trunk/tengDissertation/Methodology.tex

\chapter{\label{chapt:methodology}METHODOLOGY}

\section{\label{methodSection:rigidBodyIntegrators}Integrators for Rigid Body Motion in Molecular Dynamics}

In order to mimic the experiments, which are usually performed under
constant temperature and/or pressure, extended Hamiltonian system
methods have been developed to generate statistical ensembles, such
as canonical ensemble and isobaric-isothermal ensemble \textit{etc}.
In addition to the standard ensemble, specific ensembles have been
developed to account for the anisotropy between the lateral and
normal directions of membranes. The $NPAT$ ensemble, in which the
normal pressure and the lateral surface area of the membrane are
kept constant, and the $NP\gamma T$ ensemble, in which the normal
pressure and the lateral surface tension are kept constant were
proposed to address this issue.

Integration schemes for rotational motion of the rigid molecules in
microcanonical ensemble have been extensively studied in the last
two decades. Matubayasi and Nakahara developed a time-reversible
integrator for rigid bodies in quaternion representation. Although
it is not symplectic, this integrator still demonstrates a better
long-time energy conservation than traditional methods because of
the time-reversible nature. Extending Trotter-Suzuki to general
system with a flat phase space, Miller and his colleagues devised an
novel symplectic, time-reversible and volume-preserving integrator
in quaternion representation, which was shown to be superior to the
time-reversible integrator of Matubayasi and Nakahara. However, all
of the integrators in quaternion representation suffer from the
computational penalty of constructing a rotation matrix from
quaternions to evolve coordinates and velocities at every time step.
An alternative integration scheme utilizing rotation matrix directly
proposed by Dullweber, Leimkuhler and McLachlan (DLM) also preserved
the same structural properties of the Hamiltonian flow. In this
section, the integration scheme of DLM method will be reviewed and
extended to other ensembles.

\subsection{\label{methodSection:DLM}Integrating the Equations of Motion: the
DLM method}

The DLM method uses a Trotter factorization of the orientational
propagator.  This has three effects:
\begin{enumerate}
\item the integrator is area-preserving in phase space (i.e. it is
{\it symplectic}),
\item the integrator is time-{\it reversible}, making it suitable for Hybrid
Monte Carlo applications, and
\item the error for a single time step is of order $\mathcal{O}\left(h^4\right)$
for timesteps of length $h$.
\end{enumerate}

The integration of the equations of motion is carried out in a
velocity-Verlet style 2-part algorithm, where $h= \delta t$:

{\tt moveA:}
\begin{align*}
{\bf v}\left(t + h / 2\right)  &\leftarrow  {\bf v}(t)
    + \frac{h}{2} \left( {\bf f}(t) / m \right), \\
%
{\bf r}(t + h) &\leftarrow {\bf r}(t)
    + h  {\bf v}\left(t + h / 2 \right), \\
%
{\bf j}\left(t + h / 2 \right)  &\leftarrow {\bf j}(t)
    + \frac{h}{2} {\bf \tau}^b(t), \\
%
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left( h {\bf j}
    (t + h / 2) \cdot \overleftrightarrow{\mathsf{I}}^{-1} \right).
\end{align*}

In this context, the $\mathrm{rotate}$ function is the reversible
product of the three body-fixed rotations,
\begin{equation}
\mathrm{rotate}({\bf a}) = \mathsf{G}_x(a_x / 2) \cdot
\mathsf{G}_y(a_y / 2) \cdot \mathsf{G}_z(a_z) \cdot \mathsf{G}_y(a_y
/ 2) \cdot \mathsf{G}_x(a_x /2),
\end{equation}
where each rotational propagator, $\mathsf{G}_\alpha(\theta)$,
rotates both the rotation matrix ($\mathsf{A}$) and the body-fixed
angular momentum (${\bf j}$) by an angle $\theta$ around body-fixed
axis $\alpha$,
\begin{equation}
\mathsf{G}_\alpha( \theta ) = \left\{
\begin{array}{lcl}
\mathsf{A}(t) & \leftarrow & \mathsf{A}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\
{\bf j}(t) & \leftarrow & \mathsf{R}_\alpha(\theta) \cdot {\bf
j}(0).
\end{array}
\right.
\end{equation}
$\mathsf{R}_\alpha$ is a quadratic approximation to the single-axis
rotation matrix.  For example, in the small-angle limit, the
rotation matrix around the body-fixed x-axis can be approximated as
\begin{equation}
\mathsf{R}_x(\theta) \approx \left(
\begin{array}{ccc}
1 & 0 & 0 \\
0 & \frac{1-\theta^2 / 4}{1 + \theta^2 / 4}  & -\frac{\theta}{1+
\theta^2 / 4} \\
0 & \frac{\theta}{1+ \theta^2 / 4} & \frac{1-\theta^2 / 4}{1 +
\theta^2 / 4}
\end{array}
\right).
\end{equation}
All other rotations follow in a straightforward manner.

