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17   \setlength{\abovecaptionskip}{20 pt}
18   \setlength{\belowcaptionskip}{30 pt}
19  
20 < \bibpunct{[}{]}{,}{s}{}{;}
20 > \bibpunct{}{}{,}{s}{}{;}
21   \bibliographystyle{achemso}
22  
23   \begin{document}
# 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 Total} 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 249 | Line 251 | simulation.
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 376 | Line 378 | integrator in our code, OpenMD.\cite{Meineke2005,openm
378   configurations, so this appears to be a reasonably good approximation.
379  
380   We have implemented this method by extending the Langevin dynamics
381 < integrator in our code, OpenMD.\cite{Meineke2005,openmd}  At each
381 > integrator in our code, OpenMD.\cite{Meineke2005,open_md}  At each
382   molecular dynamics time step, the following process is carried out:
383   \begin{enumerate}
384   \item The standard inter-atomic forces ($\nabla_iU$) are computed.
# 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 398 | Line 400 | using calls to the qhull library.\cite{Qhull} There is
400   \item Atomic positions and velocities are propagated.
401   \end{enumerate}
402   The Delaunay triangulation and computation of the convex hull are done
403 < using calls to the qhull library.\cite{Qhull} There is a minimal
403 > using calls to the qhull library.\cite{Q_hull} There is a minimal
404   penalty for computing the convex hull and resistance tensors at each
405   step in the molecular dynamics simulation (roughly 0.02 $\times$ cost
406   of a single force evaluation), and the convex hull is remarkably easy
# 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
435 < the middle of the cluster. $N$ is the average number of molecules
434 > The region we used is a spherical volume of 20 \AA\ radius centered in
435 > the middle of the cluster with a roughly 25 \AA\ radius. $N$ 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
438 < cluster can be used to compute the compressibility.
437 > of the region is arbitrary, and any bulk-like portion of the
438 > cluster can be used to compute the compressibility.
439  
440   One might assume that the volume of the convex hull could simply be
441   taken as the system volume $V$ in the compressibility expression
# 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,QSC2}
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
500 < nanoparticles under the Langevin Hull at a variety of applied
501 < pressures ranging from 0 GPa to XXX.  We obtain a value of 177.547 GPa
502 < for the bulk modulus for gold using this echnique.
496 > periodic-boundary simulations of the bulk crystal have yielded values
497 > of 175.53 GPa.\cite{QSC2} Using the same force field, we have performed
498 > a series of 1 ns simulations on gold nanoparticles of three different radii under the Langevin Hull at a variety of applied pressures ranging from 0 -- 10 GPa.  For the 40 \AA~ radius nanoparticle we obtain a value of 177.55 GPa for the bulk modulus of gold, in close agreement with both previous simulations and the experimental bulk modulus reported for gold single crystals.\cite{Collard1991}  Polycrystalline gold has a reported bulk modulus of 220 GPa. The smaller gold nanoparticles (30 and 20 \AA~ radii) have calculated bulk moduli of 215.58 and 208.86 GPa, respectively, indicating that smaller nanoparticles approach the polycrystalline bulk modulus value while larger nanoparticles approach the single crystal value. As nanoparticle size decreases, the bulk modulus becomes larger and the nanoparticle is less compressible. This stiffening of the small nanoparticles may be related to their high degree of surface curvature, resulting in a lower coordination number of surface atoms relative to the the surface atoms in the 40 \AA~ radius particle.
499  
500 + We measure a gold lattice constant of 4.051 \AA~ using the Langevin Hull at 1 atm, close to the experimentally-determined value for bulk gold and the value for gold simulated using the QSC potential and periodic boundary conditions (4.079 \AA~ and 4.088\AA~, respectively).\cite{QSC2} The slightly smaller calculated lattice constant is most likely due to the presence of surface tension in the non-periodic Langevin Hull cluster, an effect absent from a bulk simulation. The specific heat of a 40 \AA~ gold nanoparticle under the Langevin Hull at 1 atm is 24.914 $\mathrm {\frac{J}{mol \, K}}$, which compares very well with the experimental value of 25.42 $\mathrm {\frac{J}{mol \, K}}$.
