \begin{equation}
417   \kappa_{T} = -\frac{1}{V} \left ( \frac{\partial V}{\partial P} \right
418   )_{T}.
# Line 422 | Line 421 | is not well-defined.  In order to compute the compress
421  
422   One problem with eliminating periodic boundary conditions and
423   simulation boxes is that the volume of a three-dimensional point cloud
424 < is not well-defined.  In order to compute the compressibility of a
424 > is not well-defined. In order to compute the compressibility of a
425   bulk material, we make an assumption that the number density, $\rho =
426 < \frac{N}{V}$, is uniform within some region of the point cloud.  The
426 > \frac{N}{V}$, is uniform within some region of the point cloud. The
427   compressibility can then be expressed in terms of the average number
428   of particles in that region,
429   \begin{equation}
430   \kappa_{T} = -\frac{1}{N} \left ( \frac{\partial N}{\partial P} \right
431 < )_{T}
431 > )_{T}.
432   \label{eq:BMN}
433   \end{equation}
434 < The region we used is a spherical volume of 10 \AA\ radius centered in
434 > The region we used is a spherical volume of 20 \AA\ radius centered in
435   the middle of the cluster. $N$ is the average number of molecules
436   found within this region throughout a given simulation. The geometry
437   and size of the region is arbitrary, and any bulk-like portion of the
# Line 454 | Line 453 | atoms and the SPC/E water molecules.\cite{ISI:00016776
453   Spohr potential was adopted in depicting the interaction between metal
454   atoms and the SPC/E water molecules.\cite{ISI:000167766600035}
455  
456 < \subsection{Compressibility of gold nanoparticles}
456 > \subsection{Bulk Modulus of gold nanoparticles}
457  
458   The compressibility (and its inverse, the bulk modulus) is well-known
459   for gold, and is captured well by the embedded atom method
460 < (EAM)~\cite{PhysRevB.33.7983} potential
461 < and related multi-body force fields.  In particular, the quantum
462 < Sutton-Chen potential gets nearly quantitative agreement with the
463 < experimental bulk modulus values, and makes a good first test of how
464 < the Langevin Hull will perform at large applied pressures.
460 > (EAM)~\cite{PhysRevB.33.7983} potential and related multi-body force
461 > fields.  In particular, the quantum Sutton-Chen potential gets nearly
462 > quantitative agreement with the experimental bulk modulus values, and
463 > makes a good first test of how the Langevin Hull will perform at large
464 > applied pressures.
465  
466   The Sutton-Chen (SC) potentials are based on a model of a metal which
467   treats the nuclei and core electrons as pseudo-atoms embedded in the
468   electron density due to the valence electrons on all of the other
469 < atoms in the system.\cite{Chen90} The SC potential has a simple form that closely
470 < resembles the Lennard Jones potential,
469 > atoms in the system.\cite{Chen90} The SC potential has a simple form
470 > that closely resembles the Lennard Jones potential,
471   \begin{equation}
472   \label{eq:SCP1}
473   U_{tot}=\sum _{i}\left[ \frac{1}{2}\sum _{j\neq i}D_{ij}V^{pair}_{ij}(r_{ij})-c_{i}D_{ii}\sqrt{\rho_{i}}\right] ,
# Line 490 | Line 489 | metals.\cite{PhysRevB.59.3527}
489   energy, and elastic moduli for FCC transition metals. The quantum
490   Sutton-Chen (QSC) formulation matches these properties while including
491   zero-point quantum corrections for different transition
492 < metals.\cite{PhysRevB.59.3527}
492 > metals.\cite{PhysRevB.59.3527,QSC}
493  
494   In bulk gold, the experimentally-measured value for the bulk modulus
495   is 180.32 GPa, while previous calculations on the QSC potential in
496 < periodic-boundary simulations of the bulk have yielded values of
497 < 175.53 GPa.\cite{XXX} Using the same force field, we have performed a
498 < series of relatively short (200 ps) simulations on 40 \r{A} radius
496 > periodic-boundary simulations of the bulk crystal have yielded values
497 > of 175.53 GPa.\cite{QSC} Using the same force field, we have performed
498 > a series of 1 ns simulations on 40 \AA~ radius
499   nanoparticles under the Langevin Hull at a variety of applied
500 < pressures ranging from 0 GPa to XXX.  We obtain a value of 177.547 GPa
501 < for the bulk modulus for gold using this echnique.