After the first part of the propagation, the forces and body-fixed
torques are calculated at the new positions and orientations

{\tt doForces:}
\begin{align*}
{\bf f}(t + h) &\leftarrow
    - \left(\frac{\partial V}{\partial {\bf r}}\right)_{{\bf r}(t + h)}, \\
%
{\bf \tau}^{s}(t + h) &\leftarrow {\bf u}(t + h)
    \times \frac{\partial V}{\partial {\bf u}}, \\
%
{\bf \tau}^{b}(t + h) &\leftarrow \mathsf{A}(t + h)
    \cdot {\bf \tau}^s(t + h).
\end{align*}

{\sc oopse} automatically updates ${\bf u}$ when the rotation matrix
$\mathsf{A}$ is calculated in {\tt moveA}.  Once the forces and
torques have been obtained at the new time step, the velocities can
be advanced to the same time value.

{\tt moveB:}
\begin{align*}
{\bf v}\left(t + h \right)  &\leftarrow  {\bf v}\left(t + h / 2
\right)
    + \frac{h}{2} \left( {\bf f}(t + h) / m \right), \\
%
{\bf j}\left(t + h \right)  &\leftarrow {\bf j}\left(t + h / 2
\right)
    + \frac{h}{2} {\bf \tau}^b(t + h) .
\end{align*}

The matrix rotations used in the DLM method end up being more costly
computationally than the simpler arithmetic quaternion propagation.
With the same time step, a 1000-molecule water simulation shows an
average 7\% increase in computation time using the DLM method in
place of quaternions. This cost is more than justified when
comparing the energy conservation of the two methods as illustrated
in Fig.~\ref{timestep}.

\begin{figure}
\centering
\includegraphics[width=\linewidth]{timeStep.eps}
\caption[Energy conservation for quaternion versus DLM
dynamics]{Energy conservation using quaternion based integration
versus the method proposed by Dullweber \emph{et al.} with
increasing time step. For each time step, the dotted line is total
energy using the DLM integrator, and the solid line comes from the
quaternion integrator. The larger time step plots are shifted up
from the true energy baseline for clarity.}
\label{methodFig:timestep}
\end{figure}

In Fig.~\ref{methodFig:timestep}, the resulting energy drift at
various time steps for both the DLM and quaternion integration
schemes is compared. All of the 1000 molecule water simulations
started with the same configuration, and the only difference was the
method for handling rotational motion. At time steps of 0.1 and 0.5
fs, both methods for propagating molecule rotation conserve energy
fairly well, with the quaternion method showing a slight energy
drift over time in the 0.5 fs time step simulation. At time steps of
1 and 2 fs, the energy conservation benefits of the DLM method are
clearly demonstrated. Thus, while maintaining the same degree of
energy conservation, one can take considerably longer time steps,
leading to an overall reduction in computation time.

\subsection{\label{methodSection:NVT}Nos\'{e}-Hoover Thermostatting}

The Nos\'e-Hoover equations of motion are given by\cite{Hoover1985}
\begin{eqnarray}
\dot{{\bf r}} & = & {\bf v}, \\
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - \chi {\bf v} ,\\
\dot{\mathsf{A}} & = & \mathsf{A} \cdot
\mbox{ skew}\left(\overleftrightarrow{\mathsf{I}}^{-1} \cdot {\bf j}\right), \\
\dot{{\bf j}} & = & {\bf j} \times \left(
\overleftrightarrow{\mathsf{I}}^{-1} \cdot {\bf j} \right) - \mbox{
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial V}{\partial
\mathsf{A}} \right) - \chi {\bf j}. \label{eq:nosehoovereom}
\end{eqnarray}

