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 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 |
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 |
20 |
> |
time-reversible integrator for rigid bodies in quaternion |
21 |
> |
representation\cite{Matubayasi1999}. Although it is not symplectic, this integrator still |
22 |
> |
demonstrates a better long-time energy conservation than Euler angle |
23 |
> |
methods because of the time-reversible nature. Extending the |
24 |
> |
Trotter-Suzuki factorization to general system with a flat phase |
25 |
> |
space, Miller\cite{Miller2002} and his colleagues devised a novel |
26 |
> |
symplectic, time-reversible and volume-preserving integrator in the |
27 |
|
quaternion representation, which was shown to be superior to the |
28 |
|
Matubayasi's time-reversible integrator. However, all of the |
29 |
< |
integrators in quaternion representation suffer from the |
29 |
> |
integrators in the quaternion representation suffer from the |
30 |
|
computational penalty of constructing a rotation matrix from |
31 |
|
quaternions to evolve coordinates and velocities at every time step. |
32 |
< |
An alternative integration scheme utilizing rotation matrix directly |
33 |
< |
proposed by Dullweber, Leimkuhler and McLachlan (DLM) also preserved |
34 |
< |
the same structural properties of the Hamiltonian flow. In this |
35 |
< |
section, the integration scheme of DLM method will be reviewed and |
36 |
< |
extended to other ensembles. |
32 |
> |
An alternative integration scheme utilizing the rotation matrix |
33 |
> |
directly proposed by Dullweber, Leimkuhler and McLachlan (DLM) also |
34 |
> |
preserved the same structural properties of the Hamiltonian |
35 |
> |
propagator\cite{Dullweber1997}. In this section, the integration |
36 |
> |
scheme of DLM method will be reviewed and extended to other |
37 |
> |
ensembles. |
38 |
|
|
39 |
< |
\subsection{\label{methodSection:DLM}Integrating the Equations of Motion: the |
40 |
< |
DLM method} |
39 |
> |
\subsection{\label{methodSection:DLM}Integrating the Equations of Motion: The |
40 |
> |
DLM Method} |
41 |
|
|
42 |
|
The DLM method uses a Trotter factorization of the orientational |
43 |
|
propagator. This has three effects: |
46 |
|
{\it symplectic}), |
47 |
|
\item the integrator is time-{\it reversible}, making it suitable for Hybrid |
48 |
|
Monte Carlo applications, and |
49 |
< |
\item the error for a single time step is of order $\mathcal{O}\left(h^4\right)$ |
49 |
> |
\item the error for a single time step is of order $\mathcal{O}\left(h^3\right)$ |
50 |
|
for timesteps of length $h$. |
51 |
|
\end{enumerate} |
50 |
– |
|
52 |
|
The integration of the equations of motion is carried out in a |
53 |
|
velocity-Verlet style 2-part algorithm, where $h= \delta t$: |
54 |
|
|
63 |
|
{\bf j}\left(t + h / 2 \right) &\leftarrow {\bf j}(t) |
64 |
|
+ \frac{h}{2} {\bf \tau}^b(t), \\ |
65 |
|
% |
66 |
< |
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left( h {\bf j} |
66 |
> |
\mathsf{Q}(t + h) &\leftarrow \mathrm{rotate}\left( h {\bf j} |
67 |
|
(t + h / 2) \cdot \overleftrightarrow{\mathsf{I}}^{-1} \right). |
68 |
|
\end{align*} |
68 |
– |
|
69 |
|
In this context, the $\mathrm{rotate}$ function is the reversible |
70 |
|
product of the three body-fixed rotations, |
71 |
|
\begin{equation} |
74 |
|
/ 2) \cdot \mathsf{G}_x(a_x /2), |
75 |
|
\end{equation} |
76 |
|
where each rotational propagator, $\mathsf{G}_\alpha(\theta)$, |
77 |
< |
rotates both the rotation matrix ($\mathsf{A}$) and the body-fixed |
78 |
< |
angular momentum (${\bf j}$) by an angle $\theta$ around body-fixed |
77 |
> |
rotates both the rotation matrix $\mathsf{Q}$ and the body-fixed |
78 |
> |
angular momentum ${\bf j}$ by an angle $\theta$ around body-fixed |
79 |
|
axis $\alpha$, |
80 |
|
\begin{equation} |
81 |
|
\mathsf{G}_\alpha( \theta ) = \left\{ |
82 |
|
\begin{array}{lcl} |
83 |
< |
\mathsf{A}(t) & \leftarrow & \mathsf{A}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\ |
83 |
> |
\mathsf{Q}(t) & \leftarrow & \mathsf{Q}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\ |
84 |
|
{\bf j}(t) & \leftarrow & \mathsf{R}_\alpha(\theta) \cdot {\bf |
85 |
|
j}(0). |
86 |
|
\end{array} |
100 |
|
\end{array} |
101 |
|
\right). |
102 |
|
\end{equation} |
103 |
< |
All other rotations follow in a straightforward manner. |
103 |
> |
All other rotations follow in a straightforward manner. After the |
104 |
> |
first part of the propagation, the forces and body-fixed torques are |
105 |
> |
calculated at the new positions and orientations |
106 |
|
|
105 |
– |
After the first part of the propagation, the forces and body-fixed |
106 |
– |
torques are calculated at the new positions and orientations |
107 |
– |
|
107 |
|
{\tt doForces:} |
108 |
|
\begin{align*} |
109 |
|
{\bf f}(t + h) &\leftarrow |
110 |
|
- \left(\frac{\partial V}{\partial {\bf r}}\right)_{{\bf r}(t + h)}, \\ |
111 |
|
% |
112 |
|
{\bf \tau}^{s}(t + h) &\leftarrow {\bf u}(t + h) |
113 |
< |
\times \frac{\partial V}{\partial {\bf u}}, \\ |
113 |
> |
\times (\frac{\partial V}{\partial {\bf u}})_{u(t+h)}, \\ |
114 |
|
% |
115 |
< |
{\bf \tau}^{b}(t + h) &\leftarrow \mathsf{A}(t + h) |
115 |
> |
{\bf \tau}^{b}(t + h) &\leftarrow \mathsf{Q}(t + h) |
116 |
|
\cdot {\bf \tau}^s(t + h). |
117 |
|
\end{align*} |
118 |
< |
|
119 |
< |
{\sc oopse} automatically updates ${\bf u}$ when the rotation matrix |
121 |
< |
$\mathsf{A}$ is calculated in {\tt moveA}. Once the forces and |
118 |
> |
${\bf u}$ is automatically updated when the rotation matrix |
119 |
> |
$\mathsf{Q}$ is calculated in {\tt moveA}. Once the forces and |
120 |
|
torques have been obtained at the new time step, the velocities can |
121 |
|
be advanced to the same time value. |
122 |
|
|
130 |
|
\right) |
131 |
|
+ \frac{h}{2} {\bf \tau}^b(t + h) . |
132 |
|
\end{align*} |
135 |
– |
|
133 |
|
The matrix rotations used in the DLM method end up being more costly |
134 |
|
computationally than the simpler arithmetic quaternion propagation. |
135 |
|
With the same time step, a 1000-molecule water simulation shows an |
136 |
|
average 7\% increase in computation time using the DLM method in |
137 |
|
place of quaternions. This cost is more than justified when |
138 |
|
comparing the energy conservation of the two methods as illustrated |
139 |
< |
in Fig.~\ref{methodFig:timestep}. |
139 |
> |
in Fig.~\ref{methodFig:timestep} where the resulting energy drifts at |
140 |
> |
various time steps for both the DLM and quaternion integration |
141 |
> |
schemes are compared. All of the 1000 molecule water simulations |
142 |
> |
started with the same configuration, and the only difference was the |
143 |
> |
method for handling rotational motion. At time steps of 0.1 and 0.5 |
144 |
> |
fs, both methods for propagating molecule rotation conserve energy |
145 |
> |
fairly well, with the quaternion method showing a slight energy |
146 |
> |
drift over time in the 0.5 fs time step simulation. At time steps of |
147 |
> |
1 and 2 fs, the energy conservation benefits of the DLM method are |
148 |
> |
clearly demonstrated. Thus, while maintaining the same degree of |
149 |
> |
energy conservation, one can take considerably longer time steps, |
150 |
> |
leading to an overall reduction in computation time. |
151 |
|
|
152 |
|
\begin{figure} |
153 |
|
\centering |
162 |
|
\label{methodFig:timestep} |
163 |
|
\end{figure} |
164 |
|
|
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 |
– |
|
165 |
|
\subsection{\label{methodSection:NVT}Nos\'{e}-Hoover Thermostatting} |
166 |
|
|
167 |
|
The Nos\'e-Hoover equations of motion are given by\cite{Hoover1985} |
168 |
|
\begin{eqnarray} |
169 |
|
\dot{{\bf r}} & = & {\bf v}, \\ |
170 |
|
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - \chi {\bf v} ,\\ |
171 |
< |
\dot{\mathsf{A}} & = & \mathsf{A} \cdot |
171 |
> |
\dot{\mathsf{Q}} & = & \mathsf{Q} \cdot |