500 > pressures ranging from 0 -- 10 GPa.  We obtain a value of 177.55 GPa
501 > for the bulk modulus of gold using this technique, in close agreement
502 > with both previous simulations and the experimental bulk modulus of
503 > gold.
504  
505   \begin{figure}
506   \includegraphics[width=\linewidth]{stacked}
# Line 508 | Line 509 | for the bulk modulus for gold using this echnique.
509    ($T_\mathrm{bath}$ = 300K, $P_\mathrm{bath}$ = 4 GPa), starting
510    from initial conditions that were far from the bath pressure and
511    temperature.  The pressure response is rapid (after the breathing mode oscillations in the nanoparticle die out), and the rate of thermal equilibration depends on both exposed surface area (top panel) and the viscosity of the bath (middle panel).}
512 < \label{pressureResponse}
512 > \label{fig:pressureResponse}
513   \end{figure}
514  
515 < \begin{equation}
516 < \kappa_T=-\frac{1}{V_{\mathrm{eq}}}\left(\frac{\partial V}{\partial
517 <    P}\right)
518 < \end{equation}
515 > We note that the Langevin Hull produces rapidly-converging behavior
516 > for structures that are started far from equilibrium.  In
517 > Fig. \ref{fig:pressureResponse} we show how the pressure and
518 > temperature respond to the Langevin Hull for nanoparticles that were
519 > initialized far from the target pressure and temperature.  As
520 > expected, the rate at which thermal equilibrium is achieved depends on
521 > the total surface area of the cluster exposed to the bath as well as
522 > the bath viscosity.  Pressure that is applied suddenly to a cluster
523 > can excite breathing vibrations, but these rapidly damp out (on time
524 > scales of 30 -- 50 ps).
525  
526   \subsection{Compressibility of SPC/E water clusters}
527  
# Line 526 | Line 533 | Compressibility values from all references are for app
533   Langevin Hull simulations for pressures between 1 and 6500 atm are
534   shown in Fig. \ref{fig:compWater} along with compressibility values
535   obtained from both other SPC/E simulations and experiment.
529 Compressibility values from all references are for applied pressures
530 within the range 1 - 1000 atm.
536  
537   \begin{figure}
538   \includegraphics[width=\linewidth]{new_isothermalN}
# Line 537 | Line 542 | and previous simulation work throughout the 1 - 1000 a
542  
543   Isothermal compressibility values calculated using the number density
544   (Eq. \ref{eq:BMN}) expression are in good agreement with experimental
545 < and previous simulation work throughout the 1 - 1000 atm pressure
545 > and previous simulation work throughout the 1 -- 1000 atm pressure
546   regime.  Compressibilities computed using the Hull volume, however,
547   deviate dramatically from the experimental values at low applied
548   pressures.  The reason for this deviation is quite simple; at low
# Line 549 | Line 554 | geometries which include large volumes of empty space.
554   geometries which include large volumes of empty space.
555  
556   \begin{figure}
557 < \includegraphics[width=\linewidth]{flytest2}
557 > \includegraphics[width=\linewidth]{coneOfShame}
558   \caption{At low pressures, the liquid is in equilibrium with the vapor
559    phase, and isolated molecules can detach from the liquid droplet.
560    This is expected behavior, but the volume of the convex hull
561 <  includes large regions of empty space.  For this reason,
561 >  includes large regions of empty space. For this reason,
562    compressibilities are computed using local number densities rather
563    than hull volumes.}
564   \label{fig:coneOfShame}
# Line 574 | Line 579 | volume,\cite{Debenedetti1986},
579   \begin{equation}
580   \kappa_{T} = \frac{\left \langle V^{2} \right \rangle - \left \langle
581      V \right \rangle ^{2}}{V \, k_{B} \, T},
582 + \label{eq:BMVfluct}
583   \end{equation}
584   or, equivalently, fluctuations in the number of molecules within the
585   fixed region,
586   \begin{equation}
587   \kappa_{T} = \frac{\left \langle N^{2} \right \rangle - \left \langle
588 <    N \right \rangle ^{2}}{N \, k_{B} \, T},
588 >    N \right \rangle ^{2}}{N \, k_{B} \, T}.
589 > \label{eq:BMNfluct}
590   \end{equation}
591   Thus, the compressibility of each simulation can be calculated
592 < entirely independently from all other trajectories. However, the
593 < resulting compressibilities were still as much as an order of
594 < magnitude larger than the reference values. However, compressibility
595 < calculation that relies on the hull volume will suffer these effects.