$\chi$ is an ``extra'' variable included in the extended system, and
it is propagated using the first order equation of motion
\begin{equation}
\dot{\chi} = \frac{1}{\tau_{T}^2} \left(
\frac{T}{T_{\mathrm{target}}} - 1 \right). \label{eq:nosehooverext}
\end{equation}

The instantaneous temperature $T$ is proportional to the total
kinetic energy (both translational and orientational) and is given
by
\begin{equation}
T = \frac{2 K}{f k_B}
\end{equation}
Here, $f$ is the total number of degrees of freedom in the system,
\begin{equation}
f = 3 N + 3 N_{\mathrm{orient}} - N_{\mathrm{constraints}},
\end{equation}
and $K$ is the total kinetic energy,
\begin{equation}
K = \sum_{i=1}^{N} \frac{1}{2} m_i {\bf v}_i^T \cdot {\bf v}_i +
\sum_{i=1}^{N_{\mathrm{orient}}}  \frac{1}{2} {\bf j}_i^T \cdot
\overleftrightarrow{\mathsf{I}}_i^{-1} \cdot {\bf j}_i.
\end{equation}

In eq.(\ref{eq:nosehooverext}), $\tau_T$ is the time constant for
relaxation of the temperature to the target value.  To set values
for $\tau_T$ or $T_{\mathrm{target}}$ in a simulation, one would use
the {\tt tauThermostat} and {\tt targetTemperature} keywords in the
{\tt .bass} file.  The units for {\tt tauThermostat} are fs, and the
units for the {\tt targetTemperature} are degrees K.   The
integration of the equations of motion is carried out in a
velocity-Verlet style 2 part algorithm:

{\tt moveA:}
\begin{align*}
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\
%
{\bf v}\left(t + h / 2\right)  &\leftarrow {\bf v}(t)
    + \frac{h}{2} \left( \frac{{\bf f}(t)}{m} - {\bf v}(t)
    \chi(t)\right), \\
%
{\bf r}(t + h) &\leftarrow {\bf r}(t)
    + h {\bf v}\left(t + h / 2 \right) ,\\
%
{\bf j}\left(t + h / 2 \right)  &\leftarrow {\bf j}(t)
    + \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t)
    \chi(t) \right) ,\\
%
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}
    \left(h * {\bf j}(t + h / 2)
    \overleftrightarrow{\mathsf{I}}^{-1} \right) ,\\
%
\chi\left(t + h / 2 \right) &\leftarrow \chi(t)
    + \frac{h}{2 \tau_T^2} \left( \frac{T(t)}
    {T_{\mathrm{target}}} - 1 \right) .
\end{align*}

Here $\mathrm{rotate}(h * {\bf j}
\overleftrightarrow{\mathsf{I}}^{-1})$ is the same symplectic
Trotter factorization of the three rotation operations that was
discussed in the section on the DLM integrator.  Note that this
operation modifies both the rotation matrix $\mathsf{A}$ and the
angular momentum ${\bf j}$.  {\tt moveA} propagates velocities by a
half time step, and positional degrees of freedom by a full time
step.  The new positions (and orientations) are then used to
calculate a new set of forces and torques in exactly the same way
they are calculated in the {\tt doForces} portion of the DLM
integrator.

Once the forces and torques have been obtained at the new time step,
the temperature, velocities, and the extended system variable can be
advanced to the same time value.

{\tt moveB:}
\begin{align*}
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\},
    \left\{{\bf j}(t + h)\right\}, \\
%
\chi\left(t + h \right) &\leftarrow \chi\left(t + h /
    2 \right) + \frac{h}{2 \tau_T^2} \left( \frac{T(t+h)}
    {T_{\mathrm{target}}} - 1 \right), \\
%
{\bf v}\left(t + h \right)  &\leftarrow {\bf v}\left(t
    + h / 2 \right) + \frac{h}{2} \left(
    \frac{{\bf f}(t + h)}{m} - {\bf v}(t + h)
    \chi(t h)\right) ,\\
%
{\bf j}\left(t + h \right) &\leftarrow {\bf j}\left(t
    + h / 2 \right) + \frac{h}{2}
    \left( {\bf \tau}^b(t + h) - {\bf j}(t + h)
    \chi(t + h) \right) .
\end{align*}