172 |
|
\mbox{ skew}\left(\overleftrightarrow{\mathsf{I}}^{-1} \cdot {\bf j}\right), \\ |
173 |
|
\dot{{\bf j}} & = & {\bf j} \times \left( |
174 |
|
\overleftrightarrow{\mathsf{I}}^{-1} \cdot {\bf j} \right) - \mbox{ |
175 |
< |
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial V}{\partial |
176 |
< |
\mathsf{A}} \right) - \chi {\bf j}. \label{eq:nosehoovereom} |
175 |
> |
rot}\left(\mathsf{Q}^{T} \cdot \frac{\partial V}{\partial |
176 |
> |
\mathsf{Q}} \right) - \chi {\bf j}. \label{eq:nosehoovereom} |
177 |
|
\end{eqnarray} |
183 |
– |
|
178 |
|
$\chi$ is an ``extra'' variable included in the extended system, and |
179 |
|
it is propagated using the first order equation of motion |
180 |
|
\begin{equation} |
181 |
|
\dot{\chi} = \frac{1}{\tau_{T}^2} \left( |
182 |
|
\frac{T}{T_{\mathrm{target}}} - 1 \right). \label{eq:nosehooverext} |
183 |
|
\end{equation} |
184 |
< |
|
185 |
< |
The instantaneous temperature $T$ is proportional to the total |
186 |
< |
kinetic energy (both translational and orientational) and is given |
193 |
< |
by |
184 |
> |
where $\tau_T$ is the time constant for relaxation of the |
185 |
> |
temperature to the target value, and the instantaneous temperature |
186 |
> |
$T$ is given by |
187 |
|
\begin{equation} |
188 |
< |
T = \frac{2 K}{f k_B} |
188 |
> |
T = \frac{2 K}{f k_B}. |
189 |
|
\end{equation} |
190 |
|
Here, $f$ is the total number of degrees of freedom in the system, |
191 |
|
\begin{equation} |
192 |
|
f = 3 N + 3 N_{\mathrm{orient}} - N_{\mathrm{constraints}}, |
193 |
|
\end{equation} |
194 |
< |
and $K$ is the total kinetic energy, |
195 |
< |
\begin{equation} |
196 |
< |
K = \sum_{i=1}^{N} \frac{1}{2} m_i {\bf v}_i^T \cdot {\bf v}_i + |
204 |
< |
\sum_{i=1}^{N_{\mathrm{orient}}} \frac{1}{2} {\bf j}_i^T \cdot |
205 |
< |
\overleftrightarrow{\mathsf{I}}_i^{-1} \cdot {\bf j}_i. |
206 |
< |
\end{equation} |
194 |
> |
where $N_{\mathrm{orient}}$ is the number of molecules with |
195 |
> |
orientational degrees of freedom. The integration of the equations of motion |
196 |
> |
is carried out in a velocity-Verlet style 2 part algorithm: |
197 |
|
|
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 |
212 |
– |
{\tt .bass} file. The units for {\tt tauThermostat} are fs, and the |
213 |
– |
units for the {\tt targetTemperature} are degrees K. The |
214 |
– |
integration of the equations of motion is carried out in a |
215 |
– |
velocity-Verlet style 2 part algorithm: |
216 |
– |
|
198 |
|
{\tt moveA:} |
199 |
|
\begin{align*} |
200 |
|
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\ |
210 |
|
+ \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t) |
211 |
|
\chi(t) \right) ,\\ |
212 |
|
% |
213 |
< |
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate} |
214 |
< |
\left(h * {\bf j}(t + h / 2) |
213 |
> |
\mathsf{Q}(t + h) &\leftarrow \mathrm{rotate} |
214 |
> |
\left(h {\bf j}(t + h / 2) |
215 |
|
\overleftrightarrow{\mathsf{I}}^{-1} \right) ,\\ |
216 |
|
% |
217 |
|
\chi\left(t + h / 2 \right) &\leftarrow \chi(t) |
218 |
|
+ \frac{h}{2 \tau_T^2} \left( \frac{T(t)} |
219 |
|
{T_{\mathrm{target}}} - 1 \right) . |
220 |
|
\end{align*} |
240 |
– |
|
221 |
|
Here $\mathrm{rotate}(h * {\bf j} |
222 |
< |
\overleftrightarrow{\mathsf{I}}^{-1})$ is the same symplectic |
223 |
< |
Trotter factorization of the three rotation operations that was |
224 |
< |
discussed in the section on the DLM integrator. Note that this |
225 |
< |
operation modifies both the rotation matrix $\mathsf{A}$ and the |
226 |
< |
angular momentum ${\bf j}$. {\tt moveA} propagates velocities by a |
227 |
< |
half time step, and positional degrees of freedom by a full time |
228 |
< |
step. The new positions (and orientations) are then used to |
229 |
< |
calculate a new set of forces and torques in exactly the same way |
230 |
< |
they are calculated in the {\tt doForces} portion of the DLM |
231 |
< |
integrator. |
232 |
< |
|
253 |
< |
Once the forces and torques have been obtained at the new time step, |
254 |
< |
the temperature, velocities, and the extended system variable can be |
222 |
> |
\overleftrightarrow{\mathsf{I}}^{-1})$ is the same symplectic Strang |
223 |
> |
factorization of the three rotation operations that was discussed in |
224 |
> |
the section on the DLM integrator. Note that this operation |
225 |
> |
modifies both the rotation matrix $\mathsf{Q}$ and the angular |
226 |
> |
momentum ${\bf j}$. {\tt moveA} propagates velocities by a half |
227 |
> |
time step, and positional degrees of freedom by a full time step. |
228 |
> |
The new positions (and orientations) are then used to calculate a |
229 |
> |
new set of forces and torques in exactly the same way they are |
230 |
> |
calculated in the {\tt doForces} portion of the DLM integrator. Once |
231 |
> |
the forces and torques have been obtained at the new time step, the |
232 |
> |
temperature, velocities, and the extended system variable can be |
233 |
|
advanced to the same time value. |
234 |
|
|
235 |
|
{\tt moveB:} |
251 |
|
\left( {\bf \tau}^b(t + h) - {\bf j}(t + h) |
252 |
|
\chi(t + h) \right) . |
253 |
|
\end{align*} |
276 |
– |
|
254 |
|
Since ${\bf v}(t + h)$ and ${\bf j}(t + h)$ are required to |
255 |
|
caclculate $T(t + h)$ as well as $\chi(t + h)$, they indirectly |
256 |
|
depend on their own values at time $t + h$. {\tt moveB} is |
257 |
|
therefore done in an iterative fashion until $\chi(t + h)$ becomes |
258 |
< |
self-consistent. The relative tolerance for the self-consistency |
259 |
< |
check defaults to a value of $\mbox{10}^{-6}$, but {\sc oopse} will |
260 |
< |
terminate the iteration after 4 loops even if the consistency check |
284 |
< |
has not been satisfied. |
285 |
< |
|
286 |
< |
The Nos\'e-Hoover algorithm is known to conserve a Hamiltonian for |
287 |
< |
the extended system that is, to within a constant, identical to the |
288 |
< |
Helmholtz free energy,\cite{Melchionna1993} |
258 |
> |
self-consistent. The Nos\'e-Hoover algorithm is known to conserve a |
259 |
> |
Hamiltonian for the extended system that is, to within a constant, |
260 |
> |
identical to the Helmholtz free energy,\cite{Melchionna1993} |
261 |
|
\begin{equation} |
262 |
|
H_{\mathrm{NVT}} = V + K + f k_B T_{\mathrm{target}} \left( |
263 |
|
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
264 |
|
dt^\prime \right). |
265 |
|
\end{equation} |
266 |
|
Poor choices of $h$ or $\tau_T$ can result in non-conservation of |
267 |
< |
$H_{\mathrm{NVT}}$, so the conserved quantity is maintained in the |
268 |
< |
last column of the {\tt .stat} file to allow checks on the quality |
297 |
< |
of the integration. |
267 |
> |
$H_{\mathrm{NVT}}$, so the conserved quantity should be checked |
268 |
> |
periodically to verify the quality of the integration. |
269 |
|
|
270 |
|
\subsection{\label{methodSection:NPTi}Constant-pressure integration with |
271 |
< |
isotropic box deformations (NPTi)} |
271 |
> |
isotropic box (NPTi)} |
272 |
|
|
273 |
< |
To carry out isobaric-isothermal ensemble calculations {\sc oopse} |
274 |
< |
implements the Melchionna modifications to the |
275 |
< |
Nos\'e-Hoover-Andersen equations of motion,\cite{Melchionna1993} |
305 |
< |
|
273 |
> |
We can used an isobaric-isothermal ensemble integrator which is |
274 |
> |
implemented using the Melchionna modifications to the |
275 |
> |
Nos\'e-Hoover-Andersen equations of motion\cite{Melchionna1993} |
276 |
|
\begin{eqnarray} |
277 |
|
\dot{{\bf r}} & = & {\bf v} + \eta \left( {\bf r} - {\bf R}_0 \right), \\ |
278 |
|
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - (\eta + \chi) {\bf v}, \\ |
279 |
< |
\dot{\mathsf{A}} & = & \mathsf{A} \cdot |
279 |
> |
\dot{\mathsf{Q}} & = & \mathsf{Q} \cdot |
280 |
|
\mbox{ skew}\left(\overleftrightarrow{I}^{-1} \cdot {\bf j}\right),\\ |
281 |
|
\dot{{\bf j}} & = & {\bf j} \times \left( |
282 |
|
\overleftrightarrow{I}^{-1} \cdot {\bf j} \right) - \mbox{ |
