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# Line 16 | Line 16 | two decades. Matubayasi and Nakahara developed a time-
16  
17   Integration schemes for rotational motion of the rigid molecules in
18   microcanonical ensemble have been extensively studied in the last
19 < two decades. Matubayasi and Nakahara developed a time-reversible
20 < integrator for rigid bodies in quaternion representation. Although
21 < it is not symplectic, this integrator still demonstrates a better
22 < long-time energy conservation than traditional methods because of
23 < the time-reversible nature. Extending Trotter-Suzuki to general
24 < system with a flat phase space, Miller and his colleagues devised an
25 < novel symplectic, time-reversible and volume-preserving integrator
26 < in quaternion representation, which was shown to be superior to the
27 < time-reversible integrator of Matubayasi and Nakahara. However, all
28 < of the integrators in quaternion representation suffer from the
19 > two decades. Matubayasi developed a time-reversible integrator for
20 > rigid bodies in quaternion representation. Although it is not
21 > symplectic, this integrator still demonstrates a better long-time
22 > energy conservation than traditional methods because of the
23 > time-reversible nature. Extending Trotter-Suzuki to general system
24 > with a flat phase space, Miller and his colleagues devised an novel
25 > symplectic, time-reversible and volume-preserving integrator in
26 > quaternion representation, which was shown to be superior to the
27 > Matubayasi's time-reversible integrator. However, all of the
28 > integrators in quaternion representation suffer from the
29   computational penalty of constructing a rotation matrix from
30   quaternions to evolve coordinates and velocities at every time step.
31   An alternative integration scheme utilizing rotation matrix directly
# Line 117 | Line 117 | torques are calculated at the new positions and orient
117      \cdot {\bf \tau}^s(t + h).
118   \end{align*}
119  
120 < {\sc oopse} automatically updates ${\bf u}$ when the rotation matrix
120 > ${\bf u}$ will be automatically updated when the rotation matrix
121   $\mathsf{A}$ is calculated in {\tt moveA}.  Once the forces and
122   torques have been obtained at the new time step, the velocities can
123   be advanced to the same time value.
# Line 139 | Line 139 | in Fig.~\ref{timestep}.
139   average 7\% increase in computation time using the DLM method in
140   place of quaternions. This cost is more than justified when
141   comparing the energy conservation of the two methods as illustrated
142 < in Fig.~\ref{timestep}.
142 > in Fig.~\ref{methodFig:timestep}.
143  
144   \begin{figure}
145   \centering
# Line 150 | Line 150 | from the true energy baseline for clarity.} \label{tim
150   increasing time step. For each time step, the dotted line is total
151   energy using the DLM integrator, and the solid line comes from the
152   quaternion integrator. The larger time step plots are shifted up
153 < from the true energy baseline for clarity.} \label{timestep}
153 > from the true energy baseline for clarity.}
154 > \label{methodFig:timestep}
155   \end{figure}
156  
157 < In Fig.~\ref{timestep}, the resulting energy drift at various time
158 < steps for both the DLM and quaternion integration schemes is
159 < compared. All of the 1000 molecule water simulations started with
160 < the same configuration, and the only difference was the method for
161 < handling rotational motion. At time steps of 0.1 and 0.5 fs, both
162 < methods for propagating molecule rotation conserve energy fairly
163 < well, with the quaternion method showing a slight energy drift over
164 < time in the 0.5 fs time step simulation. At time steps of 1 and 2
165 < fs, the energy conservation benefits of the DLM method are clearly
166 < demonstrated. Thus, while maintaining the same degree of energy
167 < conservation, one can take considerably longer time steps, leading
168 < to an overall reduction in computation time.
157 > In Fig.~\ref{methodFig:timestep}, the resulting energy drift at
158 > various time steps for both the DLM and quaternion integration
159 > schemes is compared. All of the 1000 molecule water simulations
160 > started with the same configuration, and the only difference was the
161 > method for handling rotational motion. At time steps of 0.1 and 0.5
162 > fs, both methods for propagating molecule rotation conserve energy
163 > fairly well, with the quaternion method showing a slight energy
164 > drift over time in the 0.5 fs time step simulation. At time steps of
165 > 1 and 2 fs, the energy conservation benefits of the DLM method are
166 > clearly demonstrated. Thus, while maintaining the same degree of
167 > energy conservation, one can take considerably longer time steps,
168 > leading to an overall reduction in computation time.
