2 |
|
|
3 |
|
\section{\label{methodSection:rigidBodyIntegrators}Integrators for Rigid Body Motion in Molecular Dynamics} |
4 |
|
|
5 |
< |
In order to mimic the experiments, which are usually performed under |
5 |
> |
In order to mimic experiments which are usually performed under |
6 |
|
constant temperature and/or pressure, extended Hamiltonian system |
7 |
|
methods have been developed to generate statistical ensembles, such |
8 |
< |
as canonical ensemble and isobaric-isothermal ensemble \textit{etc}. |
9 |
< |
In addition to the standard ensemble, specific ensembles have been |
10 |
< |
developed to account for the anisotropy between the lateral and |
11 |
< |
normal directions of membranes. The $NPAT$ ensemble, in which the |
12 |
< |
normal pressure and the lateral surface area of the membrane are |
13 |
< |
kept constant, and the $NP\gamma T$ ensemble, in which the normal |
14 |
< |
pressure and the lateral surface tension are kept constant were |
15 |
< |
proposed to address this issue. |
8 |
> |
as the canonical and isobaric-isothermal ensembles. In addition to |
9 |
> |
the standard ensemble, specific ensembles have been developed to |
10 |
> |
account for the anisotropy between the lateral and normal directions |
11 |
> |
of membranes. The $NPAT$ ensemble, in which the normal pressure and |
12 |
> |
the lateral surface area of the membrane are kept constant, and the |
13 |
> |
$NP\gamma T$ ensemble, in which the normal pressure and the lateral |
14 |
> |
surface tension are kept constant were proposed to address the |
15 |
> |
issues. |
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 |
17 |
> |
Integration schemes for the rotational motion of the rigid molecules |
18 |
> |
in the microcanonical ensemble have been extensively studied over |
19 |
> |
the last two decades. Matubayasi 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 |
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 |
32 |
< |
proposed by Dullweber, Leimkuhler and McLachlan (DLM) also preserved |
33 |
< |
the same structural properties of the Hamiltonian flow. In this |
34 |
< |
section, the integration scheme of DLM method will be reviewed and |
35 |
< |
extended to other ensembles. |
22 |
> |
long-time energy conservation than Euler angle methods because of |
23 |
> |
the time-reversible nature. Extending the Trotter-Suzuki |
24 |
> |
factorization to general system with a flat phase space, Miller and |
25 |
> |
his colleagues devised a novel symplectic, time-reversible and |
26 |
> |
volume-preserving integrator in the quaternion representation, which |
27 |
> |
was shown to be superior to the Matubayasi's time-reversible |
28 |
> |
integrator. However, all of the integrators in the quaternion |
29 |
> |
representation suffer from the computational penalty of constructing |
30 |
> |
a rotation matrix from quaternions to evolve coordinates and |
31 |
> |
velocities at every time step. An alternative integration scheme |
32 |
> |
utilizing the rotation matrix directly proposed by Dullweber, |
33 |
> |
Leimkuhler and McLachlan (DLM) also preserved the same structural |
34 |
> |
properties of the Hamiltonian flow. In this section, the integration |
35 |
> |
scheme of DLM method will be reviewed and extended to other |
36 |
> |
ensembles. |
37 |
|
|
38 |
|
\subsection{\label{methodSection:DLM}Integrating the Equations of Motion: the |
39 |
|
DLM method} |
112 |
|
- \left(\frac{\partial V}{\partial {\bf r}}\right)_{{\bf r}(t + h)}, \\ |
113 |
|
% |
114 |
|
{\bf \tau}^{s}(t + h) &\leftarrow {\bf u}(t + h) |
115 |
< |
\times \frac{\partial V}{\partial {\bf u}}, \\ |
115 |
> |
\times (\frac{\partial V}{\partial {\bf u}})_{u(t+h)}, \\ |
116 |
|
% |
117 |
|
{\bf \tau}^{b}(t + h) &\leftarrow \mathsf{A}(t + h) |
118 |
|
\cdot {\bf \tau}^s(t + h). |
119 |
|
\end{align*} |
120 |
|
|
121 |
< |