283 |
< |
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial |
284 |
< |
V}{\partial \mathsf{A}} \right) - \chi {\bf j}, \\ |
283 |
> |
rot}\left(\mathsf{Q}^{T} \cdot \frac{\partial |
284 |
> |
V}{\partial \mathsf{Q}} \right) - \chi {\bf j}, \\ |
285 |
|
\dot{\chi} & = & \frac{1}{\tau_{T}^2} \left( |
286 |
|
\frac{T}{T_{\mathrm{target}}} - 1 \right) ,\\ |
287 |
|
\dot{\eta} & = & \frac{1}{\tau_{B}^2 f k_B T_{\mathrm{target}}} V |
289 |
|
P_{\mathrm{target}} \right), \\ |
290 |
|
\dot{\mathcal{V}} & = & 3 \mathcal{V} \eta . \label{eq:melchionna1} |
291 |
|
\end{eqnarray} |
322 |
– |
|
292 |
|
$\chi$ and $\eta$ are the ``extra'' degrees of freedom in the |
293 |
|
extended system. $\chi$ is a thermostat, and it has the same |
294 |
|
function as it does in the Nos\'e-Hoover NVT integrator. $\eta$ is |
318 |
|
r}_{ij}(t) \otimes {\bf f}_{ij}(t). |
319 |
|
\end{equation} |
320 |
|
The instantaneous pressure is then simply obtained from the trace of |
321 |
< |
the Pressure tensor, |
321 |
> |
the pressure tensor, |
322 |
|
\begin{equation} |
323 |
|
P(t) = \frac{1}{3} \mathrm{Tr} \left( |
324 |
< |
\overleftrightarrow{\mathsf{P}}(t). \right) |
324 |
> |
\overleftrightarrow{\mathsf{P}}(t) \right) . |
325 |
|
\end{equation} |
326 |
< |
|
327 |
< |
In eq.(\ref{eq:melchionna1}), $\tau_B$ is the time constant for |
359 |
< |
relaxation of the pressure to the target value. To set values for |
360 |
< |
$\tau_B$ or $P_{\mathrm{target}}$ in a simulation, one would use the |
361 |
< |
{\tt tauBarostat} and {\tt targetPressure} keywords in the {\tt |
362 |
< |
.bass} file. The units for {\tt tauBarostat} are fs, and the units |
363 |
< |
for the {\tt targetPressure} are atmospheres. Like in the NVT |
326 |
> |
In Eq.~\ref{eq:melchionna1}, $\tau_B$ is the time constant for |
327 |
> |
relaxation of the pressure to the target value. Like in the NVT |
328 |
|
integrator, the integration of the equations of motion is carried |
329 |
|
out in a velocity-Verlet style 2 part algorithm: |
330 |
|
|
342 |
|
+ \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t) |
343 |
|
\chi(t) \right), \\ |
344 |
|
% |
345 |
< |
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left(h * |
345 |
> |
\mathsf{Q}(t + h) &\leftarrow \mathrm{rotate}\left(h * |
346 |
|
{\bf j}(t + h / 2) \overleftrightarrow{\mathsf{I}}^{-1} |
347 |
|
\right) ,\\ |
348 |
|
% |
362 |
|
\mathsf{H}(t + h) &\leftarrow e^{-h \eta(t + h / 2)} |
363 |
|
\mathsf{H}(t). |
364 |
|
\end{align*} |
401 |
– |
|
365 |
|
Most of these equations are identical to their counterparts in the |
366 |
|
NVT integrator, but the propagation of positions to time $t + h$ |
367 |
< |
depends on the positions at the same time. {\sc oopse} carries out |
368 |
< |
this step iteratively (with a limit of 5 passes through the |
369 |
< |
iterative loop). Also, the simulation box $\mathsf{H}$ is scaled |
370 |
< |
uniformly for one full time step by an exponential factor that |
371 |
< |
depends on the value of $\eta$ at time $t + h / 2$. Reshaping the |
409 |
< |
box uniformly also scales the volume of the box by |
367 |
> |
depends on the positions at the same time. The simulation box |
368 |
> |
$\mathsf{H}$ is scaled uniformly for one full time step by an |
369 |
> |
exponential factor that depends on the value of $\eta$ at time $t + |
370 |
> |
h / 2$. Reshaping the box uniformly also scales the volume of the |
371 |
> |
box by |
372 |
|
\begin{equation} |
373 |
|
\mathcal{V}(t + h) \leftarrow e^{ - 3 h \eta(t + h /2)}. |
374 |
|
\mathcal{V}(t) |
375 |
|
\end{equation} |
414 |
– |
|
376 |
|
The {\tt doForces} step for the NPTi integrator is exactly the same |
377 |
|
as in both the DLM and NVT integrators. Once the forces and torques |
378 |
|
have been obtained at the new time step, the velocities can be |
404 |
|
\tau}^b(t + h) - {\bf j}(t + h) |
405 |
|
\chi(t + h) \right) . |
406 |
|
\end{align*} |
446 |
– |
|
407 |
|
Once again, since ${\bf v}(t + h)$ and ${\bf j}(t + h)$ are required |
408 |
|
to caclculate $T(t + h)$, $P(t + h)$, $\chi(t + h)$, and $\eta(t + |
409 |
|
h)$, they indirectly depend on their own values at time $t + h$. |
410 |
|
{\tt moveB} is therefore done in an iterative fashion until $\chi(t |
411 |
< |
+ h)$ and $\eta(t + h)$ become self-consistent. The relative |
452 |
< |
tolerance for the self-consistency check defaults to a value of |
453 |
< |
$\mbox{10}^{-6}$, but {\sc oopse} will terminate the iteration after |
454 |
< |
4 loops even if the consistency check has not been satisfied. |
411 |
> |
+ h)$ and $\eta(t + h)$ become self-consistent. |
412 |
|
|
413 |
|
The Melchionna modification of the Nos\'e-Hoover-Andersen algorithm |
414 |
|
is known to conserve a Hamiltonian for the extended system that is, |
418 |
|
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
419 |
|
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t). |
420 |
|
\end{equation} |
421 |
< |
Poor choices of $\delta t$, $\tau_T$, or $\tau_B$ can result in |
422 |
< |
non-conservation of $H_{\mathrm{NPTi}}$, so the conserved quantity |
423 |
< |
is maintained in the last column of the {\tt .stat} file to allow |
467 |
< |
checks on the quality of the integration. It is also known that |
468 |
< |
this algorithm samples the equilibrium distribution for the enthalpy |
469 |
< |
(including contributions for the thermostat and barostat), |
421 |
> |
It is also known that this algorithm samples the equilibrium |
422 |
> |
distribution for the enthalpy (including contributions for the |
423 |
> |
thermostat and barostat), |
424 |
|
\begin{equation} |
425 |
|
H_{\mathrm{NPTi}} = V + K + \frac{f k_B T_{\mathrm{target}}}{2} |
426 |
|
\left( \chi^2 \tau_T^2 + \eta^2 \tau_B^2 \right) + |
427 |
|
P_{\mathrm{target}} \mathcal{V}(t). |
428 |
|
\end{equation} |
429 |
|
|
476 |
– |
Bond constraints are applied at the end of both the {\tt moveA} and |
477 |
– |
{\tt moveB} portions of the algorithm. |
478 |
– |
|
430 |
|
\subsection{\label{methodSection:NPTf}Constant-pressure integration with a |
431 |
|
flexible box (NPTf)} |
432 |
|
|
441 |
|
\dot{{\bf r}} & = & {\bf v} + \overleftrightarrow{\eta} \cdot \left( {\bf r} - {\bf R}_0 \right), \\ |
442 |
|
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - (\overleftrightarrow{\eta} + |
443 |
|
\chi \cdot \mathsf{1}) {\bf v}, \\ |
444 |
< |
\dot{\mathsf{A}} & = & \mathsf{A} \cdot |
444 |
> |
\dot{\mathsf{Q}} & = & \mathsf{Q} \cdot |
445 |
|
\mbox{ skew}\left(\overleftrightarrow{I}^{-1} \cdot {\bf j}\right) ,\\ |
446 |
|
\dot{{\bf j}} & = & {\bf j} \times \left( |
447 |
|
\overleftrightarrow{I}^{-1} \cdot {\bf j} \right) - \mbox{ |
448 |
< |
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial |
449 |
< |
V}{\partial \mathsf{A}} \right) - \chi {\bf j} ,\\ |
448 |
> |
rot}\left(\mathsf{Q}^{T} \cdot \frac{\partial |
449 |
> |
V}{\partial \mathsf{Q}} \right) - \chi {\bf j} ,\\ |
450 |
|
\dot{\chi} & = & \frac{1}{\tau_{T}^2} \left( |
451 |
|
\frac{T}{T_{\mathrm{target}}} - 1 \right) ,\\ |
452 |
|
\dot{\overleftrightarrow{\eta}} & = & \frac{1}{\tau_{B}^2 f k_B |
479 |
|
+ \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t) |
480 |
|
\chi(t) \right), \\ |
481 |
|
% |
482 |
< |
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left(h * |
482 |
> |
\mathsf{Q}(t + h) &\leftarrow \mathrm{rotate}\left(h * |
483 |
|
{\bf j}(t + h / 2) \overleftrightarrow{\mathsf{I}}^{-1} |
484 |
|
\right), \\ |
485 |
|
% |
500 |
|
\mathsf{H}(t + h) &\leftarrow \mathsf{H}(t) \cdot e^{-h |
501 |
|
\overleftrightarrow{\eta}(t + h / 2)} . |
502 |
|
\end{align*} |
503 |
< |
{\sc oopse} uses a power series expansion truncated at second order |
504 |
< |
for the exponential operation which scales the simulation box. |
503 |
> |
Here, a power series expansion truncated at second order for the |
504 |
> |
exponential operation is used to scale the simulation box. The {\tt |
505 |
> |
moveB} portion of the algorithm is largely unchanged from the NPTi |