596 < WE NEED MORE HERE.
592 > entirely independently from other trajectories.  Compressibility
593 > calculations that rely on the hull volume will still suffer the
594 > effects of the empty space due to the vapor phase; for this reason, we
595 > recommend using the number density (Eq. \ref{eq:BMN}) or number
596 > density fluctuations (Eq. \ref{eq:BMNfluct}) for computing
597 > compressibilities.
598  
599   \subsection{Molecular orientation distribution at cluster boundary}
600  
601 < In order for non-periodic boundary conditions to be widely applicable,
602 < they must be constructed in such a way that they allow a finite system
603 < to replicate the properties of the bulk.  Early non-periodic
604 < simulation methods (e.g. hydrophobic boundary potentials) induced
605 < spurious orientational correlations deep within the simulated
601 > In order for a non-periodic boundary method to be widely applicable,
602 > it must be constructed in such a way that they allow a finite system
603 > to replicate the properties of the bulk. Early non-periodic simulation
604 > methods (e.g. hydrophobic boundary potentials) induced spurious
605 > orientational correlations deep within the simulated
606   system.\cite{Lee1984,Belch1985} This behavior spawned many methods for
607   fixing and characterizing the effects of artifical boundaries
608   including methods which fix the orientations of a set of edge
# Line 602 | Line 610 | hydrophobic boundary, orientational constraint or radi
610  
611   As described above, the Langevin Hull does not require that the
612   orientation of molecules be fixed, nor does it utilize an explicitly
613 < hydrophobic boundary, orientational constraint or radial constraint.
614 < Therefore, the orientational correlations of the molecules in a water
615 < cluster are of particular interest in testing this method.  Ideally,
616 < the water molecules on the surface of the cluster will have enough
617 < mobility into and out of the center of the cluster to maintain a
613 > hydrophobic boundary, or orientational or radial constraints.
614 > Therefore, the orientational correlations of the molecules in water
615 > clusters are of particular interest in testing this method.  Ideally,
616 > the water molecules on the surfaces of the clusters will have enough
617 > mobility into and out of the center of the cluster to maintain
618   bulk-like orientational distribution in the absence of orientational
619   and radial constraints.  However, since the number of hydrogen bonding
620   partners available to molecules on the exterior are limited, it is
621 < likely that there will be some effective hydrophobicity of the hull.
621 > likely that there will be an effective hydrophobicity of the hull.
622  
623 < To determine the extent of these effects demonstrated by the Langevin
624 < Hull, we examined the orientationations exhibited by SPC/E water in a
625 < cluster of 1372 molecules at 300 K and at pressures ranging from 1 -
626 < 1000 atm.  The orientational angle of a water molecule is described
623 > To determine the extent of these effects, we examined the
624 > orientations exhibited by SPC/E water in a cluster of 1372
625 > molecules at 300 K and at pressures ranging from 1 -- 1000 atm.  The
626 > orientational angle of a water molecule is described by
627   \begin{equation}
628   \cos{\theta}=\frac{\vec{r}_i\cdot\vec{\mu}_i}{|\vec{r}_i||\vec{\mu}_i|}
629   \end{equation}
630   where $\vec{r}_{i}$ is the vector between molecule {\it i}'s center of
631 < mass and the cluster center of mass and $\vec{\mu}_{i}$ is the vector
632 < bisecting the H-O-H angle of molecule {\it i} Bulk-like distributions
633 < will result in $\langle \cos \theta \rangle$ values close to zero.  If
634 < the hull exhibits an overabundance of externally-oriented oxygen sites
635 < the average orientation will be negative, while dangling hydrogen
636 < sites will result in positive average orientations.
631 > mass and the cluster center of mass, and $\vec{\mu}_{i}$ is the vector
632 > bisecting the H-O-H angle of molecule {\it i}.  Bulk-like
633 > distributions will result in $\langle \cos \theta \rangle$ values
634 > close to zero.  If the hull exhibits an overabundance of
635 > externally-oriented oxygen sites, the average orientation will be
636 > negative, while dangling hydrogen sites will result in positive
637 > average orientations.