Since ${\bf v}(t + h)$ and ${\bf j}(t + h)$ are required to
caclculate $T(t + h)$ as well as $\chi(t + h)$, they indirectly
depend on their own values at time $t + h$.  {\tt moveB} is
therefore done in an iterative fashion until $\chi(t + h)$ becomes
self-consistent.  The relative tolerance for the self-consistency
check defaults to a value of $\mbox{10}^{-6}$, but {\sc oopse} will
terminate the iteration after 4 loops even if the consistency check
has not been satisfied.

The Nos\'e-Hoover algorithm is known to conserve a Hamiltonian for
the extended system that is, to within a constant, identical to the
Helmholtz free energy,\cite{Melchionna1993}
\begin{equation}
H_{\mathrm{NVT}} = V + K + f k_B T_{\mathrm{target}} \left(
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime)
dt^\prime \right).
\end{equation}
Poor choices of $h$ or $\tau_T$ can result in non-conservation of
$H_{\mathrm{NVT}}$, so the conserved quantity is maintained in the
last column of the {\tt .stat} file to allow checks on the quality
of the integration.

\subsection{\label{methodSection:NPTi}Constant-pressure integration with
isotropic box deformations (NPTi)}

To carry out isobaric-isothermal ensemble calculations {\sc oopse}
implements the Melchionna modifications to the
Nos\'e-Hoover-Andersen equations of motion,\cite{Melchionna1993}

\begin{eqnarray}
\dot{{\bf r}} & = & {\bf v} + \eta \left( {\bf r} - {\bf R}_0 \right), \\
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - (\eta + \chi) {\bf v}, \\
\dot{\mathsf{A}} & = & \mathsf{A} \cdot
\mbox{ skew}\left(\overleftrightarrow{I}^{-1} \cdot {\bf j}\right),\\
\dot{{\bf j}} & = & {\bf j} \times \left(
\overleftrightarrow{I}^{-1} \cdot {\bf j} \right) - \mbox{
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial
V}{\partial \mathsf{A}} \right) - \chi {\bf j}, \\
\dot{\chi} & = & \frac{1}{\tau_{T}^2} \left(
\frac{T}{T_{\mathrm{target}}} - 1 \right) ,\\
\dot{\eta} & = & \frac{1}{\tau_{B}^2 f k_B T_{\mathrm{target}}} V
\left( P -
P_{\mathrm{target}} \right), \\
\dot{\mathcal{V}} & = & 3 \mathcal{V} \eta . \label{eq:melchionna1}
\end{eqnarray}

$\chi$ and $\eta$ are the ``extra'' degrees of freedom in the
extended system.  $\chi$ is a thermostat, and it has the same
function as it does in the Nos\'e-Hoover NVT integrator.  $\eta$ is
a barostat which controls changes to the volume of the simulation
box.  ${\bf R}_0$ is the location of the center of mass for the
entire system, and $\mathcal{V}$ is the volume of the simulation
box.  At any time, the volume can be calculated from the determinant
of the matrix which describes the box shape:
\begin{equation}
\mathcal{V} = \det(\mathsf{H}).
\end{equation}

The NPTi integrator requires an instantaneous pressure. This
quantity is calculated via the pressure tensor,
\begin{equation}
\overleftrightarrow{\mathsf{P}}(t) = \frac{1}{\mathcal{V}(t)} \left(
\sum_{i=1}^{N} m_i {\bf v}_i(t) \otimes {\bf v}_i(t) \right) +
\overleftrightarrow{\mathsf{W}}(t).
\end{equation}
The kinetic contribution to the pressure tensor utilizes the {\it
outer} product of the velocities denoted by the $\otimes$ symbol.
The stress tensor is calculated from another outer product of the
inter-atomic separation vectors (${\bf r}_{ij} = {\bf r}_j - {\bf
r}_i$) with the forces between the same two atoms,
\begin{equation}
\overleftrightarrow{\mathsf{W}}(t) = \sum_{i} \sum_{j>i} {\bf
r}_{ij}(t) \otimes {\bf f}_{ij}(t).
\end{equation}
The instantaneous pressure is then simply obtained from the trace of
the Pressure tensor,
\begin{equation}
P(t) = \frac{1}{3} \mathrm{Tr} \left(
\overleftrightarrow{\mathsf{P}}(t). \right)
\end{equation}