506 |
> |
integrator: |
507 |
|
|
555 |
– |
The {\tt moveB} portion of the algorithm is largely unchanged from |
556 |
– |
the NPTi integrator: |
557 |
– |
|
508 |
|
{\tt moveB:} |
509 |
|
\begin{align*} |
510 |
|
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\}, |
534 |
|
+ h / 2 \right) + \frac{h}{2} \left( {\bf \tau}^b(t |
535 |
|
+ h) - {\bf j}(t + h) \chi(t + h) \right) . |
536 |
|
\end{align*} |
587 |
– |
|
537 |
|
The iterative schemes for both {\tt moveA} and {\tt moveB} are |
538 |
< |
identical to those described for the NPTi integrator. |
539 |
< |
|
540 |
< |
The NPTf integrator is known to conserve the following Hamiltonian: |
541 |
< |
\begin{equation} |
593 |
< |
H_{\mathrm{NPTf}} = V + K + f k_B T_{\mathrm{target}} \left( |
538 |
> |
identical to those described for the NPTi integrator. The NPTf |
539 |
> |
integrator is known to conserve the following Hamiltonian: |
540 |
> |
\begin{eqnarray*} |
541 |
> |
H_{\mathrm{NPTf}} & = & V + K + f k_B T_{\mathrm{target}} \left( |
542 |
|
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
543 |
< |
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B |
543 |
> |
dt^\prime \right) \\ |
544 |
> |
& & + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B |
545 |
|
T_{\mathrm{target}}}{2} |
546 |
|
\mathrm{Tr}\left[\overleftrightarrow{\eta}(t)\right]^2 \tau_B^2. |
547 |
< |
\end{equation} |
599 |
< |
|
547 |
> |
\end{eqnarray*} |
548 |
|
This integrator must be used with care, particularly in liquid |
549 |
|
simulations. Liquids have very small restoring forces in the |
550 |
|
off-diagonal directions, and the simulation box can very quickly |
553 |
|
finds most use in simulating crystals or liquid crystals which |
554 |
|
assume non-orthorhombic geometries. |
555 |
|
|
556 |
< |
\subsection{\label{methodSection:otherSpecialEnsembles}Other Special Ensembles} |
556 |
> |
\subsection{\label{methodSection:NPAT}NPAT Ensemble} |
557 |
|
|
558 |
< |
\subsubsection{\label{methodSection:NPAT}\textbf{NPAT Ensemble}} |
558 |
> |
A comprehensive understanding of relations between structures and |
559 |
> |
functions in biological membrane system ultimately relies on |
560 |
> |
structure and dynamics of lipid bilayers, which are strongly |
561 |
> |
affected by the interfacial interaction between lipid molecules and |
562 |
> |
surrounding media. One quantity used to describe the interfacial |
563 |
> |
interaction is the average surface area per lipid. |
564 |
> |
Constant area and constant lateral pressure simulations can be |
565 |
> |
achieved by extending the standard NPT ensemble with a different |
566 |
> |
pressure control strategy |
567 |
|
|
612 |
– |
A comprehensive understanding of structure¨Cfunction relations of |
613 |
– |
biological membrane system ultimately relies on structure and |
614 |
– |
dynamics of lipid bilayer, which are strongly affected by the |
615 |
– |
interfacial interaction between lipid molecules and surrounding |
616 |
– |
media. One quantity to describe the interfacial interaction is so |
617 |
– |
called the average surface area per lipid. Constat area and constant |
618 |
– |
lateral pressure simulation can be achieved by extending the |
619 |
– |
standard NPT ensemble with a different pressure control strategy |
620 |
– |
|
568 |
|
\begin{equation} |
569 |
|
\dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll} |
570 |
|
\frac{{V(P_{\alpha \beta } - P_{{\rm{target}}} )}}{{\tau_{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }} |
573 |
|
\end{array} |
574 |
|
\right. |
575 |
|
\end{equation} |
629 |
– |
|
576 |
|
Note that the iterative schemes for NPAT are identical to those |
577 |
|
described for the NPTi integrator. |
578 |
|
|
579 |
< |
\subsubsection{\label{methodSection:NPrT}\textbf{NP$\gamma$T Ensemble}} |
579 |
> |
\subsection{\label{methodSection:NPrT}NP$\gamma$T |
580 |
> |
Ensemble} |
581 |
|
|
582 |
|
Theoretically, the surface tension $\gamma$ of a stress free |
583 |
|
membrane system should be zero since its surface free energy $G$ is |
584 |
< |
minimum with respect to surface area $A$ |
638 |
< |
\[ |
639 |
< |
\gamma = \frac{{\partial G}}{{\partial A}}. |
640 |
< |
\] |
641 |
< |
However, a surface tension of zero is not appropriate for relatively |
642 |
< |
small patches of membrane. In order to eliminate the edge effect of |
643 |
< |
the membrane simulation, a special ensemble, NP$\gamma$T, is |
644 |
< |
proposed to maintain the lateral surface tension and normal |
645 |
< |
pressure. The equation of motion for cell size control tensor, |
646 |
< |
$\eta$, in $NP\gamma T$ is |
584 |
> |
minimum with respect to surface area $A$, |
585 |
|
\begin{equation} |
586 |
+ |
\gamma = \frac{{\partial G}}{{\partial A}}=0. |
587 |
+ |
\end{equation} |
588 |
+ |
However, a surface tension of zero is not |
589 |
+ |
appropriate for relatively small patches of membrane. In order to |
590 |
+ |
eliminate the edge effect of membrane simulations, a special |
591 |
+ |
ensemble NP$\gamma$T has been proposed to maintain the lateral |
592 |
+ |
surface tension and normal pressure. The equation of motion for the |
593 |
+ |
cell size control tensor, $\eta$, in $NP\gamma T$ is |
594 |
+ |
\begin{equation} |
595 |
|
\dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll} |
596 |
|
- A_{xy} (\gamma _\alpha - \gamma _{{\rm{target}}} ) & \mbox{$\alpha = \beta = x$ or $\alpha = \beta = y$}\\ |
597 |
|
\frac{{V(P_{\alpha \beta } - P_{{\rm{target}}} )}}{{\tau _{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}}}} & \mbox{$\alpha = \beta = z$} \\ |
606 |
|
- P_{{\rm{target}}} ) |
607 |
|
\label{methodEquation:instantaneousSurfaceTensor} |
608 |
|
\end{equation} |
662 |
– |
|
609 |
|
There is one additional extended system integrator (NPTxyz), in |
610 |
|
which each attempt to preserve the target pressure along the box |
611 |
|
walls perpendicular to that particular axis. The lengths of the box |
612 |
|
axes are allowed to fluctuate independently, but the angle between |
613 |
|
the box axes does not change. It should be noted that the NPTxyz |
614 |
|
integrator is a special case of $NP\gamma T$ if the surface tension |
615 |
< |
$\gamma$ is set to zero. |
615 |
> |
$\gamma$ is set to zero, and if $x$ and $y$ can move independently. |
616 |
|
|
617 |
< |
\section{\label{methodSection:zcons}Z-Constraint Method} |
617 |
> |
\section{\label{methodSection:zcons}The Z-Constraint Method} |
618 |
|
|
619 |
|
Based on the fluctuation-dissipation theorem, a force |
620 |
|
auto-correlation method was developed by Roux and Karplus to |
628 |
|
\begin{equation} |
629 |
|
\delta F(z,t)=F(z,t)-\langle F(z,t)\rangle. |
630 |
|
\end{equation} |
685 |
– |
|
631 |
|
If the time-dependent friction decays rapidly, the static friction |
632 |
|
coefficient can be approximated by |
633 |
|
\begin{equation} |
640 |
|
D(z)=\frac{k_{B}T}{\xi_{\text{static}}(z)}=\frac{(k_{B}T)^{2}}{\int_{0}^{\infty |
641 |
|
}\langle\delta F(z,t)\delta F(z,0)\rangle dt}.% |
642 |
|
\end{equation} |
698 |
– |
|
643 |
|
The Z-Constraint method, which fixes the z coordinates of the |
644 |
|
molecules with respect to the center of the mass of the system, has |
645 |
|
been a method suggested to obtain the forces required for the force |
648 |
|
whole system. To avoid this problem, we reset the forces of |
649 |
|
z-constrained molecules as well as subtract the total constraint |
650 |
|
forces from the rest of the system after the force calculation at |
651 |
< |
each time step instead of resetting the coordinate. |
652 |
< |
|
709 |
< |
After the force calculation, define $G_\alpha$ as |
651 |
> |
each time step instead of resetting the coordinate. After the force |
652 |
> |
calculation, we define $G_\alpha$ as |
653 |
|
\begin{equation} |
654 |
|
G_{\alpha} = \sum_i F_{\alpha i}, \label{oopseEq:zc1} |
655 |
|
\end{equation} |
684 |
|
v_{\beta i} = v_{\beta i} + \sum_{\alpha} |
685 |
|