638  
639   Fig. \ref{fig:pAngle} shows the distribution of $\cos{\theta}$ values
640   for molecules in the interior of the cluster (squares) and for
# Line 634 | Line 643 | molecules included in the convex hull (circles).
643   \includegraphics[width=\linewidth]{pAngle}
644   \caption{Distribution of $\cos{\theta}$ values for molecules on the
645    interior of the cluster (squares) and for those participating in the
646 <  convex hull (circles) at a variety of pressures.  The Langevin hull
646 >  convex hull (circles) at a variety of pressures.  The Langevin Hull
647    exhibits minor dewetting behavior with exposed oxygen sites on the
648    hull water molecules.  The orientational preference for exposed
649    oxygen appears to be independent of applied pressure. }
# Line 646 | Line 655 | forming a dangling hydrogen bond acceptor site.
655   orientations. Molecules included in the convex hull show a slight
656   preference for values of $\cos{\theta} < 0.$ These values correspond
657   to molecules with oxygen directed toward the exterior of the cluster,
658 < forming a dangling hydrogen bond acceptor site.
658 > forming dangling hydrogen bond acceptor sites.
659  
660 < In the absence of an electrostatic contribution from the exterior
661 < bath, the orientational distribution of water molecules included in
662 < the Langevin Hull will slightly resemble the distribution at a neat
663 < water liquid/vapor interface.  Previous molecular dynamics simulations
664 < of SPC/E water \cite{Taylor1996} have shown that molecules at the
656 < liquid/vapor interface favor an orientation where one hydrogen
657 < protrudes from the liquid phase. This behavior is demonstrated by
658 < experiments \cite{Du1994} \cite{Scatena2001} showing that
659 < approximately one-quarter of water molecules at the liquid/vapor
660 < interface form dangling hydrogen bonds. The negligible preference
661 < shown in these cluster simulations could be removed through the
662 < introduction of an implicit solvent model, which would provide the
663 < missing electrostatic interactions between the cluster molecules and
664 < the surrounding temperature/pressure bath.
660 > The orientational preference exhibited by water molecules on the hull
661 > is significantly weaker than the preference caused by an explicit
662 > hydrophobic bounding potential.  Additionally, the Langevin Hull does
663 > not require that the orientation of any molecules be fixed in order to
664 > maintain bulk-like structure, even near the cluster surface.
665  
666 < The orientational preference exhibited by hull molecules in the
667 < Langevin hull is significantly weaker than the preference caused by an
668 < explicit hydrophobic bounding potential.  Additionally, the Langevin
669 < Hull does not require that the orientation of any molecules be fixed
670 < in order to maintain bulk-like structure, even at the cluster surface.
666 > Previous molecular dynamics simulations of SPC/E liquid / vapor
667 > interfaces using periodic boundary conditions have shown that
668 > molecules on the liquid side of interface favor a similar orientation
669 > where oxygen is directed away from the bulk.\cite{Taylor1996} These
670 > simulations had well-defined liquid and vapor phase regions
671 > equilibrium and it was observed that {\it vapor} molecules generally
672 > had one hydrogen protruding from the surface, forming a dangling
673 > hydrogen bond donor. Our water clusters do not have a true vapor
674 > region, but rather a few transient molecules that leave the liquid
675 > droplet (and which return to the droplet relatively quickly).
676 > Although we cannot obtain an orientational preference of vapor phase
677 > molecules in a Langevin Hull simulation, but we do agree with previous
678 > estimates of the orientation of {\it liquid phase} molecules at the
679 > interface.
680  
681   \subsection{Heterogeneous nanoparticle / water mixtures}
682  
683 + To further test the method, we simulated gold nanopartices ($r = 18$
684 + \AA) solvated by explicit SPC/E water clusters using a model for the
685 + gold / water interactions that has been used by Dou {\it et. al.} for
686 + investigating the separation of water films near hot metal
687 + surfaces.\cite{ISI:000167766600035} The Langevin Hull was used to
688 + sample pressures of 1, 2, 5, 10, 20, 50, 100 and 200 atm, while all
689 + simulations were done at a temperature of 300 K.   At these
690 + temperatures and pressures, there is no observed separation of the
691 + water film from the surface.  
692 +
693 + In Fig. \ref{fig:RhoR} we show the density of water and gold as a
694 + function of the distance from the center of the nanoparticle.  Higher
695 + applied pressures appear to destroy structural correlations in the
696 + outermost monolayer of the gold nanoparticle as well as in the water
697 + at the near the metal / water interface.  Simulations at increased
698 + pressures exhibit significant overlap of the gold and water densities,
699 + indicating a less well-defined interfacial surface.