169  
170   \subsection{\label{methodSection:NVT}Nos\'{e}-Hoover Thermostatting}
171  
172 < The Nos\'e-Hoover equations of motion are given by\cite{Hoover85}
172 > The Nos\'e-Hoover equations of motion are given by\cite{Hoover1985}
173   \begin{eqnarray}
174   \dot{{\bf r}} & = & {\bf v}, \\
175   \dot{{\bf v}} & = & \frac{{\bf f}}{m} - \chi {\bf v} ,\\
# Line 205 | Line 206 | relaxation of the temperature to the target value.  To
206   \end{equation}
207  
208   In eq.(\ref{eq:nosehooverext}), $\tau_T$ is the time constant for
209 < relaxation of the temperature to the target value.  To set values
210 < for $\tau_T$ or $T_{\mathrm{target}}$ in a simulation, one would use
211 < the {\tt tauThermostat} and {\tt targetTemperature} keywords in the
211 < {\tt .bass} file.  The units for {\tt tauThermostat} are fs, and the
212 < units for the {\tt targetTemperature} are degrees K.   The
213 < integration of the equations of motion is carried out in a
214 < velocity-Verlet style 2 part algorithm:
209 > relaxation of the temperature to the target value. The integration
210 > of the equations of motion is carried out in a velocity-Verlet style
211 > 2 part algorithm:
212  
213   {\tt moveA:}
214   \begin{align*}
# Line 277 | Line 274 | self-consistent.  The relative tolerance for the self-
274   caclculate $T(t + h)$ as well as $\chi(t + h)$, they indirectly
275   depend on their own values at time $t + h$.  {\tt moveB} is
276   therefore done in an iterative fashion until $\chi(t + h)$ becomes
277 < self-consistent.  The relative tolerance for the self-consistency
281 < check defaults to a value of $\mbox{10}^{-6}$, but {\sc oopse} will
282 < terminate the iteration after 4 loops even if the consistency check
283 < has not been satisfied.
277 > self-consistent.
278  
279   The Nos\'e-Hoover algorithm is known to conserve a Hamiltonian for
280   the extended system that is, to within a constant, identical to the
281 < Helmholtz free energy,\cite{melchionna93}
281 > Helmholtz free energy,\cite{Melchionna1993}
282   \begin{equation}
283   H_{\mathrm{NVT}} = V + K + f k_B T_{\mathrm{target}} \left(
284   \frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime)
# Line 295 | Line 289 | Bond constraints are applied at the end of both the {\
289   last column of the {\tt .stat} file to allow checks on the quality
290   of the integration.
291  
298 Bond constraints are applied at the end of both the {\tt moveA} and
299 {\tt moveB} portions of the algorithm.  Details on the constraint
300 algorithms are given in section \ref{oopseSec:rattle}.
301
292   \subsection{\label{methodSection:NPTi}Constant-pressure integration with
293   isotropic box deformations (NPTi)}
294  
295 < To carry out isobaric-isothermal ensemble calculations {\sc oopse}
296 < implements the Melchionna modifications to the
297 < Nos\'e-Hoover-Andersen equations of motion,\cite{melchionna93}
295 > Isobaric-isothermal ensemble integrator is implemented using the
296 > Melchionna modifications to the Nos\'e-Hoover-Andersen equations of
297 > motion,\cite{Melchionna1993}
298  
299   \begin{eqnarray}
300   \dot{{\bf r}} & = & {\bf v} + \eta \left( {\bf r} - {\bf R}_0 \right), \\
# Line 359 | Line 349 | relaxation of the pressure to the target value.  To se
349   \end{equation}
350  
351   In eq.(\ref{eq:melchionna1}), $\tau_B$ is the time constant for
352 < relaxation of the pressure to the target value.  To set values for
363 < $\tau_B$ or $P_{\mathrm{target}}$ in a simulation, one would use the
364 < {\tt tauBarostat} and {\tt targetPressure} keywords in the {\tt
365 < .bass} file.  The units for {\tt tauBarostat} are fs, and the units
366 < for the {\tt targetPressure} are atmospheres.  Like in the NVT
352 > relaxation of the pressure to the target value. Like in the NVT
353   integrator, the integration of the equations of motion is carried
354   out in a velocity-Verlet style 2 part algorithm:
355  
# Line 404 | Line 390 | depends on the positions at the same time.  {\sc oopse
390  
391   Most of these equations are identical to their counterparts in the
392   NVT integrator, but the propagation of positions to time $t + h$
393 < depends on the positions at the same time.  {\sc oopse} carries out
394 < this step iteratively (with a limit of 5 passes through the
395 < iterative loop).  Also, the simulation box $\mathsf{H}$ is scaled
396 < uniformly for one full time step by an exponential factor that
397 < depends on the value of $\eta$ at time $t + h / 2$.  Reshaping the
412 < box uniformly also scales the volume of the box by
393 > depends on the positions at the same time. The simulation box
394 > $\mathsf{H}$ is scaled uniformly for one full time step by an
395 > exponential factor that depends on the value of $\eta$ at time $t +
396 > h / 2$.  Reshaping the box uniformly also scales the volume of the
397 > box by
398   \begin{equation}
399   \mathcal{V}(t + h) \leftarrow e^{ - 3 h \eta(t + h /2)}.