{\sc oopse} automatically updates ${\bf u}$ when the rotation matrix |
121 |
> |
${\bf u}$ is automatically updated when the rotation matrix |
122 |
|
$\mathsf{A}$ is calculated in {\tt moveA}. Once the forces and |
123 |
|
torques have been obtained at the new time step, the velocities can |
124 |
|
be advanced to the same time value. |
140 |
|
average 7\% increase in computation time using the DLM method in |
141 |
|
place of quaternions. This cost is more than justified when |
142 |
|
comparing the energy conservation of the two methods as illustrated |
143 |
< |
in Fig.~\ref{timestep}. |
143 |
> |
in Fig.~\ref{methodFig:timestep}. |
144 |
|
|
145 |
|
\begin{figure} |
146 |
|
\centering |
151 |
|
increasing time step. For each time step, the dotted line is total |
152 |
|
energy using the DLM integrator, and the solid line comes from the |
153 |
|
quaternion integrator. The larger time step plots are shifted up |
154 |
< |
from the true energy baseline for clarity.} \label{timestep} |
154 |
> |
from the true energy baseline for clarity.} |
155 |
> |
\label{methodFig:timestep} |
156 |
|
\end{figure} |
157 |
|
|
158 |
< |
In Fig.~\ref{timestep}, the resulting energy drift at various time |
159 |
< |
steps for both the DLM and quaternion integration schemes is |
160 |
< |
compared. All of the 1000 molecule water simulations started with |
161 |
< |
the same configuration, and the only difference was the method for |
162 |
< |
handling rotational motion. At time steps of 0.1 and 0.5 fs, both |
163 |
< |
methods for propagating molecule rotation conserve energy fairly |
164 |
< |
well, with the quaternion method showing a slight energy drift over |
165 |
< |
time in the 0.5 fs time step simulation. At time steps of 1 and 2 |
166 |
< |
fs, the energy conservation benefits of the DLM method are clearly |
167 |
< |
demonstrated. Thus, while maintaining the same degree of energy |
168 |
< |
conservation, one can take considerably longer time steps, leading |
169 |
< |
to an overall reduction in computation time. |
158 |
> |
In Fig.~\ref{methodFig:timestep}, the resulting energy drift at |
159 |
> |
various time steps for both the DLM and quaternion integration |
160 |
> |
schemes is compared. All of the 1000 molecule water simulations |
161 |
> |
started with the same configuration, and the only difference was the |
162 |
> |
method for handling rotational motion. At time steps of 0.1 and 0.5 |
163 |
> |
fs, both methods for propagating molecule rotation conserve energy |
164 |
> |
fairly well, with the quaternion method showing a slight energy |
165 |
> |
drift over time in the 0.5 fs time step simulation. At time steps of |
166 |
> |
1 and 2 fs, the energy conservation benefits of the DLM method are |
167 |
> |
clearly demonstrated. Thus, while maintaining the same degree of |
168 |
> |
energy conservation, one can take considerably longer time steps, |
169 |
> |
leading to an overall reduction in computation time. |
170 |
|
|
171 |
|
\subsection{\label{methodSection:NVT}Nos\'{e}-Hoover Thermostatting} |
172 |
|
|
173 |
< |
The Nos\'e-Hoover equations of motion are given by\cite{Hoover85} |
173 |
> |
The Nos\'e-Hoover equations of motion are given by\cite{Hoover1985} |
174 |
|
\begin{eqnarray} |
175 |
|
\dot{{\bf r}} & = & {\bf v}, \\ |
176 |
|
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - \chi {\bf v} ,\\ |
199 |
|
\begin{equation} |
200 |
|
f = 3 N + 3 N_{\mathrm{orient}} - N_{\mathrm{constraints}}, |
201 |
|
\end{equation} |
202 |
< |
and $K$ is the total kinetic energy, |
202 |
> |
where $N_{\mathrm{orient}}$ is the number of molecules with |
203 |
> |
orientational degrees of freedom, and $K$ is the total kinetic |
204 |
> |
energy, |
205 |
|
\begin{equation} |
206 |
|
K = \sum_{i=1}^{N} \frac{1}{2} m_i {\bf v}_i^T \cdot {\bf v}_i + |
207 |
|