700 +
701 + \begin{figure}
702 + \includegraphics[width=\linewidth]{RhoR}
703 + \caption{Density profiles of gold and water at the nanoparticle
704 +  surface. Each curve has been normalized by the average density in
705 +  the bulk-like region available to the corresponding material.  Higher applied pressures
706 +  de-structure both the gold nanoparticle surface and water at the
707 +  metal/water interface.}
708 + \label{fig:RhoR}
709 + \end{figure}
710 +
711 + At even higher pressures (500 atm and above), problems with the metal
712 + - water interaction potential became quite clear.  The model we are
713 + using appears to have been parameterized for relatively low pressures;
714 + it utilizes both shifted Morse and repulsive Morse potentials to model
715 + the Au/O and Au/H interactions, respectively.  The repulsive wall of
716 + the Morse potential does not diverge quickly enough at short distances
717 + to prevent water from diffusing into the center of the gold
718 + nanoparticles.  This behavior is likely not a realistic description of
719 + the real physics of the situation.  A better model of the gold-water
720 + adsorption behavior appears to require harder repulsive walls to
721 + prevent this behavior.
722 +
723   \section{Discussion}
724   \label{sec:discussion}
725  
726   The Langevin Hull samples the isobaric-isothermal ensemble for
727 < non-periodic systems by coupling the system to an bath characterized
728 < by pressure, temperature, and solvent viscosity.  This enables the
729 < study of heterogeneous systems composed of materials of significantly
730 < different compressibilities.  Because the boundary is dynamically
731 < determined during the simulation and the molecules interacting with
732 < the boundary can change, the method and has minimal perturbations on
733 < the behavior of molecules at the edges of the simulation.  Further
734 < work on this method will involve implicit electrostatics at the
735 < boundary (which is missing in the current implementation) as well as
736 < more sophisticated treatments of the surface geometry (alpha
727 > non-periodic systems by coupling the system to a bath characterized by
728 > pressure, temperature, and solvent viscosity.  This enables the
729 > simulation of heterogeneous systems composed of materials with
730 > significantly different compressibilities.  Because the boundary is
731 > dynamically determined during the simulation and the molecules
732 > interacting with the boundary can change, the method inflicts minimal
733 > perturbations on the behavior of molecules at the edges of the
734 > simulation.  Further work on this method will involve implicit
735 > electrostatics at the boundary (which is missing in the current
736 > implementation) as well as more sophisticated treatments of the
737 > surface geometry (alpha
738   shapes\cite{EDELSBRUNNER:1994oq,EDELSBRUNNER:1995cj} and Tight
739   Cocone\cite{Dey:2003ts}). The non-convex hull geometries are
740   significantly more expensive ($\mathcal{O}(N^2)$) than the convex hull
# Line 695 | Line 745 | surface, facets, and resistance tensors when the proce
745  
746   In order to use the Langevin Hull for simulations on parallel
747   computers, one of the more difficult tasks is to compute the bounding
748 < surface, facets, and resistance tensors when the processors have
749 < incomplete information about the entire system's topology.  Most
748 > surface, facets, and resistance tensors when the individual processors
749 > have incomplete information about the entire system's topology.  Most
750   parallel decomposition methods assign primary responsibility for the
751   motion of an atomic site to a single processor, and we can exploit
752   this to efficiently compute the convex hull for the entire system.
# Line 734 | Line 784 | hull operations create a set of $p$ hulls each with ap
784   The individual hull operations scale with
785   $\mathcal{O}(\frac{n}{p}\log\frac{n}{p})$ where $n$ is the total
786   number of sites, and $p$ is the number of processors.  These local
787 < hull operations create a set of $p$ hulls each with approximately
788 < $\frac{n}{3pr}$ sites (for a cluster of radius $r$). The worst-case
787 > hull operations create a set of $p$ hulls, each with approximately
788 > $\frac{n}{3pr}$ sites for a cluster of radius $r$. The worst-case
789   communication cost for using a ``gather'' operation to distribute this
790   information to all processors is $\mathcal{O}( \alpha (p-1) + \frac{n
791    \beta (p-1)}{3 r p^2})$, while the final computation of the system
# Line 743 | Line 793 | and communication of these hulls to so the Langevin hu
793  
794   For a large number of atoms on a moderately parallel machine, the
795   total costs are dominated by the computations of the individual hulls,
796 < and communication of these hulls to so the Langevin hull sees roughly
796 > and communication of these hulls to create the Langevin Hull sees roughly
797   linear speed-up with increasing processor counts.
798  
799   \section*{Acknowledgments}

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