400   \mathcal{V}(t)
# Line 451 | Line 436 | + h)$ and $\eta(t + h)$ become self-consistent.  The r
436   to caclculate $T(t + h)$, $P(t + h)$, $\chi(t + h)$, and $\eta(t +
437   h)$, they indirectly depend on their own values at time $t + h$.
438   {\tt moveB} is therefore done in an iterative fashion until $\chi(t
439 < + h)$ and $\eta(t + h)$ become self-consistent.  The relative
455 < tolerance for the self-consistency check defaults to a value of
456 < $\mbox{10}^{-6}$, but {\sc oopse} will terminate the iteration after
457 < 4 loops even if the consistency check has not been satisfied.
439 > + h)$ and $\eta(t + h)$ become self-consistent.
440  
441   The Melchionna modification of the Nos\'e-Hoover-Andersen algorithm
442   is known to conserve a Hamiltonian for the extended system that is,
# Line 475 | Line 457 | P_{\mathrm{target}} \mathcal{V}(t).
457   \left( \chi^2 \tau_T^2 + \eta^2 \tau_B^2 \right) +
458   P_{\mathrm{target}} \mathcal{V}(t).
459   \end{equation}
478
479 Bond constraints are applied at the end of both the {\tt moveA} and
480 {\tt moveB} portions of the algorithm.  Details on the constraint
481 algorithms are given in section \ref{oopseSec:rattle}.
460  
461   \subsection{\label{methodSection:NPTf}Constant-pressure integration with a
462   flexible box (NPTf)}
# Line 553 | Line 531 | r}(t)\right\},
531   \mathsf{H}(t + h) &\leftarrow \mathsf{H}(t) \cdot e^{-h
532      \overleftrightarrow{\eta}(t + h / 2)} .
533   \end{align*}
534 < {\sc oopse} uses a power series expansion truncated at second order
535 < for the exponential operation which scales the simulation box.
534 > Here, a power series expansion truncated at second order for the
535 > exponential operation is used to scale the simulation box.
536  
537   The {\tt moveB} portion of the algorithm is largely unchanged from
538   the NPTi integrator:
# Line 593 | Line 571 | The NPTf integrator is known to conserve the following
571   identical to those described for the NPTi integrator.
572  
573   The NPTf integrator is known to conserve the following Hamiltonian:
574 < \begin{equation}
575 < H_{\mathrm{NPTf}} = V + K + f k_B T_{\mathrm{target}} \left(
574 > \begin{eqnarray*}
575 > H_{\mathrm{NPTf}} & = & V + K + f k_B T_{\mathrm{target}} \left(
576   \frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime)
577 < dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B
577 > dt^\prime \right) \\
578 > & & + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B
579   T_{\mathrm{target}}}{2}
580   \mathrm{Tr}\left[\overleftrightarrow{\eta}(t)\right]^2 \tau_B^2.
581 < \end{equation}
581 > \end{eqnarray*}
582  
583   This integrator must be used with care, particularly in liquid
584   simulations.  Liquids have very small restoring forces in the
# Line 609 | Line 588 | assume non-orthorhombic geometries.
588   finds most use in simulating crystals or liquid crystals which
589   assume non-orthorhombic geometries.
590  
591 < \subsection{\label{methodSection:otherSpecialEnsembles}Other Special Ensembles}
591 > \subsection{\label{methodSection:NPAT}NPAT Ensemble}
592  
614 \subsubsection{\label{methodSection:NPAT}NPAT Ensemble}
615
593   A comprehensive understanding of structure¨Cfunction relations of
594   biological membrane system ultimately relies on structure and
595   dynamics of lipid bilayer, which are strongly affected by the
# Line 621 | Line 598 | standard NPT ensemble with a different pressure contro
598   called the average surface area per lipid. Constat area and constant
599   lateral pressure simulation can be achieved by extending the
600   standard NPT ensemble with a different pressure control strategy
601 +
602   \begin{equation}
603 < \dot
604 < \mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}}
605 < \over \eta } _{\alpha \beta }  = \left\{ \begin{array}{l}
606 < \frac{{V(P_{\alpha \beta }  - P_{{\rm{target}}} )}}{{\tau _{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }}{\rm{   }}(\alpha  = \beta  = z), \\
607 < 0{\rm{                        }}(\alpha  \ne z{\rm{ }}or{\rm{ }}\beta  \ne z) \\
608 < \end{array} \right.