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}. |
686 |
|
\end{equation} |
744 |
– |
|
687 |
|
At the very beginning of the simulation, the molecules may not be at |
688 |
|
their constrained positions. To move a z-constrained molecule to its |
689 |
|
specified position, a simple harmonic potential is used |
698 |
|
\begin{equation} |
699 |
|
F_{z_{\text{Harmonic}}}(t)=-\frac{\partial U(t)}{\partial z(t)}= |
700 |
|
-k_{\text{Harmonic}}(z(t)-z_{\text{cons}}). |
759 |
– |
\end{equation} |
760 |
– |
|
761 |
– |
|
762 |
– |
\section{\label{methodSection:langevin}Integrators for Langevin Dynamics of Rigid Bodies} |
763 |
– |
|
764 |
– |
%\subsection{\label{methodSection:temperature}Temperature Control} |
765 |
– |
|
766 |
– |
%\subsection{\label{methodSection:pressureControl}Pressure Control} |
767 |
– |
|
768 |
– |
%\section{\label{methodSection:hydrodynamics}Hydrodynamics} |
769 |
– |
|
770 |
– |
%applications of langevin dynamics |
771 |
– |
As an excellent alternative to newtonian dynamics, Langevin |
772 |
– |
dynamics, which mimics a simple heat bath with stochastic and |
773 |
– |
dissipative forces, has been applied in a variety of studies. The |
774 |
– |
stochastic treatment of the solvent enables us to carry out |
775 |
– |
substantially longer time simulation. Implicit solvent Langevin |
776 |
– |
dynamics simulation of met-enkephalin not only outperforms explicit |
777 |
– |
solvent simulation on computation efficiency, but also agrees very |
778 |
– |
well with explicit solvent simulation on dynamics |
779 |
– |
properties\cite{Shen2002}. Recently, applying Langevin dynamics with |
780 |
– |
UNRES model, Liow and his coworkers suggest that protein folding |
781 |
– |
pathways can be possibly exploited within a reasonable amount of |
782 |
– |
time\cite{Liwo2005}. The stochastic nature of the Langevin dynamics |
783 |
– |
also enhances the sampling of the system and increases the |
784 |
– |
probability of crossing energy barrier\cite{Banerjee2004, Cui2003}. |
785 |
– |
Combining Langevin dynamics with Kramers's theory, Klimov and |
786 |
– |
Thirumalai identified the free-energy barrier by studying the |
787 |
– |
viscosity dependence of the protein folding rates\cite{Klimov1997}. |
788 |
– |
In order to account for solvent induced interactions missing from |
789 |
– |
implicit solvent model, Kaya incorporated desolvation free energy |
790 |
– |
barrier into implicit coarse-grained solvent model in protein |
791 |
– |
folding/unfolding study and discovered a higher free energy barrier |
792 |
– |
between the native and denatured states. Because of its stability |
793 |
– |
against noise, Langevin dynamics is very suitable for studying |
794 |
– |
remagnetization processes in various |
795 |
– |
systems\cite{Palacios1998,Berkov2002,Denisov2003}. For instance, the |
796 |
– |
oscillation power spectrum of nanoparticles from Langevin dynamics |
797 |
– |
simulation has the same peak frequencies for different wave |
798 |
– |
vectors,which recovers the property of magnetic excitations in small |
799 |
– |
finite structures\cite{Berkov2005a}. In an attempt to reduce the |
800 |
– |
computational cost of simulation, multiple time stepping (MTS) |
801 |
– |
methods have been introduced and have been of great interest to |
802 |
– |
macromolecule and protein community\cite{Tuckerman1992}. Relying on |
803 |
– |
the observation that forces between distant atoms generally |
804 |
– |
demonstrate slower fluctuations than forces between close atoms, MTS |
805 |
– |
method are generally implemented by evaluating the slowly |
806 |
– |
fluctuating forces less frequently than the fast ones. |
807 |
– |
Unfortunately, nonlinear instability resulting from increasing |
808 |
– |
timestep in MTS simulation have became a critical obstruction |
809 |
– |
preventing the long time simulation. Due to the coupling to the heat |
810 |
– |
bath, Langevin dynamics has been shown to be able to damp out the |
811 |
– |
resonance artifact more efficiently\cite{Sandu1999}. |
812 |
– |
|
813 |
– |
It is very important to develop stable and efficient methods to |
814 |
– |
integrate the equations of motion of orientational degrees of |
815 |
– |
freedom. Euler angles are the nature choice to describe the |
816 |
– |
rotational degrees of freedom. However, due to its singularity, the |
817 |
– |
numerical integration of corresponding equations of motion is very |
818 |
– |
inefficient and inaccurate. Although an alternative integrator using |
819 |
– |
different sets of Euler angles can overcome this |
820 |
– |
difficulty\cite{Ryckaert1977, Andersen1983}, the computational |
821 |
– |
penalty and the lost of angular momentum conservation still remain. |
822 |
– |
In 1977, a singularity free representation utilizing quaternions was |
823 |
– |
developed by Evans\cite{Evans1977}. Unfortunately, this approach |
824 |
– |
suffer from the nonseparable Hamiltonian resulted from quaternion |
825 |
– |
representation, which prevents the symplectic algorithm to be |
826 |
– |
utilized. Another different approach is to apply holonomic |
827 |
– |
constraints to the atoms belonging to the rigid |
828 |
– |
body\cite{Barojas1973}. Each atom moves independently under the |
829 |
– |
normal forces deriving from potential energy and constraint forces |
830 |
– |
which are used to guarantee the rigidness. However, due to their |
831 |
– |
iterative nature, SHAKE and Rattle algorithm converge very slowly |
832 |
– |
when the number of constraint increases. |
833 |
– |
|
834 |
– |
The break through in geometric literature suggests that, in order to |
835 |
– |
develop a long-term integration scheme, one should preserve the |
836 |
– |
geometric structure of the flow. Matubayasi developed a |
837 |
– |
time-reversible integrator for rigid bodies in quaternion |
838 |
– |
representation. Although it is not symplectic, this integrator still |
839 |
– |
demonstrates a better long-time energy conservation than traditional |
840 |
– |
methods because of the time-reversible nature. Extending |
841 |
– |
Trotter-Suzuki to general system with a flat phase space, Miller and |
842 |
– |
his colleagues devised an novel symplectic, time-reversible and |
843 |
– |
volume-preserving integrator in quaternion representation. However, |
844 |
– |
all of the integrators in quaternion representation suffer from the |
845 |
– |
computational penalty of constructing a rotation matrix from |
846 |
– |
quaternions to evolve coordinates and velocities at every time step. |
847 |
– |
An alternative integration scheme utilizing rotation matrix directly |
848 |
– |
is RSHAKE\cite{Kol1997}, in which a conjugate momentum to rotation |
849 |
– |
matrix is introduced to re-formulate the Hamiltonian's equation and |
850 |
– |
the Hamiltonian is evolved in a constraint manifold by iteratively |
851 |
– |
satisfying the orthogonality constraint. However, RSHAKE is |
852 |
– |
inefficient because of the iterative procedure. An extremely |
853 |
– |
efficient integration scheme in rotation matrix representation, |
854 |
– |
which also preserves the same structural properties of the |
855 |
– |
Hamiltonian flow as Miller's integrator, is proposed by Dullweber, |
856 |
– |
Leimkuhler and McLachlan (DLM)\cite{Dullweber1997}. |
857 |
– |
|
858 |
– |
%review langevin/browninan dynamics for arbitrarily shaped rigid body |
859 |
– |
Combining Langevin or Brownian dynamics with rigid body dynamics, |
860 |
– |
one can study the slow processes in biomolecular systems. Modeling |
861 |
– |
the DNA as a chain of rigid spheres beads, which subject to harmonic |
862 |
– |
potentials as well as excluded volume potentials, Mielke and his |
863 |
– |
coworkers discover rapid superhelical stress generations from the |
864 |
– |
stochastic simulation of twin supercoiling DNA with response to |
865 |
– |
induced torques\cite{Mielke2004}. Membrane fusion is another key |