In eq.(\ref{eq:melchionna1}), $\tau_B$ is the time constant for
relaxation of the pressure to the target value.  To set values for
$\tau_B$ or $P_{\mathrm{target}}$ in a simulation, one would use the
{\tt tauBarostat} and {\tt targetPressure} keywords in the {\tt
.bass} file.  The units for {\tt tauBarostat} are fs, and the units
for the {\tt targetPressure} are atmospheres.  Like in the NVT
integrator, the integration of the equations of motion is carried
out in a velocity-Verlet style 2 part algorithm:

{\tt moveA:}
\begin{align*}
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\
%
P(t) &\leftarrow \left\{{\bf r}(t)\right\}, \left\{{\bf v}(t)\right\} ,\\
%
{\bf v}\left(t + h / 2\right)  &\leftarrow {\bf v}(t)
    + \frac{h}{2} \left( \frac{{\bf f}(t)}{m} - {\bf v}(t)
    \left(\chi(t) + \eta(t) \right) \right), \\
%
{\bf j}\left(t + h / 2 \right)  &\leftarrow {\bf j}(t)
    + \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t)
    \chi(t) \right), \\
%
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left(h *
    {\bf j}(t + h / 2) \overleftrightarrow{\mathsf{I}}^{-1}
    \right) ,\\
%
\chi\left(t + h / 2 \right) &\leftarrow \chi(t) +
    \frac{h}{2 \tau_T^2} \left( \frac{T(t)}{T_{\mathrm{target}}} - 1
    \right) ,\\
%
\eta(t + h / 2) &\leftarrow \eta(t) + \frac{h
    \mathcal{V}(t)}{2 N k_B T(t) \tau_B^2} \left( P(t)
    - P_{\mathrm{target}} \right), \\
%
{\bf r}(t + h) &\leftarrow {\bf r}(t) + h
    \left\{ {\bf v}\left(t + h / 2 \right)
    + \eta(t + h / 2)\left[ {\bf r}(t + h)
    - {\bf R}_0 \right] \right\} ,\\
%
\mathsf{H}(t + h) &\leftarrow e^{-h \eta(t + h / 2)}
    \mathsf{H}(t).
\end{align*}

Most of these equations are identical to their counterparts in the
NVT integrator, but the propagation of positions to time $t + h$
depends on the positions at the same time.  {\sc oopse} carries out
this step iteratively (with a limit of 5 passes through the
iterative loop).  Also, the simulation box $\mathsf{H}$ is scaled
uniformly for one full time step by an exponential factor that
depends on the value of $\eta$ at time $t + h / 2$.  Reshaping the
box uniformly also scales the volume of the box by
\begin{equation}
\mathcal{V}(t + h) \leftarrow e^{ - 3 h \eta(t + h /2)}.
\mathcal{V}(t)
\end{equation}

The {\tt doForces} step for the NPTi integrator is exactly the same
as in both the DLM and NVT integrators.  Once the forces and torques
have been obtained at the new time step, the velocities can be
advanced to the same time value.

{\tt moveB:}
\begin{align*}
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\},
    \left\{{\bf j}(t + h)\right\} ,\\
%
P(t + h) &\leftarrow  \left\{{\bf r}(t + h)\right\},
    \left\{{\bf v}(t + h)\right\}, \\
%
\chi\left(t + h \right) &\leftarrow \chi\left(t + h /
    2 \right) + \frac{h}{2 \tau_T^2} \left( \frac{T(t+h)}
    {T_{\mathrm{target}}} - 1 \right), \\
%
\eta(t + h) &\leftarrow \eta(t + h / 2) +
    \frac{h \mathcal{V}(t + h)}{2 N k_B T(t + h)
    \tau_B^2} \left( P(t + h) - P_{\mathrm{target}} \right), \\
%
{\bf v}\left(t + h \right)  &\leftarrow {\bf v}\left(t
    + h / 2 \right) + \frac{h}{2} \left(
    \frac{{\bf f}(t + h)}{m} - {\bf v}(t + h)
    (\chi(t + h) + \eta(t + h)) \right) ,\\
%
{\bf j}\left(t + h \right)  &\leftarrow {\bf j}\left(t
    + h / 2 \right) + \frac{h}{2} \left( {\bf
    \tau}^b(t + h) - {\bf j}(t + h)
    \chi(t + h) \right) .
\end{align*}