501 +
502   \begin{figure}
503   \includegraphics[width=\linewidth]{stacked}
504   \caption{The response of the internal pressure and temperature of gold
# Line 508 | Line 506 | for the bulk modulus for gold using this echnique.
506    ($T_\mathrm{bath}$ = 300K, $P_\mathrm{bath}$ = 4 GPa), starting
507    from initial conditions that were far from the bath pressure and
508    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).}
509 < \label{pressureResponse}
509 > \label{fig:pressureResponse}
510   \end{figure}
511  
512 < \begin{equation}
513 < \kappa_T=-\frac{1}{V_{\mathrm{eq}}}\left(\frac{\partial V}{\partial
514 <    P}\right)
515 < \end{equation}
512 > We note that the Langevin Hull produces rapidly-converging behavior
513 > for structures that are started far from equilibrium.  In
514 > Fig. \ref{fig:pressureResponse} we show how the pressure and
515 > temperature respond to the Langevin Hull for nanoparticles that were
516 > initialized far from the target pressure and temperature.  As
517 > expected, the rate at which thermal equilibrium is achieved depends on
518 > the total surface area of the cluster exposed to the bath as well as
519 > the bath viscosity.  Pressure that is applied suddenly to a cluster
520 > can excite breathing vibrations, but these rapidly damp out (on time
521 > scales of 30 -- 50 ps).
522  
523   \subsection{Compressibility of SPC/E water clusters}
524  
# Line 523 | Line 527 | Langevin Hull simulations for pressures between 1 and
527   ensembles) have yielded values for the isothermal compressibility that
528   agree well with experiment.\cite{Fine1973} The results of two
529   different approaches for computing the isothermal compressibility from
530 < Langevin Hull simulations for pressures between 1 and 6500 atm are
530 > Langevin Hull simulations for pressures between 1 and 3000 atm are
531   shown in Fig. \ref{fig:compWater} along with compressibility values
532   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.
533  
534   \begin{figure}
535   \includegraphics[width=\linewidth]{new_isothermalN}
# Line 537 | Line 539 | and previous simulation work throughout the 1 - 1000 a
539  
540   Isothermal compressibility values calculated using the number density
541   (Eq. \ref{eq:BMN}) expression are in good agreement with experimental
542 < and previous simulation work throughout the 1 - 1000 atm pressure
542 > and previous simulation work throughout the 1 -- 1000 atm pressure
543   regime.  Compressibilities computed using the Hull volume, however,
544   deviate dramatically from the experimental values at low applied
545 < pressures.  The reason for this deviation is quite simple; at low
545 > pressures.  The reason for this deviation is quite simple: at low
546   applied pressures, the liquid is in equilibrium with a vapor phase,
547   and it is entirely possible for one (or a few) molecules to drift away
548   from the liquid cluster (see Fig. \ref{fig:coneOfShame}).  At low
# Line 553 | Line 555 | geometries which include large volumes of empty space.
555   \caption{At low pressures, the liquid is in equilibrium with the vapor
556    phase, and isolated molecules can detach from the liquid droplet.
557    This is expected behavior, but the volume of the convex hull
558 <  includes large regions of empty space.  For this reason,
558 >  includes large regions of empty space. For this reason,
559    compressibilities are computed using local number densities rather
560    than hull volumes.}
561   \label{fig:coneOfShame}
# Line 570 | Line 572 | volume,\cite{Debenedetti1986},
572   different pressures must be done to compute the first derivatives.  It
573   is also possible to compute the compressibility using the fluctuation
574   dissipation theorem using either fluctuations in the
575 < volume,\cite{Debenedetti1986},
575 > volume,\cite{Debenedetti1986}
576   \begin{equation}
577   \kappa_{T} = \frac{\left \langle V^{2} \right \rangle - \left \langle
578      V \right \rangle ^{2}}{V \, k_{B} \, T},
579 + \label{eq:BMVfluct}
580   \end{equation}
581   or, equivalently, fluctuations in the number of molecules within the
582   fixed region,
583   \begin{equation}
584   \kappa_{T} = \frac{\left \langle N^{2} \right \rangle - \left \langle
585 <    N \right \rangle ^{2}}{N \, k_{B} \, T},
585 >    N \right \rangle ^{2}}{N \, k_{B} \, T}.
586 > \label{eq:BMNfluct}
587   \end{equation}
588   Thus, the compressibility of each simulation can be calculated
589 < entirely independently from all other trajectories. However, the
590 < resulting compressibilities were still as much as an order of
591 < magnitude larger than the reference values. However, compressibility
592 < calculation that relies on the hull volume will suffer these effects.
593 < WE NEED MORE HERE.