866 |
– |
biological process which controls a variety of physiological |
867 |
– |
functions, such as release of neurotransmitters \textit{etc}. A |
868 |
– |
typical fusion event happens on the time scale of millisecond, which |
869 |
– |
is impracticable to study using all atomistic model with newtonian |
870 |
– |
mechanics. With the help of coarse-grained rigid body model and |
871 |
– |
stochastic dynamics, the fusion pathways were exploited by many |
872 |
– |
researchers\cite{Noguchi2001,Noguchi2002,Shillcock2005}. Due to the |
873 |
– |
difficulty of numerical integration of anisotropy rotation, most of |
874 |
– |
the rigid body models are simply modeled by sphere, cylinder, |
875 |
– |
ellipsoid or other regular shapes in stochastic simulations. In an |
876 |
– |
effort to account for the diffusion anisotropy of the arbitrary |
877 |
– |
particles, Fernandes and de la Torre improved the original Brownian |
878 |
– |
dynamics simulation algorithm\cite{Ermak1978,Allison1991} by |
879 |
– |
incorporating a generalized $6\times6$ diffusion tensor and |
880 |
– |
introducing a simple rotation evolution scheme consisting of three |
881 |
– |
consecutive rotations\cite{Fernandes2002}. Unfortunately, unexpected |
882 |
– |
error and bias are introduced into the system due to the arbitrary |
883 |
– |
order of applying the noncommuting rotation |
884 |
– |
operators\cite{Beard2003}. Based on the observation the momentum |
885 |
– |
relaxation time is much less than the time step, one may ignore the |
886 |
– |
inertia in Brownian dynamics. However, assumption of the zero |
887 |
– |
average acceleration is not always true for cooperative motion which |
888 |
– |
is common in protein motion. An inertial Brownian dynamics (IBD) was |
889 |
– |
proposed to address this issue by adding an inertial correction |
890 |
– |
term\cite{Beard2003}. As a complement to IBD which has a lower bound |
891 |
– |
in time step because of the inertial relaxation time, long-time-step |
892 |
– |
inertial dynamics (LTID) can be used to investigate the inertial |
893 |
– |
behavior of the polymer segments in low friction |
894 |
– |
regime\cite{Beard2003}. LTID can also deal with the rotational |
895 |
– |
dynamics for nonskew bodies without translation-rotation coupling by |
896 |
– |
separating the translation and rotation motion and taking advantage |
897 |
– |
of the analytical solution of hydrodynamics properties. However, |
898 |
– |
typical nonskew bodies like cylinder and ellipsoid are inadequate to |
899 |
– |
represent most complex macromolecule assemblies. These intricate |
900 |
– |
molecules have been represented by a set of beads and their |
901 |
– |
hydrodynamics properties can be calculated using variant |
902 |
– |
hydrodynamic interaction tensors. |
903 |
– |
|
904 |
– |
The goal of the present work is to develop a Langevin dynamics |
905 |
– |
algorithm for arbitrary rigid particles by integrating the accurate |
906 |
– |
estimation of friction tensor from hydrodynamics theory into the |
907 |
– |
sophisticated rigid body dynamics. |
908 |
– |
|
909 |
– |
\subsection{\label{introSection:frictionTensor} Friction Tensor} |
910 |
– |
Theoretically, the friction kernel can be determined using velocity |
911 |
– |
autocorrelation function. However, this approach become impractical |
912 |
– |
when the system become more and more complicate. Instead, various |
913 |
– |
approaches based on hydrodynamics have been developed to calculate |
914 |
– |
the friction coefficients. The friction effect is isotropic in |
915 |
– |
Equation, $\zeta$ can be taken as a scalar. In general, friction |
916 |
– |
tensor $\Xi$ is a $6\times 6$ matrix given by |
917 |
– |
\[ |
918 |
– |
\Xi = \left( {\begin{array}{*{20}c} |
919 |
– |
{\Xi _{}^{tt} } & {\Xi _{}^{rt} } \\ |
920 |
– |
{\Xi _{}^{tr} } & {\Xi _{}^{rr} } \\ |
921 |
– |
\end{array}} \right). |
922 |
– |
\] |
923 |
– |
Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction |
924 |
– |
tensor and rotational resistance (friction) tensor respectively, |
925 |
– |
while ${\Xi^{tr} }$ is translation-rotation coupling tensor and $ |
926 |
– |
{\Xi^{rt} }$ is rotation-translation coupling tensor. When a |
927 |
– |
particle moves in a fluid, it may experience friction force or |
928 |
– |
torque along the opposite direction of the velocity or angular |
929 |
– |
velocity, |
930 |
– |
\[ |
931 |
– |
\left( \begin{array}{l} |
932 |
– |
F_R \\ |
933 |
– |
\tau _R \\ |
934 |
– |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
935 |
– |
{\Xi ^{tt} } & {\Xi ^{rt} } \\ |
936 |
– |
{\Xi ^{tr} } & {\Xi ^{rr} } \\ |
937 |
– |
\end{array}} \right)\left( \begin{array}{l} |
938 |
– |
v \\ |
939 |
– |
w \\ |
940 |
– |
\end{array} \right) |
941 |
– |
\] |
942 |
– |
where $F_r$ is the friction force and $\tau _R$ is the friction |
943 |
– |
toque. |
944 |
– |
|
945 |
– |
\subsubsection{\label{introSection:resistanceTensorRegular}\textbf{The Resistance Tensor for Regular Shape}} |
946 |
– |
|
947 |
– |
For a spherical particle, the translational and rotational friction |
948 |
– |
constant can be calculated from Stoke's law, |
949 |
– |
\[ |
950 |
– |
\Xi ^{tt} = \left( {\begin{array}{*{20}c} |
951 |
– |
{6\pi \eta R} & 0 & 0 \\ |
952 |
– |
0 & {6\pi \eta R} & 0 \\ |
953 |
– |
0 & 0 & {6\pi \eta R} \\ |
954 |
– |
\end{array}} \right) |
955 |
– |
\] |
956 |
– |
and |
957 |
– |
\[ |
958 |
– |
\Xi ^{rr} = \left( {\begin{array}{*{20}c} |
959 |
– |
{8\pi \eta R^3 } & 0 & 0 \\ |
960 |
– |
0 & {8\pi \eta R^3 } & 0 \\ |
961 |
– |
0 & 0 & {8\pi \eta R^3 } \\ |
962 |
– |
\end{array}} \right) |
963 |
– |
\] |
964 |
– |
where $\eta$ is the viscosity of the solvent and $R$ is the |
965 |
– |
hydrodynamics radius. |
966 |
– |
|
967 |
– |
Other non-spherical shape, such as cylinder and ellipsoid |
968 |
– |
\textit{etc}, are widely used as reference for developing new |
969 |
– |
hydrodynamics theory, because their properties can be calculated |
970 |
– |
exactly. In 1936, Perrin extended Stokes's law to general ellipsoid, |
971 |
– |
also called a triaxial ellipsoid, which is given in Cartesian |
972 |
– |
coordinates by\cite{Perrin1934, Perrin1936} |
973 |
– |
\[ |
974 |
– |
\frac{{x^2 }}{{a^2 }} + \frac{{y^2 }}{{b^2 }} + \frac{{z^2 }}{{c^2 |
975 |
– |
}} = 1 |
976 |
– |
\] |
977 |
– |
where the semi-axes are of lengths $a$, $b$, and $c$. Unfortunately, |
978 |
– |
due to the complexity of the elliptic integral, only the ellipsoid |
979 |
– |
with the restriction of two axes having to be equal, \textit{i.e.} |
980 |
– |
prolate($ a \ge b = c$) and oblate ($ a < b = c $), can be solved |
981 |
– |
exactly. Introducing an elliptic integral parameter $S$ for prolate, |
982 |
– |
\[ |
983 |
– |
S = \frac{2}{{\sqrt {a^2 - b^2 } }}\ln \frac{{a + \sqrt {a^2 - b^2 |
984 |
– |
} }}{b}, |
985 |
– |
\] |
986 |
– |
and oblate, |
987 |
– |
\[ |
988 |
– |
S = \frac{2}{{\sqrt {b^2 - a^2 } }}arctg\frac{{\sqrt {b^2 - a^2 } |
989 |
– |
}}{a} |
990 |
– |
\], |
991 |
– |
one can write down the translational and rotational resistance |
992 |
– |
tensors |
993 |
– |
\[ |
994 |
– |
\begin{array}{l} |
995 |
– |
\Xi _a^{tt} = 16\pi \eta \frac{{a^2 - b^2 }}{{(2a^2 - b^2 )S - 2a}} \\ |
996 |
– |
\Xi _b^{tt} = \Xi _c^{tt} = 32\pi \eta \frac{{a^2 - b^2 }}{{(2a^2 - 3b^2 )S + 2a}} \\ |
997 |
– |
\end{array}, |
998 |
– |
\] |
999 |
– |
and |
1000 |
– |
\[ |
1001 |
– |
\begin{array}{l} |
1002 |
– |
\Xi _a^{rr} = \frac{{32\pi }}{3}\eta \frac{{(a^2 - b^2 )b^2 }}{{2a - b^2 S}} \\ |
1003 |
– |
\Xi _b^{rr} = \Xi _c^{rr} = \frac{{32\pi }}{3}\eta \frac{{(a^4 - b^4 )}}{{(2a^2 - b^2 )S - 2a}} \\ |
1004 |
– |
\end{array}. |
1005 |
– |
\] |
1006 |
– |
|
1007 |
– |
\subsubsection{\label{introSection:resistanceTensorRegularArbitrary}\textbf{The Resistance Tensor for Arbitrary Shape}} |
1008 |
– |
|
1009 |
– |
Unlike spherical and other regular shaped molecules, there is not |
1010 |
– |