\sum_{i=1}^{N_{\mathrm{orient}}} \frac{1}{2} {\bf j}_i^T \cdot |
209 |
|
\end{equation} |
210 |
|
|
211 |
|
In eq.(\ref{eq:nosehooverext}), $\tau_T$ is the time constant for |
212 |
< |
relaxation of the temperature to the target value. To set values |
213 |
< |
for $\tau_T$ or $T_{\mathrm{target}}$ in a simulation, one would use |
214 |
< |
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: |
212 |
> |
relaxation of the temperature to the target value. The integration |
213 |
> |
of the equations of motion is carried out in a velocity-Verlet style |
214 |
> |
2 part algorithm: |
215 |
|
|
216 |
|
{\tt moveA:} |
217 |
|
\begin{align*} |
277 |
|
caclculate $T(t + h)$ as well as $\chi(t + h)$, they indirectly |
278 |
|
depend on their own values at time $t + h$. {\tt moveB} is |
279 |
|
therefore done in an iterative fashion until $\chi(t + h)$ becomes |
280 |
< |
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. |
280 |
> |
self-consistent. |
281 |
|
|
282 |
|
The Nos\'e-Hoover algorithm is known to conserve a Hamiltonian for |
283 |
|
the extended system that is, to within a constant, identical to the |
284 |
< |
Helmholtz free energy,\cite{melchionna93} |
284 |
> |
Helmholtz free energy,\cite{Melchionna1993} |
285 |
|
\begin{equation} |
286 |
|
H_{\mathrm{NVT}} = V + K + f k_B T_{\mathrm{target}} \left( |
287 |
|
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
292 |
|
last column of the {\tt .stat} file to allow checks on the quality |
293 |
|
of the integration. |
294 |
|
|
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 |
– |
|
295 |
|
\subsection{\label{methodSection:NPTi}Constant-pressure integration with |
296 |
|
isotropic box deformations (NPTi)} |
297 |
|
|
298 |
< |
To carry out isobaric-isothermal ensemble calculations {\sc oopse} |
299 |
< |
implements the Melchionna modifications to the |
300 |
< |
Nos\'e-Hoover-Andersen equations of motion,\cite{melchionna93} |
298 |
> |
We can used an isobaric-isothermal ensemble integrator which is |
299 |
> |
implemented using the Melchionna modifications to the |
300 |
> |
Nos\'e-Hoover-Andersen equations of motion,\cite{Melchionna1993} |
301 |
|
|
302 |
|
\begin{eqnarray} |
303 |
|
\dot{{\bf r}} & = & {\bf v} + \eta \left( {\bf r} - {\bf R}_0 \right), \\ |
352 |
|
\end{equation} |
353 |
|
|
354 |
|
In eq.(\ref{eq:melchionna1}), $\tau_B$ is the time constant for |
355 |
< |
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 |
355 |
> |
relaxation of the pressure to the target value. Like in the NVT |
356 |
|
integrator, the integration of the equations of motion is carried |
357 |
|
out in a velocity-Verlet style 2 part algorithm: |
358 |
|
|
393 |
|
|
394 |
|
Most of these equations are identical to their counterparts in the |
395 |
|
NVT integrator, but the propagation of positions to time $t + h$ |
396 |
< |
depends on the positions at the same time. {\sc oopse} carries out |
397 |
< |
this step iteratively (with a limit of 5 passes through the |
398 |
< |
iterative loop). Also, the simulation box $\mathsf{H}$ is scaled |
399 |
< |
uniformly for one full time step by an exponential factor that |
400 |
< |
depends on the value of $\eta$ at time $t + h / 2$. Reshaping the |
412 |
< |
box uniformly also scales the volume of the box by |
396 |
> |
depends on the positions at the same time. The simulation box |
397 |
> |
$\mathsf{H}$ is scaled uniformly for one full time step by an |
398 |
> |
exponential factor that depends on the value of $\eta$ at time $t + |
399 |
> |
h / 2$. Reshaping the box uniformly also scales the volume of the |
400 |
> |
box by |
401 |
|
\begin{equation} |
402 |
|
\mathcal{V}(t + h) \leftarrow e^{ - 3 h \eta(t + h /2)}. |
403 |
|
\mathcal{V}(t) |
439 |
|
to caclculate $T(t + h)$, $P(t + h)$, $\chi(t + h)$, and $\eta(t + |
440 |
|
h)$, they indirectly depend on their own values at time $t + h$. |