589 > entirely independently from other trajectories.  Compressibility
590 > calculations that rely on the hull volume will still suffer the
591 > effects of the empty space due to the vapor phase; for this reason, we
592 > recommend using the number density (Eq. \ref{eq:BMN}) or number
593 > density fluctuations (Eq. \ref{eq:BMNfluct}) for computing
594 > compressibilities. We achieved the best results using a sampling radius approximately 80\% of the cluster radius. This ratio of sampling radius to cluster radius excludes the problematic vapor phase on the outside of the cluster while including enough of the liquid phase to avoid poor statistics due to fluctuating local densities.
595  
596 + A comparison of the oxygen-oxygen radial distribution functions for SPC/E water simulated using the Langevin Hull and bulk SPC/E using periodic boundary conditions  -- both at 1 atm and 300K -- reveals a slight understructuring of water in the Langevin Hull that manifests as a minor broadening of the solvation shells. This effect may be related to the introduction of surface tension around the entire cluster, an effect absent in bulk systems. As a result, molecules on the hull may experience an increased inward force, slightly compressing the solvation shell structure.
597 +
598   \subsection{Molecular orientation distribution at cluster boundary}
599  
600 < In order for non-periodic boundary conditions to be widely applicable,
601 < they must be constructed in such a way that they allow a finite system
602 < to replicate the properties of the bulk.  Early non-periodic
603 < simulation methods (e.g. hydrophobic boundary potentials) induced
604 < spurious orientational correlations deep within the simulated
600 > In order for a non-periodic boundary method to be widely applicable,
601 > it must be constructed in such a way that they allow a finite system
602 > to replicate the properties of the bulk. Early non-periodic simulation
603 > methods (e.g. hydrophobic boundary potentials) induced spurious
604 > orientational correlations deep within the simulated
605   system.\cite{Lee1984,Belch1985} This behavior spawned many methods for
606 < fixing and characterizing the effects of artifical boundaries
606 > fixing and characterizing the effects of artificial boundaries
607   including methods which fix the orientations of a set of edge
608   molecules.\cite{Warshel1978,King1989}
609  
610   As described above, the Langevin Hull does not require that the
611   orientation of molecules be fixed, nor does it utilize an explicitly
612 < hydrophobic boundary, orientational constraint or radial constraint.
613 < Therefore, the orientational correlations of the molecules in a water
614 < cluster are of particular interest in testing this method.  Ideally,
615 < the water molecules on the surface of the cluster will have enough
616 < mobility into and out of the center of the cluster to maintain a
612 > hydrophobic boundary, or orientational or radial constraints.
613 > Therefore, the orientational correlations of the molecules in water
614 > clusters are of particular interest in testing this method.  Ideally,
615 > the water molecules on the surfaces of the clusters will have enough
616 > mobility into and out of the center of the cluster to maintain
617   bulk-like orientational distribution in the absence of orientational
618   and radial constraints.  However, since the number of hydrogen bonding
619   partners available to molecules on the exterior are limited, it is
620 < likely that there will be some effective hydrophobicity of the hull.
620 > likely that there will be an effective hydrophobicity of the hull.
621  
622 < To determine the extent of these effects demonstrated by the Langevin
623 < Hull, we examined the orientationations exhibited by SPC/E water in a
624 < cluster of 1372 molecules at 300 K and at pressures ranging from 1 -
625 < 1000 atm.  The orientational angle of a water molecule is described
622 > To determine the extent of these effects, we examined the
623 > orientations exhibited by SPC/E water in a cluster of 1372
624 > molecules at 300 K and at pressures ranging from 1 -- 1000 atm.  The
625 > orientational angle of a water molecule is described by
626   \begin{equation}
627   \cos{\theta}=\frac{\vec{r}_i\cdot\vec{\mu}_i}{|\vec{r}_i||\vec{\mu}_i|}
628   \end{equation}
629   where $\vec{r}_{i}$ is the vector between molecule {\it i}'s center of
630 < mass and the cluster center of mass and $\vec{\mu}_{i}$ is the vector
631 < bisecting the H-O-H angle of molecule {\it i} Bulk-like distributions
632 < will result in $\langle \cos \theta \rangle$ values close to zero.  If
633 < the hull exhibits an overabundance of externally-oriented oxygen sites
634 < the average orientation will be negative, while dangling hydrogen
635 < sites will result in positive average orientations.