Once again, since ${\bf v}(t + h)$ and ${\bf j}(t + h)$ are required
to caclculate $T(t + h)$, $P(t + h)$, $\chi(t + h)$, and $\eta(t +
h)$, they indirectly depend on their own values at time $t + h$.
{\tt moveB} is therefore done in an iterative fashion until $\chi(t
+ h)$ and $\eta(t + h)$ become self-consistent.  The relative
tolerance for the self-consistency check defaults to a value of
$\mbox{10}^{-6}$, but {\sc oopse} will terminate the iteration after
4 loops even if the consistency check has not been satisfied.

The Melchionna modification of the Nos\'e-Hoover-Andersen algorithm
is known to conserve a Hamiltonian for the extended system that is,
to within a constant, identical to the Gibbs free energy,
\begin{equation}
H_{\mathrm{NPTi}} = V + K + f k_B T_{\mathrm{target}} \left(
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime)
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t).
\end{equation}
Poor choices of $\delta t$, $\tau_T$, or $\tau_B$ can result in
non-conservation of $H_{\mathrm{NPTi}}$, so the conserved quantity
is maintained in the last column of the {\tt .stat} file to allow
checks on the quality of the integration.  It is also known that
this algorithm samples the equilibrium distribution for the enthalpy
(including contributions for the thermostat and barostat),
\begin{equation}
H_{\mathrm{NPTi}} = V + K + \frac{f k_B T_{\mathrm{target}}}{2}
\left( \chi^2 \tau_T^2 + \eta^2 \tau_B^2 \right) +
P_{\mathrm{target}} \mathcal{V}(t).
\end{equation}

Bond constraints are applied at the end of both the {\tt moveA} and
{\tt moveB} portions of the algorithm.  Details on the constraint
algorithms are given in section \ref{oopseSec:rattle}.

\subsection{\label{methodSection:NPTf}Constant-pressure integration with a
flexible box (NPTf)}

There is a relatively simple generalization of the
Nos\'e-Hoover-Andersen method to include changes in the simulation
box {\it shape} as well as in the volume of the box.  This method
utilizes the full $3 \times 3$ pressure tensor and introduces a
tensor of extended variables ($\overleftrightarrow{\eta}$) to
control changes to the box shape.  The equations of motion for this
method are
\begin{eqnarray}
\dot{{\bf r}} & = & {\bf v} + \overleftrightarrow{\eta} \cdot \left( {\bf r} - {\bf R}_0 \right), \\
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - (\overleftrightarrow{\eta} +
\chi \cdot \mathsf{1}) {\bf v}, \\
\dot{\mathsf{A}} & = & \mathsf{A} \cdot
\mbox{ skew}\left(\overleftrightarrow{I}^{-1} \cdot {\bf j}\right) ,\\
\dot{{\bf j}} & = & {\bf j} \times \left(
\overleftrightarrow{I}^{-1} \cdot {\bf j} \right) - \mbox{
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial
V}{\partial \mathsf{A}} \right) - \chi {\bf j} ,\\
\dot{\chi} & = & \frac{1}{\tau_{T}^2} \left(
\frac{T}{T_{\mathrm{target}}} - 1 \right) ,\\
\dot{\overleftrightarrow{\eta}} & = & \frac{1}{\tau_{B}^2 f k_B
T_{\mathrm{target}}} V \left( \overleftrightarrow{\mathsf{P}} - P_{\mathrm{target}}\mathsf{1} \right) ,\\
\dot{\mathsf{H}} & = &  \overleftrightarrow{\eta} \cdot \mathsf{H} .
\label{eq:melchionna2}
\end{eqnarray}

Here, $\mathsf{1}$ is the unit matrix and
$\overleftrightarrow{\mathsf{P}}$ is the pressure tensor.  Again,
the volume, $\mathcal{V} = \det \mathsf{H}$.