441 |
|
{\tt moveB} is therefore done in an iterative fashion until $\chi(t |
442 |
< |
+ h)$ 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. |
442 |
> |
+ h)$ and $\eta(t + h)$ become self-consistent. |
443 |
|
|
444 |
|
The Melchionna modification of the Nos\'e-Hoover-Andersen algorithm |
445 |
|
is known to conserve a Hamiltonian for the extended system that is, |
461 |
|
P_{\mathrm{target}} \mathcal{V}(t). |
462 |
|
\end{equation} |
463 |
|
|
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}. |
482 |
– |
|
464 |
|
\subsection{\label{methodSection:NPTf}Constant-pressure integration with a |
465 |
|
flexible box (NPTf)} |
466 |
|
|
534 |
|
\mathsf{H}(t + h) &\leftarrow \mathsf{H}(t) \cdot e^{-h |
535 |
|
\overleftrightarrow{\eta}(t + h / 2)} . |
536 |
|
\end{align*} |
537 |
< |
{\sc oopse} uses a power series expansion truncated at second order |
538 |
< |
for the exponential operation which scales the simulation box. |
537 |
> |
Here, a power series expansion truncated at second order for the |
538 |
> |
exponential operation is used to scale the simulation box. |
539 |
|
|
540 |
|
The {\tt moveB} portion of the algorithm is largely unchanged from |
541 |
|
the NPTi integrator: |
574 |
|
identical to those described for the NPTi integrator. |
575 |
|
|
576 |
|
The NPTf integrator is known to conserve the following Hamiltonian: |
577 |
< |
\begin{equation} |
578 |
< |
H_{\mathrm{NPTf}} = V + K + f k_B T_{\mathrm{target}} \left( |
577 |
> |
\begin{eqnarray*} |
578 |
> |
H_{\mathrm{NPTf}} & = & V + K + f k_B T_{\mathrm{target}} \left( |
579 |
|
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
580 |
< |
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B |
580 |
> |
dt^\prime \right) \\ |
581 |
> |
& & + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B |
582 |
|
T_{\mathrm{target}}}{2} |
583 |
|
\mathrm{Tr}\left[\overleftrightarrow{\eta}(t)\right]^2 \tau_B^2. |
584 |
< |
\end{equation} |
584 |
> |
\end{eqnarray*} |
585 |
|
|
586 |
|
This integrator must be used with care, particularly in liquid |
587 |
|
simulations. Liquids have very small restoring forces in the |
591 |
|
finds most use in simulating crystals or liquid crystals which |
592 |
|
assume non-orthorhombic geometries. |
593 |
|
|
594 |
< |
\subsection{\label{methodSection:otherSpecialEnsembles}Other Special Ensembles} |
594 |
> |
\subsection{\label{methodSection:NPAT}NPAT Ensemble} |
595 |
|
|
596 |
< |
\subsubsection{\label{methodSection:NPAT}NPAT Ensemble} |
596 |
> |
A comprehensive understanding of relations between structures and |
597 |
> |
functions in biological membrane system ultimately relies on |
598 |
> |
structure and dynamics of lipid bilayers, which are strongly |
599 |
> |
affected by the interfacial interaction between lipid molecules and |
600 |
> |
surrounding media. One quantity to describe the interfacial |
601 |
> |
interaction is so called the average surface area per lipid. |
602 |
> |
Constant area and constant lateral pressure simulations can be |
603 |
> |
achieved by extending the standard NPT ensemble with a different |
604 |
> |
pressure control strategy |
605 |
|
|
616 |
– |
A comprehensive understanding of structure¨Cfunction relations of |
617 |
– |
biological membrane system ultimately relies on structure and |
618 |
– |
dynamics of lipid bilayer, which are strongly affected by the |
619 |
– |
interfacial interaction between lipid molecules and surrounding |
620 |
– |
media. One quantity to describe the interfacial interaction is so |
621 |
– |
called the average surface area per lipid. Constat area and constant |
622 |
– |
lateral pressure simulation can be achieved by extending the |
623 |
– |
standard NPT ensemble with a different pressure control strategy |
624 |
– |
|
606 |
|
\begin{equation} |
607 |
|
\dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll} |