631 < \label{methodEquation:NPATeta}
603 > \dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll}
604 >                  \frac{{V(P_{\alpha \beta }  - P_{{\rm{target}}} )}}{{\tau_{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }}
605 >                  & \mbox{if $ \alpha = \beta  = z$}\\
606 >                  0 & \mbox{otherwise}\\
607 >           \end{array}
608 >    \right.
609   \end{equation}
610 +
611   Note that the iterative schemes for NPAT are identical to those
612   described for the NPTi integrator.
613  
614 < \subsubsection{\label{methodSection:NPrT}NP$\gamma$T Ensemble}
614 > \subsection{\label{methodSection:NPrT}NP$\gamma$T
615 > Ensemble}
616  
617   Theoretically, the surface tension $\gamma$ of a stress free
618   membrane system should be zero since its surface free energy $G$ is
# Line 646 | Line 625 | $\eta$, in NP\gamma T is
625   the membrane simulation, a special ensemble, NP$\gamma$T, is
626   proposed to maintain the lateral surface tension and normal
627   pressure. The equation of motion for cell size control tensor,
628 < $\eta$, in NP\gamma T is
628 > $\eta$, in $NP\gamma T$ is
629   \begin{equation}
630 < \dot
631 < \mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}}
632 < \over \eta } _{\alpha \beta }  = \left\{ \begin{array}{l}
633 <  - A_{xy} (\gamma _\alpha   - \gamma _{{\rm{target}}} ){\rm{ (}}\alpha  = \beta  = x{\rm{ or }} = y{\rm{)}} \\
634 < \frac{{V(P_{\alpha \beta }  - P_{{\rm{target}}} )}}{{\tau _{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }}{\rm{   }}(\alpha  = \beta  = z) \\
635 < 0{\rm{                         }}(\alpha  \ne \beta ) \\
657 < \end{array} \right.
658 < \label{methodEquation:NPrTeta}
630 > \dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll}
631 >    - A_{xy} (\gamma _\alpha   - \gamma _{{\rm{target}}} ) & \mbox{$\alpha  = \beta  = x$ or $\alpha  = \beta  = y$}\\
632 >    \frac{{V(P_{\alpha \beta }  - P_{{\rm{target}}} )}}{{\tau _{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}}}} & \mbox{$\alpha  = \beta  = z$} \\
633 >    0 & \mbox{$\alpha  \ne \beta$} \\
634 >       \end{array}
635 >    \right.
636   \end{equation}
637   where $ \gamma _{{\rm{target}}}$ is the external surface tension and
638   the instantaneous surface tensor $\gamma _\alpha$ is given by
639   \begin{equation}
640 < \gamma _\alpha   =  - h_z
641 < (\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}}
665 < \over P} _{\alpha \alpha }  - P_{{\rm{target}}} )
640 > \gamma _\alpha   =  - h_z( \overleftrightarrow{P} _{\alpha \alpha }
641 > - P_{{\rm{target}}} )
642   \label{methodEquation:instantaneousSurfaceTensor}
643   \end{equation}
644  
# Line 674 | Line 650 | $\gamma$ is set to zero.
650   integrator is a special case of $NP\gamma T$ if the surface tension
651   $\gamma$ is set to zero.
652  
653 < %\section{\label{methodSection:constraintMethod}Constraint Method}
653 > \section{\label{methodSection:zcons}Z-Constraint Method}
654  
655 < %\subsection{\label{methodSection:bondConstraint}Bond Constraint for Rigid Body}
655 > Based on the fluctuation-dissipation theorem, a force
656 > auto-correlation method was developed by Roux and Karplus to
657 > investigate the dynamics of ions inside ion channels\cite{Roux1991}.
658 > The time-dependent friction coefficient can be calculated from the
659 > deviation of the instantaneous force from its mean force.
660 > \begin{equation}
661 > \xi(z,t)=\langle\delta F(z,t)\delta F(z,0)\rangle/k_{B}T,
662 > \end{equation}
663 > where%
664 > \begin{equation}
665 > \delta F(z,t)=F(z,t)-\langle F(z,t)\rangle.