630 > mass and the cluster center of mass, and $\vec{\mu}_{i}$ is the vector
631 > bisecting the H-O-H angle of molecule {\it i}.  Bulk-like
632 > distributions will result in $\langle \cos \theta \rangle$ values
633 > close to zero.  If the hull exhibits an overabundance of
634 > externally-oriented oxygen sites, the average orientation will be
635 > negative, while dangling hydrogen sites will result in positive
636 > average orientations.
637  
638   Fig. \ref{fig:pAngle} shows the distribution of $\cos{\theta}$ values
639   for molecules in the interior of the cluster (squares) and for
# Line 634 | Line 642 | molecules included in the convex hull (circles).
642   \includegraphics[width=\linewidth]{pAngle}
643   \caption{Distribution of $\cos{\theta}$ values for molecules on the
644    interior of the cluster (squares) and for those participating in the
645 <  convex hull (circles) at a variety of pressures.  The Langevin hull
645 >  convex hull (circles) at a variety of pressures.  The Langevin Hull
646    exhibits minor dewetting behavior with exposed oxygen sites on the
647    hull water molecules.  The orientational preference for exposed
648    oxygen appears to be independent of applied pressure. }
# Line 646 | Line 654 | forming a dangling hydrogen bond acceptor site.
654   orientations. Molecules included in the convex hull show a slight
655   preference for values of $\cos{\theta} < 0.$ These values correspond
656   to molecules with oxygen directed toward the exterior of the cluster,
657 < forming a dangling hydrogen bond acceptor site.
657 > forming dangling hydrogen bond acceptor sites.
658  
659 < In the absence of an electrostatic contribution from the exterior
660 < bath, the orientational distribution of water molecules included in
661 < the Langevin Hull will slightly resemble the distribution at a neat
662 < water liquid/vapor interface.  Previous molecular dynamics simulations
663 < 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.
659 > The orientational preference exhibited by water molecules on the hull
660 > is significantly weaker than the preference caused by an explicit
661 > hydrophobic bounding potential.  Additionally, the Langevin Hull does
662 > not require that the orientation of any molecules be fixed in order to
663 > maintain bulk-like structure, even near the cluster surface.
664  
665 < The orientational preference exhibited by hull molecules in the
666 < Langevin hull is significantly weaker than the preference caused by an
667 < explicit hydrophobic bounding potential.  Additionally, the Langevin
668 < Hull does not require that the orientation of any molecules be fixed
669 < in order to maintain bulk-like structure, even at the cluster surface.
665 > Previous molecular dynamics simulations of SPC/E liquid / vapor
666 > interfaces using periodic boundary conditions have shown that
667 > molecules on the liquid side of interface favor a similar orientation
668 > where oxygen is directed away from the bulk.\cite{Taylor1996} These
669 > simulations had well-defined liquid and vapor phase regions
670 > equilibrium and it was observed that {\it vapor} molecules generally
671 > had one hydrogen protruding from the surface, forming a dangling
672 > hydrogen bond donor. Our water clusters do not have a true vapor
673 > region, but rather a few transient molecules that leave the liquid
674 > droplet (and which return to the droplet relatively quickly).
675 > Although we cannot obtain an orientational preference of vapor phase
676 > molecules in a Langevin Hull simulation, but we do agree with previous
677 > estimates of the orientation of {\it liquid phase} molecules at the
678 > interface.
679  
680   \subsection{Heterogeneous nanoparticle / water mixtures}
681  
682 + To further test the method, we simulated gold nanoparticles ($r = 18$
683 + \AA) solvated by explicit SPC/E water clusters using a model for the
684 + gold / water interactions that has been used by Dou {\it et. al.} for
685 + investigating the separation of water films near hot metal
686 + surfaces.\cite{ISI:000167766600035} The Langevin Hull was used to
687 + sample pressures of 1, 2, 5, 10, 20, 50, 100 and 200 atm, while all
688 + simulations were done at a temperature of 300 K.   At these
689 + temperatures and pressures, there is no observed separation of the
690 + water film from the surface.  
691 +
692 + In Fig. \ref{fig:RhoR} we show the density of water and gold as a
693 + function of the distance from the center of the nanoparticle.  Higher
694 + applied pressures appear to destroy structural correlations in the
695 + outermost monolayer of the gold nanoparticle as well as in the water
696 + at the near the metal / water interface.  Simulations at increased
697 + pressures exhibit significant overlap of the gold and water densities,
698 + indicating a less well-defined interfacial surface.