analytical solution for friction tensor of any arbitrary shaped |
1011 |
– |
rigid molecules. The ellipsoid of revolution model and general |
1012 |
– |
triaxial ellipsoid model have been used to approximate the |
1013 |
– |
hydrodynamic properties of rigid bodies. However, since the mapping |
1014 |
– |
from all possible ellipsoidal space, $r$-space, to all possible |
1015 |
– |
combination of rotational diffusion coefficients, $D$-space is not |
1016 |
– |
unique\cite{Wegener1979} as well as the intrinsic coupling between |
1017 |
– |
translational and rotational motion of rigid body, general ellipsoid |
1018 |
– |
is not always suitable for modeling arbitrarily shaped rigid |
1019 |
– |
molecule. A number of studies have been devoted to determine the |
1020 |
– |
friction tensor for irregularly shaped rigid bodies using more |
1021 |
– |
advanced method where the molecule of interest was modeled by |
1022 |
– |
combinations of spheres(beads)\cite{Carrasco1999} and the |
1023 |
– |
hydrodynamics properties of the molecule can be calculated using the |
1024 |
– |
hydrodynamic interaction tensor. Let us consider a rigid assembly of |
1025 |
– |
$N$ beads immersed in a continuous medium. Due to hydrodynamics |
1026 |
– |
interaction, the ``net'' velocity of $i$th bead, $v'_i$ is different |
1027 |
– |
than its unperturbed velocity $v_i$, |
1028 |
– |
\[ |
1029 |
– |
v'_i = v_i - \sum\limits_{j \ne i} {T_{ij} F_j } |
1030 |
– |
\] |
1031 |
– |
where $F_i$ is the frictional force, and $T_{ij}$ is the |
1032 |
– |
hydrodynamic interaction tensor. The friction force of $i$th bead is |
1033 |
– |
proportional to its ``net'' velocity |
1034 |
– |
\begin{equation} |
1035 |
– |
F_i = \zeta _i v_i - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }. |
1036 |
– |
\label{introEquation:tensorExpression} |
701 |
|
\end{equation} |
1038 |
– |
This equation is the basis for deriving the hydrodynamic tensor. In |
1039 |
– |
1930, Oseen and Burgers gave a simple solution to Equation |
1040 |
– |
\ref{introEquation:tensorExpression} |
1041 |
– |
\begin{equation} |
1042 |
– |
T_{ij} = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij} |
1043 |
– |
R_{ij}^T }}{{R_{ij}^2 }}} \right). \label{introEquation:oseenTensor} |
1044 |
– |
\end{equation} |
1045 |
– |
Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$. |
1046 |
– |
A second order expression for element of different size was |
1047 |
– |
introduced by Rotne and Prager\cite{Rotne1969} and improved by |
1048 |
– |
Garc\'{i}a de la Torre and Bloomfield\cite{Torre1977}, |
1049 |
– |
\begin{equation} |
1050 |
– |
T_{ij} = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I + |
1051 |
– |
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma |
1052 |
– |
_i^2 + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} - |
1053 |
– |
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right]. |
1054 |
– |
\label{introEquation:RPTensorNonOverlapped} |
1055 |
– |
\end{equation} |
1056 |
– |
Both of the Equation \ref{introEquation:oseenTensor} and Equation |
1057 |
– |
\ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij} |
1058 |
– |
\ge \sigma _i + \sigma _j$. An alternative expression for |
1059 |
– |
overlapping beads with the same radius, $\sigma$, is given by |
1060 |
– |
\begin{equation} |
1061 |
– |
T_{ij} = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 - |
1062 |
– |
\frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I + |
1063 |
– |
\frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right] |
1064 |
– |
\label{introEquation:RPTensorOverlapped} |
1065 |
– |
\end{equation} |
1066 |
– |
|
1067 |
– |
To calculate the resistance tensor at an arbitrary origin $O$, we |
1068 |
– |
construct a $3N \times 3N$ matrix consisting of $N \times N$ |
1069 |
– |
$B_{ij}$ blocks |
1070 |
– |
\begin{equation} |
1071 |
– |
B = \left( {\begin{array}{*{20}c} |
1072 |
– |
{B_{11} } & \ldots & {B_{1N} } \\ |
1073 |
– |
\vdots & \ddots & \vdots \\ |
1074 |
– |
{B_{N1} } & \cdots & {B_{NN} } \\ |
1075 |
– |
\end{array}} \right), |
1076 |
– |
\end{equation} |
1077 |
– |
where $B_{ij}$ is given by |
1078 |
– |
\[ |
1079 |
– |
B_{ij} = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij} |
1080 |
– |
)T_{ij} |
1081 |
– |
\] |
1082 |
– |
where $\delta _{ij}$ is Kronecker delta function. Inverting matrix |
1083 |
– |
$B$, we obtain |
1084 |
– |
|
1085 |
– |
\[ |
1086 |
– |
C = B^{ - 1} = \left( {\begin{array}{*{20}c} |
1087 |
– |
{C_{11} } & \ldots & {C_{1N} } \\ |
1088 |
– |
\vdots & \ddots & \vdots \\ |
1089 |
– |
{C_{N1} } & \cdots & {C_{NN} } \\ |
1090 |
– |
\end{array}} \right) |
1091 |
– |
\] |
1092 |
– |
, which can be partitioned into $N \times N$ $3 \times 3$ block |
1093 |
– |
$C_{ij}$. With the help of $C_{ij}$ and skew matrix $U_i$ |
1094 |
– |
\[ |
1095 |
– |
U_i = \left( {\begin{array}{*{20}c} |
1096 |
– |
0 & { - z_i } & {y_i } \\ |
1097 |
– |
{z_i } & 0 & { - x_i } \\ |
1098 |
– |
{ - y_i } & {x_i } & 0 \\ |
1099 |
– |
\end{array}} \right) |
1100 |
– |
\] |
1101 |
– |
where $x_i$, $y_i$, $z_i$ are the components of the vector joining |
1102 |
– |
bead $i$ and origin $O$. Hence, the elements of resistance tensor at |
1103 |
– |
arbitrary origin $O$ can be written as |
1104 |
– |
\begin{equation} |
1105 |
– |
\begin{array}{l} |
1106 |
– |
\Xi _{}^{tt} = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\ |
1107 |
– |
\Xi _{}^{tr} = \Xi _{}^{rt} = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\ |
1108 |
– |
\Xi _{}^{rr} = - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j \\ |
1109 |
– |
\end{array} |
1110 |
– |
\label{introEquation:ResistanceTensorArbitraryOrigin} |
1111 |
– |
\end{equation} |
1112 |
– |
|
1113 |
– |
The resistance tensor depends on the origin to which they refer. The |
1114 |
– |
proper location for applying friction force is the center of |
1115 |
– |
resistance (reaction), at which the trace of rotational resistance |
1116 |
– |
tensor, $ \Xi ^{rr}$ reaches minimum. Mathematically, the center of |
1117 |
– |
resistance is defined as an unique point of the rigid body at which |
1118 |
– |
the translation-rotation coupling tensor are symmetric, |
1119 |
– |
\begin{equation} |
1120 |
– |
\Xi^{tr} = \left( {\Xi^{tr} } \right)^T |
1121 |
– |
\label{introEquation:definitionCR} |
1122 |
– |
\end{equation} |
1123 |
– |
Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin}, |
1124 |
– |
we can easily find out that the translational resistance tensor is |
1125 |
– |
origin independent, while the rotational resistance tensor and |
1126 |
– |
translation-rotation coupling resistance tensor depend on the |
1127 |
– |
origin. Given resistance tensor at an arbitrary origin $O$, and a |
1128 |
– |
vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can |
1129 |
– |
obtain the resistance tensor at $P$ by |
1130 |
– |
\begin{equation} |
1131 |
– |
\begin{array}{l} |
1132 |
– |
\Xi _P^{tt} = \Xi _O^{tt} \\ |
1133 |
– |
\Xi _P^{tr} = \Xi _P^{rt} = \Xi _O^{tr} - U_{OP} \Xi _O^{tt} \\ |
1134 |
– |
\Xi _P^{rr} = \Xi _O^{rr} - U_{OP} \Xi _O^{tt} U_{OP} + \Xi _O^{tr} U_{OP} - U_{OP} \Xi _O^{{tr} ^{^T }} \\ |
1135 |
– |
\end{array} |
1136 |
– |
\label{introEquation:resistanceTensorTransformation} |
1137 |
– |
\end{equation} |
1138 |
– |
where |
1139 |
– |
\[ |
1140 |
– |
U_{OP} = \left( {\begin{array}{*{20}c} |
1141 |
– |
0 & { - z_{OP} } & {y_{OP} } \\ |
1142 |
– |
{z_i } & 0 & { - x_{OP} } \\ |
1143 |
– |
{ - y_{OP} } & {x_{OP} } & 0 \\ |
1144 |
– |
\end{array}} \right) |
1145 |
– |
\] |
1146 |
– |
Using Equations \ref{introEquation:definitionCR} and |
1147 |
– |
\ref{introEquation:resistanceTensorTransformation}, one can locate |
1148 |
– |
the position of center of resistance, |
1149 |
– |
\begin{eqnarray*} |
1150 |
– |
\left( \begin{array}{l} |
1151 |
– |
x_{OR} \\ |
1152 |
– |
y_{OR} \\ |
1153 |
– |
z_{OR} \\ |
1154 |
– |
\end{array} \right) & = &\left( {\begin{array}{*{20}c} |
1155 |
– |