608 |
|
\frac{{V(P_{\alpha \beta } - P_{{\rm{target}}} )}}{{\tau_{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }} |
615 |
|
Note that the iterative schemes for NPAT are identical to those |
616 |
|
described for the NPTi integrator. |
617 |
|
|
618 |
< |
\subsubsection{\label{methodSection:NPrT}NP$\gamma$T Ensemble} |
618 |
> |
\subsection{\label{methodSection:NPrT}NP$\gamma$T |
619 |
> |
Ensemble} |
620 |
|
|
621 |
|
Theoretically, the surface tension $\gamma$ of a stress free |
622 |
|
membrane system should be zero since its surface free energy $G$ is |
626 |
|
\] |
627 |
|
However, a surface tension of zero is not appropriate for relatively |
628 |
|
small patches of membrane. In order to eliminate the edge effect of |
629 |
< |
the membrane simulation, a special ensemble, NP$\gamma$T, is |
629 |
> |
the membrane simulation, a special ensemble, NP$\gamma$T, has been |
630 |
|
proposed to maintain the lateral surface tension and normal |
631 |
< |
pressure. The equation of motion for cell size control tensor, |
631 |
> |
pressure. The equation of motion for the cell size control tensor, |
632 |
|
$\eta$, in $NP\gamma T$ is |
633 |
|
\begin{equation} |
634 |
|
\dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll} |
641 |
|
where $ \gamma _{{\rm{target}}}$ is the external surface tension and |
642 |
|
the instantaneous surface tensor $\gamma _\alpha$ is given by |
643 |
|
\begin{equation} |
644 |
< |
\gamma _\alpha = - h_z |
645 |
< |
(\mathord{\buildrel{\lower3pt\hbox{$\scriptscriptstyle\leftrightarrow$}} |
664 |
< |
\over P} _{\alpha \alpha } - P_{{\rm{target}}} ) |
644 |
> |
\gamma _\alpha = - h_z( \overleftrightarrow{P} _{\alpha \alpha } |
645 |
> |
- P_{{\rm{target}}} ) |
646 |
|
\label{methodEquation:instantaneousSurfaceTensor} |
647 |
|
\end{equation} |
648 |
|
|
652 |
|
axes are allowed to fluctuate independently, but the angle between |
653 |
|
the box axes does not change. It should be noted that the NPTxyz |
654 |
|
integrator is a special case of $NP\gamma T$ if the surface tension |
655 |
< |
$\gamma$ is set to zero. |
655 |
> |
$\gamma$ is set to zero, and if $x$ and $y$ can move independently. |
656 |
|
|
657 |
< |
%\section{\label{methodSection:constraintMethod}Constraint Method} |
657 |
> |
\section{\label{methodSection:zcons}The Z-Constraint Method} |
658 |
|
|
659 |
< |
%\subsection{\label{methodSection:bondConstraint}Bond Constraint for Rigid Body} |
659 |
> |
Based on the fluctuation-dissipation theorem, a force |
660 |
> |
auto-correlation method was developed by Roux and Karplus to |
661 |
> |
investigate the dynamics of ions inside ion channels\cite{Roux1991}. |
662 |
> |
The time-dependent friction coefficient can be calculated from the |
663 |
> |
deviation of the instantaneous force from its mean force. |
664 |
> |
\begin{equation} |
665 |
> |
\xi(z,t)=\langle\delta F(z,t)\delta F(z,0)\rangle/k_{B}T, |
666 |
> |
\end{equation} |
667 |
> |
where% |
668 |
> |
\begin{equation} |
669 |
> |
\delta F(z,t)=F(z,t)-\langle F(z,t)\rangle. |
670 |
> |
\end{equation} |
671 |
|
|
672 |
< |
%\subsection{\label{methodSection:zcons}Z-constraint Method} |
672 |
> |
If the time-dependent friction decays rapidly, the static friction |
673 |
> |
coefficient can be approximated by |
674 |
> |
\begin{equation} |
675 |
> |
\xi_{\text{static}}(z)=\int_{0}^{\infty}\langle\delta F(z,t)\delta |
676 |
> |
F(z,0)\rangle dt. |
677 |
> |
\end{equation} |
678 |
> |
Allowing diffusion constant to then be calculated through the |
679 |
> |
Einstein relation:\cite{Marrink1994} |
680 |
> |
\begin{equation} |
681 |
> |
D(z)=\frac{k_{B}T}{\xi_{\text{static}}(z)}=\frac{(k_{B}T)^{2}}{\int_{0}^{\infty |
682 |
> |
}\langle\delta F(z,t)\delta F(z,0)\rangle dt}.% |
683 |
> |
\end{equation} |
684 |
|
|
685 |
< |
\section{\label{methodSection:langevin}Integrators for Langevin Dynamics of Rigid Bodies} |