The propagation of the equations of motion is nearly identical to
the NPTi integration:

{\tt moveA:}
\begin{align*}
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\
%
\overleftrightarrow{\mathsf{P}}(t) &\leftarrow \left\{{\bf
r}(t)\right\},
    \left\{{\bf v}(t)\right\} ,\\
%
{\bf v}\left(t + h / 2\right)  &\leftarrow {\bf v}(t)
    + \frac{h}{2} \left( \frac{{\bf f}(t)}{m} -
    \left(\chi(t)\mathsf{1} + \overleftrightarrow{\eta}(t) \right) \cdot
    {\bf v}(t) \right), \\
%
{\bf j}\left(t + h / 2 \right)  &\leftarrow {\bf j}(t)
    + \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t)
    \chi(t) \right), \\
%
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left(h *
    {\bf j}(t + h / 2) \overleftrightarrow{\mathsf{I}}^{-1}
    \right), \\
%
\chi\left(t + h / 2 \right) &\leftarrow \chi(t) +
    \frac{h}{2 \tau_T^2} \left( \frac{T(t)}{T_{\mathrm{target}}}
    - 1 \right), \\
%
\overleftrightarrow{\eta}(t + h / 2) &\leftarrow
    \overleftrightarrow{\eta}(t) + \frac{h \mathcal{V}(t)}{2 N k_B
    T(t) \tau_B^2} \left( \overleftrightarrow{\mathsf{P}}(t)
    - P_{\mathrm{target}}\mathsf{1} \right), \\
%
{\bf r}(t + h) &\leftarrow {\bf r}(t) + h \left\{ {\bf v}
    \left(t + h / 2 \right) + \overleftrightarrow{\eta}(t +
    h / 2) \cdot \left[ {\bf r}(t + h)
    - {\bf R}_0 \right] \right\}, \\
%
\mathsf{H}(t + h) &\leftarrow \mathsf{H}(t) \cdot e^{-h
    \overleftrightarrow{\eta}(t + h / 2)} .
\end{align*}
{\sc oopse} uses a power series expansion truncated at second order
for the exponential operation which scales the simulation box.

The {\tt moveB} portion of the algorithm is largely unchanged from
the NPTi integrator:

{\tt moveB:}
\begin{align*}
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\},
    \left\{{\bf j}(t + h)\right\}, \\
%
\overleftrightarrow{\mathsf{P}}(t + h) &\leftarrow \left\{{\bf r}
    (t + h)\right\}, \left\{{\bf v}(t
    + h)\right\}, \left\{{\bf f}(t + h)\right\} ,\\
%
\chi\left(t + h \right) &\leftarrow \chi\left(t + h /
    2 \right) + \frac{h}{2 \tau_T^2} \left( \frac{T(t+
    h)}{T_{\mathrm{target}}} - 1 \right), \\
%
\overleftrightarrow{\eta}(t + h) &\leftarrow
    \overleftrightarrow{\eta}(t + h / 2) +
    \frac{h \mathcal{V}(t + h)}{2 N k_B T(t + h)
    \tau_B^2} \left( \overleftrightarrow{P}(t + h)
    - P_{\mathrm{target}}\mathsf{1} \right) ,\\
%
{\bf v}\left(t + h \right)  &\leftarrow {\bf v}\left(t
    + h / 2 \right) + \frac{h}{2} \left(
    \frac{{\bf f}(t + h)}{m} -
    (\chi(t + h)\mathsf{1} + \overleftrightarrow{\eta}(t
    + h)) \right) \cdot {\bf v}(t + h), \\
%
{\bf j}\left(t + h \right)  &\leftarrow {\bf j}\left(t
    + h / 2 \right) + \frac{h}{2} \left( {\bf \tau}^b(t
    + h) - {\bf j}(t + h) \chi(t + h) \right) .
\end{align*}

The iterative schemes for both {\tt moveA} and {\tt moveB} are
identical to those described for the NPTi integrator.