{(\Xi _O^{rr} )_{yy} + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} } \\ |
1156 |
– |
{ - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz} + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} } \\ |
1157 |
– |
{ - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx} + (\Xi _O^{rr} )_{yy} } \\ |
1158 |
– |
\end{array}} \right)^{ - 1} \\ |
1159 |
– |
& & \left( \begin{array}{l} |
1160 |
– |
(\Xi _O^{tr} )_{yz} - (\Xi _O^{tr} )_{zy} \\ |
1161 |
– |
(\Xi _O^{tr} )_{zx} - (\Xi _O^{tr} )_{xz} \\ |
1162 |
– |
(\Xi _O^{tr} )_{xy} - (\Xi _O^{tr} )_{yx} \\ |
1163 |
– |
\end{array} \right) \\ |
1164 |
– |
\end{eqnarray*} |
1165 |
– |
|
1166 |
– |
where $x_OR$, $y_OR$, $z_OR$ are the components of the vector |
1167 |
– |
joining center of resistance $R$ and origin $O$. |
1168 |
– |
|
1169 |
– |
\subsection{Langevin dynamics for rigid particles of arbitrary shape} |
1170 |
– |
|
1171 |
– |
Consider a Langevin equation of motions in generalized coordinates |
1172 |
– |
\begin{equation} |
1173 |
– |
M_i \dot V_i (t) = F_{s,i} (t) + F_{f,i(t)} + F_{r,i} (t) |
1174 |
– |
\label{LDGeneralizedForm} |
1175 |
– |
\end{equation} |
1176 |
– |
where $M_i$ is a $6\times6$ generalized diagonal mass (include mass |
1177 |
– |
and moment of inertial) matrix and $V_i$ is a generalized velocity, |
1178 |
– |
$V_i = V_i(v_i,\omega _i)$. The right side of Eq. |
1179 |
– |
(\ref{LDGeneralizedForm}) consists of three generalized forces in |
1180 |
– |
lab-fixed frame, systematic force $F_{s,i}$, dissipative force |
1181 |
– |
$F_{f,i}$ and stochastic force $F_{r,i}$. While the evolution of the |
1182 |
– |
system in Newtownian mechanics typically refers to lab-fixed frame, |
1183 |
– |
it is also convenient to handle the rotation of rigid body in |
1184 |
– |
body-fixed frame. Thus the friction and random forces are calculated |
1185 |
– |
in body-fixed frame and converted back to lab-fixed frame by: |
1186 |
– |
\[ |
1187 |
– |
\begin{array}{l} |
1188 |
– |
F_{f,i}^l (t) = A^T F_{f,i}^b (t), \\ |
1189 |
– |
F_{r,i}^l (t) = A^T F_{r,i}^b (t) \\ |
1190 |
– |
\end{array}. |
1191 |
– |
\] |
1192 |
– |
Here, the body-fixed friction force $F_{r,i}^b$ is proportional to |
1193 |
– |
the body-fixed velocity at center of resistance $v_{R,i}^b$ and |
1194 |
– |
angular velocity $\omega _i$, |
1195 |
– |
\begin{equation} |
1196 |
– |
F_{r,i}^b (t) = \left( \begin{array}{l} |
1197 |
– |
f_{r,i}^b (t) \\ |
1198 |
– |
\tau _{r,i}^b (t) \\ |
1199 |
– |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
1200 |
– |
{\Xi _{R,t} } & {\Xi _{R,c}^T } \\ |
1201 |
– |
{\Xi _{R,c} } & {\Xi _{R,r} } \\ |
1202 |
– |
\end{array}} \right)\left( \begin{array}{l} |
1203 |
– |
v_{R,i}^b (t) \\ |
1204 |
– |
\omega _i (t) \\ |
1205 |
– |
\end{array} \right), |
1206 |
– |
\end{equation} |
1207 |
– |
while the random force $F_{r,i}^l$ is a Gaussian stochastic variable |
1208 |
– |
with zero mean and variance |
1209 |
– |
\begin{equation} |
1210 |
– |
\left\langle {F_{r,i}^l (t)(F_{r,i}^l (t'))^T } \right\rangle = |
1211 |
– |
\left\langle {F_{r,i}^b (t)(F_{r,i}^b (t'))^T } \right\rangle = |
1212 |
– |
2k_B T\Xi _R \delta (t - t'). |
1213 |
– |
\end{equation} |
1214 |
– |
The equation of motion for $v_i$ can be written as |
1215 |
– |
\begin{equation} |
1216 |
– |
m\dot v_i (t) = f_{t,i} (t) = f_{s,i} (t) + f_{f,i}^l (t) + |
1217 |
– |
f_{r,i}^l (t) |
1218 |
– |
\end{equation} |
1219 |
– |
Since the frictional force is applied at the center of resistance |
1220 |
– |
which generally does not coincide with the center of mass, an extra |
1221 |
– |
torque is exerted at the center of mass. Thus, the net body-fixed |
1222 |
– |
frictional torque at the center of mass, $\tau _{n,i}^b (t)$, is |
1223 |
– |
given by |
1224 |
– |
\begin{equation} |
1225 |
– |
\tau _{r,i}^b = \tau _{r,i}^b +r_{MR} \times f_{r,i}^b |
1226 |
– |
\end{equation} |
1227 |
– |
where $r_{MR}$ is the vector from the center of mass to the center |
1228 |
– |
of the resistance. Instead of integrating angular velocity in |
1229 |
– |
lab-fixed frame, we consider the equation of motion of angular |
1230 |
– |
momentum in body-fixed frame |
1231 |
– |
\begin{equation} |
1232 |
– |
\dot \pi _i (t) = \tau _{t,i} (t) = \tau _{s,i} (t) + \tau _{f,i}^b |
1233 |
– |
(t) + \tau _{r,i}^b(t) |
1234 |
– |
\end{equation} |
1235 |
– |
|
1236 |
– |
Embedding the friction terms into force and torque, one can |
1237 |
– |
integrate the langevin equations of motion for rigid body of |
1238 |
– |
arbitrary shape in a velocity-Verlet style 2-part algorithm, where |
1239 |
– |
$h= \delta t$: |
1240 |
– |
|
1241 |
– |
{\tt part one:} |
1242 |
– |
\begin{align*} |
1243 |
– |
v_i (t + h/2) &\leftarrow v_i (t) + \frac{{hf_{t,i}^l (t)}}{{2m_i }} \\ |
1244 |
– |
\pi _i (t + h/2) &\leftarrow \pi _i (t) + \frac{{h\tau _{t,i}^b (t)}}{2} \\ |
1245 |
– |
r_i (t + h) &\leftarrow r_i (t) + hv_i (t + h/2) \\ |
1246 |
– |
A_i (t + h) &\leftarrow rotate\left( {h\pi _i (t + h/2)I^{ - 1} } \right) \\ |
1247 |
– |
\end{align*} |
1248 |
– |
In this context, the $\mathrm{rotate}$ function is the reversible |
1249 |
– |
product of five consecutive body-fixed rotations, |
1250 |
– |
\begin{equation} |
1251 |
– |
\mathrm{rotate}({\bf a}) = \mathsf{G}_x(a_x / 2) \cdot |
1252 |
– |
\mathsf{G}_y(a_y / 2) \cdot \mathsf{G}_z(a_z) \cdot \mathsf{G}_y(a_y |
1253 |
– |
/ 2) \cdot \mathsf{G}_x(a_x /2), |
1254 |
– |
\end{equation} |
1255 |
– |
where each rotational propagator, $\mathsf{G}_\alpha(\theta)$, |
1256 |
– |
rotates both the rotation matrix ($\mathsf{A}$) and the body-fixed |
1257 |
– |
angular momentum ($\pi$) by an angle $\theta$ around body-fixed axis |
1258 |
– |
$\alpha$, |
1259 |
– |
\begin{equation} |
1260 |
– |
\mathsf{G}_\alpha( \theta ) = \left\{ |
1261 |
– |
\begin{array}{lcl} |
1262 |
– |
\mathsf{A}(t) & \leftarrow & \mathsf{A}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\ |
1263 |
– |
{\bf j}(t) & \leftarrow & \mathsf{R}_\alpha(\theta) \cdot {\bf |
1264 |
– |
j}(0). |
1265 |
– |
\end{array} |
1266 |
– |
\right. |
1267 |
– |
\end{equation} |
1268 |
– |
$\mathsf{R}_\alpha$ is a quadratic approximation to the single-axis |
1269 |
– |
rotation matrix. For example, in the small-angle limit, the |
1270 |
– |
rotation matrix around the body-fixed x-axis can be approximated as |
1271 |
– |
\begin{equation} |
1272 |
– |
\mathsf{R}_x(\theta) \approx \left( |
1273 |
– |
\begin{array}{ccc} |
1274 |
– |
1 & 0 & 0 \\ |
1275 |
– |
0 & \frac{1-\theta^2 / 4}{1 + \theta^2 / 4} & -\frac{\theta}{1+ |
1276 |
– |
\theta^2 / 4} \\ |
1277 |
– |
0 & \frac{\theta}{1+ \theta^2 / 4} & \frac{1-\theta^2 / 4}{1 + |
1278 |
– |
\theta^2 / 4} |
1279 |
– |
\end{array} |
1280 |
– |
\right). |
1281 |
– |
\end{equation} |
1282 |
– |
All other rotations follow in a straightforward manner. |
1283 |
– |
|
1284 |
– |
After the first part of the propagation, the friction and random |
1285 |
– |
forces are generated at the center of resistance in body-fixed frame |
1286 |
– |
and converted back into lab-fixed frame |
1287 |
– |
\[ |
1288 |
– |
f_{t,i}^l (t + h) = - \left( {\frac{{\partial V}}{{\partial r_i }}} |
1289 |
– |
\right)_{r_i (t + h)} + A_i^T (t + h)[F_{f,i}^b (t + h) + F_{r,i}^b |
1290 |
– |
(t + h)], |
1291 |
– |
\] |
1292 |
– |
while the system torque in lab-fixed frame is transformed into |
1293 |
– |
body-fixed frame, |
1294 |
– |
\[ |
1295 |
– |
\tau _{t,i}^b (t + h) = A\tau _{s,i}^l (t) + \tau _{n,i}^b (t) + |
1296 |
– |
\tau _{r,i}^b (t). |
1297 |
– |
\] |
1298 |
– |
Once the forces and torques have been obtained at the new time step, |
1299 |
– |
the velocities can be advanced to the same time value. |
1300 |
– |
|
1301 |
– |
{\tt part two:} |
1302 |
– |
\begin{align*} |
1303 |
– |
v_i (t) &\leftarrow v_i (t + h/2) + \frac{{hf_{t,i}^l (t + h)}}{{2m_i }} \\ |
1304 |
– |
\pi _i (t) &\leftarrow \pi _i (t + h/2) + \frac{{h\tau _{t,i}^b (t + h)}}{2} \\ |
1305 |
– |
\end{align*} |
1306 |
– |
|
1307 |
– |
\subsection{Results and discussion} |