685 |
> |
The Z-Constraint method, which fixes the z coordinates of the |
686 |
> |
molecules with respect to the center of the mass of the system, has |
687 |
> |
been a method suggested to obtain the forces required for the force |
688 |
> |
auto-correlation calculation.\cite{Marrink1994} However, simply |
689 |
> |
resetting the coordinate will move the center of the mass of the |
690 |
> |
whole system. To avoid this problem, we reset the forces of |
691 |
> |
z-constrained molecules as well as subtract the total constraint |
692 |
> |
forces from the rest of the system after the force calculation at |
693 |
> |
each time step instead of resetting the coordinate. |
694 |
|
|
695 |
< |
\subsection{\label{methodSection:temperature}Temperature Control} |
695 |
> |
After the force calculation, we define $G_\alpha$ as |
696 |
> |
\begin{equation} |
697 |
> |
G_{\alpha} = \sum_i F_{\alpha i}, \label{oopseEq:zc1} |
698 |
> |
\end{equation} |
699 |
> |
where $F_{\alpha i}$ is the force in the z direction of atom $i$ in |
700 |
> |
z-constrained molecule $\alpha$. The forces of the z constrained |
701 |
> |
molecule are then set to: |
702 |
> |
\begin{equation} |
703 |
> |
F_{\alpha i} = F_{\alpha i} - |
704 |
> |
\frac{m_{\alpha i} G_{\alpha}}{\sum_i m_{\alpha i}}. |
705 |
> |
\end{equation} |
706 |
> |
Here, $m_{\alpha i}$ is the mass of atom $i$ in the z-constrained |
707 |
> |
molecule. Having rescaled the forces, the velocities must also be |
708 |
> |
rescaled to subtract out any center of mass velocity in the z |
709 |
> |
direction. |
710 |
> |
\begin{equation} |
711 |
> |
v_{\alpha i} = v_{\alpha i} - |
712 |
> |
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}, |
713 |
> |
\end{equation} |
714 |
> |
where $v_{\alpha i}$ is the velocity of atom $i$ in the z direction. |
715 |
> |
Lastly, all of the accumulated z constrained forces must be |
716 |
> |
subtracted from the system to keep the system center of mass from |
717 |
> |
drifting. |
718 |
> |
\begin{equation} |
719 |
> |
F_{\beta i} = F_{\beta i} - \frac{m_{\beta i} \sum_{\alpha} |
720 |
> |
G_{\alpha}} |
721 |
> |
{\sum_{\beta}\sum_i m_{\beta i}}, |
722 |
> |
\end{equation} |
723 |
> |
where $\beta$ are all of the unconstrained molecules in the system. |
724 |
> |
Similarly, the velocities of the unconstrained molecules must also |
725 |
> |
be scaled. |
726 |
> |
\begin{equation} |
727 |
> |
v_{\beta i} = v_{\beta i} + \sum_{\alpha} |
728 |
> |
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}. |
729 |
> |
\end{equation} |
730 |
|
|
731 |
< |
\subsection{\label{methodSection:pressureControl}Pressure Control} |
732 |
< |
|
733 |
< |
\section{\label{methodSection:hydrodynamics}Hydrodynamics} |
734 |
< |
|
735 |
< |
%\section{\label{methodSection:coarseGrained}Coarse-Grained Modeling} |
736 |
< |
|
737 |
< |
%\section{\label{methodSection:moleculeScale}Molecular-Scale Modeling} |
731 |
> |
At the very beginning of the simulation, the molecules may not be at |
732 |
> |
their constrained positions. To move a z-constrained molecule to its |
733 |
> |
specified position, a simple harmonic potential is used |
734 |
> |
\begin{equation} |
735 |
> |
U(t)=\frac{1}{2}k_{\text{Harmonic}}(z(t)-z_{\text{cons}})^{2},% |
736 |
> |
\end{equation} |
737 |
> |
where $k_{\text{Harmonic}}$ is the harmonic force constant, $z(t)$ |
738 |
> |
is the current $z$ coordinate of the center of mass of the |
739 |
> |
constrained molecule, and $z_{\text{cons}}$ is the constrained |
740 |
> |
position. The harmonic force operating on the z-constrained molecule |
741 |
> |
at time $t$ can be calculated by |
742 |
> |
\begin{equation} |
743 |
> |
F_{z_{\text{Harmonic}}}(t)=-\frac{\partial U(t)}{\partial z(t)}= |
744 |
> |
-k_{\text{Harmonic}}(z(t)-z_{\text{cons}}). |
745 |
> |
\end{equation} |