699 +
700 + \begin{figure}
701 + \includegraphics[width=\linewidth]{RhoR}
702 + \caption{Density profiles of gold and water at the nanoparticle
703 +  surface. Each curve has been normalized by the average density in
704 +  the bulk-like region available to the corresponding material.  Higher applied pressures
705 +  de-structure both the gold nanoparticle surface and water at the
706 +  metal/water interface.}
707 + \label{fig:RhoR}
708 + \end{figure}
709 +
710 + At even higher pressures (500 atm and above), problems with the metal
711 + - water interaction potential became quite clear.  The model we are
712 + using appears to have been parameterized for relatively low pressures;
713 + it utilizes both shifted Morse and repulsive Morse potentials to model
714 + the Au/O and Au/H interactions, respectively.  The repulsive wall of
715 + the Morse potential does not diverge quickly enough at short distances
716 + to prevent water from diffusing into the center of the gold
717 + nanoparticles.  This behavior is likely not a realistic description of
718 + the real physics of the situation.  A better model of the gold-water
719 + adsorption behavior appears to require harder repulsive walls to
720 + prevent this behavior.
721 +
722   \section{Discussion}
723   \label{sec:discussion}
724  
725   The Langevin Hull samples the isobaric-isothermal ensemble for
726 < non-periodic systems by coupling the system to an bath characterized
727 < by pressure, temperature, and solvent viscosity.  This enables the
728 < study of heterogeneous systems composed of materials of significantly
729 < different compressibilities.  Because the boundary is dynamically
730 < determined during the simulation and the molecules interacting with
731 < the boundary can change, the method and has minimal perturbations on
732 < the behavior of molecules at the edges of the simulation.  Further
733 < work on this method will involve implicit electrostatics at the
734 < boundary (which is missing in the current implementation) as well as
735 < more sophisticated treatments of the surface geometry (alpha
726 > non-periodic systems by coupling the system to a bath characterized by
727 > pressure, temperature, and solvent viscosity.  This enables the
728 > simulation of heterogeneous systems composed of materials with
729 > significantly different compressibilities.  Because the boundary is
730 > dynamically determined during the simulation and the molecules
731 > interacting with the boundary can change, the method inflicts minimal
732 > perturbations on the behavior of molecules at the edges of the
733 > simulation.  Further work on this method will involve implicit
734 > electrostatics at the boundary (which is missing in the current
735 > implementation) as well as more sophisticated treatments of the
736 > surface geometry (alpha
737   shapes\cite{EDELSBRUNNER:1994oq,EDELSBRUNNER:1995cj} and Tight
738   Cocone\cite{Dey:2003ts}). The non-convex hull geometries are
739   significantly more expensive ($\mathcal{O}(N^2)$) than the convex hull
# Line 695 | Line 744 | surface, facets, and resistance tensors when the proce
744  
745   In order to use the Langevin Hull for simulations on parallel
746   computers, one of the more difficult tasks is to compute the bounding
747 < surface, facets, and resistance tensors when the processors have
748 < incomplete information about the entire system's topology.  Most
747 > surface, facets, and resistance tensors when the individual processors
748 > have incomplete information about the entire system's topology.  Most
749   parallel decomposition methods assign primary responsibility for the
750   motion of an atomic site to a single processor, and we can exploit
751   this to efficiently compute the convex hull for the entire system.
# Line 734 | Line 783 | hull operations create a set of $p$ hulls each with ap
783   The individual hull operations scale with
784   $\mathcal{O}(\frac{n}{p}\log\frac{n}{p})$ where $n$ is the total
785   number of sites, and $p$ is the number of processors.  These local
786 < hull operations create a set of $p$ hulls each with approximately
787 < $\frac{n}{3pr}$ sites (for a cluster of radius $r$). The worst-case
786 > hull operations create a set of $p$ hulls, each with approximately
787 > $\frac{n}{3pr}$ sites for a cluster of radius $r$. The worst-case
788   communication cost for using a ``gather'' operation to distribute this
789   information to all processors is $\mathcal{O}( \alpha (p-1) + \frac{n
790    \beta (p-1)}{3 r p^2})$, while the final computation of the system
# Line 743 | Line 792 | and communication of these hulls to so the Langevin hu
792  
793   For a large number of atoms on a moderately parallel machine, the
794   total costs are dominated by the computations of the individual hulls,
795 < and communication of these hulls to so the Langevin hull sees roughly
795 > and communication of these hulls to create the Langevin Hull sees roughly
796   linear speed-up with increasing processor counts.
797  
798   \section*{Acknowledgments}

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