The NPTf integrator is known to conserve the following Hamiltonian:
\begin{equation}
H_{\mathrm{NPTf}} = V + K + f k_B T_{\mathrm{target}} \left(
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime)
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B
T_{\mathrm{target}}}{2}
\mathrm{Tr}\left[\overleftrightarrow{\eta}(t)\right]^2 \tau_B^2.
\end{equation}

This integrator must be used with care, particularly in liquid
simulations.  Liquids have very small restoring forces in the
off-diagonal directions, and the simulation box can very quickly
form elongated and sheared geometries which become smaller than the
electrostatic or Lennard-Jones cutoff radii.  The NPTf integrator
finds most use in simulating crystals or liquid crystals which
assume non-orthorhombic geometries.

\subsection{\label{methodSection:otherSpecialEnsembles}Other Special Ensembles}

\subsubsection{\label{methodSection:NPAT}NPAT Ensemble}

A comprehensive understanding of structure�Cfunction relations of
biological membrane system ultimately relies on structure and
dynamics of lipid bilayer, which are strongly affected by the
interfacial interaction between lipid molecules and surrounding
media. One quantity to describe the interfacial interaction is so
called the average surface area per lipid. Constat area and constant
lateral pressure simulation can be achieved by extending the
standard NPT ensemble with a different pressure control strategy

\begin{equation}
 \dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll}
                  \frac{{V(P_{\alpha \beta }  - P_{{\rm{target}}} )}}{{\tau_{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }}
                  & \mbox{if $ \alpha = \beta  = z$}\\
                  0 & \mbox{otherwise}\\
           \end{array}
    \right.
\end{equation}

Note that the iterative schemes for NPAT are identical to those
described for the NPTi integrator.

\subsubsection{\label{methodSection:NPrT}NP$\gamma$T Ensemble}

Theoretically, the surface tension $\gamma$ of a stress free
membrane system should be zero since its surface free energy $G$ is
minimum with respect to surface area $A$
\[
\gamma  = \frac{{\partial G}}{{\partial A}}.
\]
However, a surface tension of zero is not appropriate for relatively
small patches of membrane. In order to eliminate the edge effect of
the membrane simulation, a special ensemble, NP$\gamma$T, is
proposed to maintain the lateral surface tension and normal
pressure. The equation of motion for cell size control tensor,
$\eta$, in $NP\gamma T$ is
\begin{equation}
 \dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll}
    - A_{xy} (\gamma _\alpha   - \gamma _{{\rm{target}}} ) & \mbox{$\alpha  = \beta  = x$ or $\alpha  = \beta  = y$}\\
    \frac{{V(P_{\alpha \beta }  - P_{{\rm{target}}} )}}{{\tau _{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}}}} & \mbox{$\alpha  = \beta  = z$} \\
    0 & \mbox{$\alpha  \ne \beta$} \\
       \end{array}
    \right.
\end{equation}
where $ \gamma _{{\rm{target}}}$ is the external surface tension and
the instantaneous surface tensor $\gamma _\alpha$ is given by
\begin{equation}
\gamma _\alpha   =  - h_z( \overleftrightarrow{P} _{\alpha \alpha }
- P_{{\rm{target}}} )
\label{methodEquation:instantaneousSurfaceTensor}
\end{equation}

There is one additional extended system integrator (NPTxyz), in
which each attempt to preserve the target pressure along the box
walls perpendicular to that particular axis.  The lengths of the box
axes are allowed to fluctuate independently, but the angle between
the box axes does not change. It should be noted that the NPTxyz
integrator is a special case of $NP\gamma T$ if the surface tension
$\gamma$ is set to zero.

%\section{\label{methodSection:constraintMethod}Constraint Method}

%\subsection{\label{methodSection:bondConstraint}Bond Constraint for Rigid Body}

%\subsection{\label{methodSection:zcons}Z-constraint Method}

\section{\label{methodSection:langevin}Integrators for Langevin Dynamics of Rigid Bodies}

\subsection{\label{methodSection:temperature}Temperature Control}

\subsection{\label{methodSection:pressureControl}Pressure Control}

\section{\label{methodSection:hydrodynamics}Hydrodynamics}

%\section{\label{methodSection:coarseGrained}Coarse-Grained Modeling}

%\section{\label{methodSection:moleculeScale}Molecular-Scale Modeling}
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