666 > \end{equation}
667  
668 < %\subsection{\label{methodSection:zcons}Z-constraint Method}
668 > If the time-dependent friction decays rapidly, the static friction
669 > coefficient can be approximated by
670 > \begin{equation}
671 > \xi_{\text{static}}(z)=\int_{0}^{\infty}\langle\delta F(z,t)\delta
672 > F(z,0)\rangle dt.
673 > \end{equation}
674 > Allowing diffusion constant to then be calculated through the
675 > Einstein relation:\cite{Marrink1994}
676 > \begin{equation}
677 > D(z)=\frac{k_{B}T}{\xi_{\text{static}}(z)}=\frac{(k_{B}T)^{2}}{\int_{0}^{\infty
678 > }\langle\delta F(z,t)\delta F(z,0)\rangle dt}.%
679 > \end{equation}
680  
681 < \section{\label{methodSection:langevin}Integrators for Langevin Dynamics of Rigid Bodies}
681 > The Z-Constraint method, which fixes the z coordinates of the
682 > molecules with respect to the center of the mass of the system, has
683 > been a method suggested to obtain the forces required for the force
684 > auto-correlation calculation.\cite{Marrink1994} However, simply
685 > resetting the coordinate will move the center of the mass of the
686 > whole system. To avoid this problem, we reset the forces of
687 > z-constrained molecules as well as subtract the total constraint
688 > forces from the rest of the system after the force calculation at
689 > each time step instead of resetting the coordinate.
690  
691 < \subsection{\label{methodSection:temperature}Temperature Control}
691 > After the force calculation, define $G_\alpha$ as
692 > \begin{equation}
693 > G_{\alpha} = \sum_i F_{\alpha i}, \label{oopseEq:zc1}
694 > \end{equation}
695 > where $F_{\alpha i}$ is the force in the z direction of atom $i$ in
696 > z-constrained molecule $\alpha$. The forces of the z constrained
697 > molecule are then set to:
698 > \begin{equation}
699 > F_{\alpha i} = F_{\alpha i} -
700 >    \frac{m_{\alpha i} G_{\alpha}}{\sum_i m_{\alpha i}}.
701 > \end{equation}
702 > Here, $m_{\alpha i}$ is the mass of atom $i$ in the z-constrained
703 > molecule. Having rescaled the forces, the velocities must also be
704 > rescaled to subtract out any center of mass velocity in the z
705 > direction.
706 > \begin{equation}
707 > v_{\alpha i} = v_{\alpha i} -
708 >    \frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}},
709 > \end{equation}
710 > where $v_{\alpha i}$ is the velocity of atom $i$ in the z direction.
711 > Lastly, all of the accumulated z constrained forces must be
712 > subtracted from the system to keep the system center of mass from
713 > drifting.
714 > \begin{equation}
715 > F_{\beta i} = F_{\beta i} - \frac{m_{\beta i} \sum_{\alpha}
716 > G_{\alpha}}
717 >    {\sum_{\beta}\sum_i m_{\beta i}},
718 > \end{equation}
719 > where $\beta$ are all of the unconstrained molecules in the system.
720 > Similarly, the velocities of the unconstrained molecules must also
721 > be scaled.
722 > \begin{equation}
723 > v_{\beta i} = v_{\beta i} + \sum_{\alpha}
724 >    \frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}.
725 > \end{equation}
726  
727 < \subsection{\label{methodSection:pressureControl}Pressure Control}
728 <
729 < \section{\label{methodSection:hydrodynamics}Hydrodynamics}
730 <
731 < %\section{\label{methodSection:coarseGrained}Coarse-Grained Modeling}
732 <
733 < %\section{\label{methodSection:moleculeScale}Molecular-Scale Modeling}
727 > At the very beginning of the simulation, the molecules may not be at
728 > their constrained positions. To move a z-constrained molecule to its
729 > specified position, a simple harmonic potential is used
730 > \begin{equation}
731 > U(t)=\frac{1}{2}k_{\text{Harmonic}}(z(t)-z_{\text{cons}})^{2},%
732 > \end{equation}
733 > where $k_{\text{Harmonic}}$ is the harmonic force constant, $z(t)$
734 > is the current $z$ coordinate of the center of mass of the
735 > constrained molecule, and $z_{\text{cons}}$ is the constrained
736 > position. The harmonic force operating on the z-constrained molecule
737 > at time $t$ can be calculated by
738 > \begin{equation}
739 > F_{z_{\text{Harmonic}}}(t)=-\frac{\partial U(t)}{\partial z(t)}=
740 >    -k_{\text{Harmonic}}(z(t)-z_{\text{cons}}).
741 > \end{equation}

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