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\chapter{\label{chapt:methodology}METHODOLOGY} |
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|
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\section{\label{methodSection:rigidBodyIntegrators}Integrators for Rigid Body Motion in Molecular Dynamics} |
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|
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In order to mimic the experiments, which are usually performed under |
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constant temperature and/or pressure, extended Hamiltonian system |
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methods have been developed to generate statistical ensembles, such |
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as canonical ensemble and isobaric-isothermal ensemble \textit{etc}. |
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In addition to the standard ensemble, specific ensembles have been |
| 10 |
developed to account for the anisotropy between the lateral and |
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normal directions of membranes. The $NPAT$ ensemble, in which the |
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normal pressure and the lateral surface area of the membrane are |
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kept constant, and the $NP\gamma T$ ensemble, in which the normal |
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pressure and the lateral surface tension are kept constant were |
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proposed to address this issue. |
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|
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Integration schemes for rotational motion of the rigid molecules in |
| 18 |
microcanonical ensemble have been extensively studied in the last |
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two decades. Matubayasi and Nakahara developed a time-reversible |
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integrator for rigid bodies in quaternion representation. Although |
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it is not symplectic, this integrator still demonstrates a better |
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long-time energy conservation than traditional methods because of |
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the time-reversible nature. Extending Trotter-Suzuki to general |
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system with a flat phase space, Miller and his colleagues devised an |
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novel symplectic, time-reversible and volume-preserving integrator |
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in quaternion representation, which was shown to be superior to the |
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time-reversible integrator of Matubayasi and Nakahara. However, all |
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of the integrators in quaternion representation suffer from the |
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computational penalty of constructing a rotation matrix from |
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quaternions to evolve coordinates and velocities at every time step. |
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An alternative integration scheme utilizing rotation matrix directly |
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proposed by Dullweber, Leimkuhler and McLachlan (DLM) also preserved |
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the same structural properties of the Hamiltonian flow. In this |
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section, the integration scheme of DLM method will be reviewed and |
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extended to other ensembles. |
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|
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\subsection{\label{methodSection:DLM}Integrating the Equations of Motion: the |
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DLM method} |
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|
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The DLM method uses a Trotter factorization of the orientational |
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propagator. This has three effects: |
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\begin{enumerate} |
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\item the integrator is area-preserving in phase space (i.e. it is |
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{\it symplectic}), |
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\item the integrator is time-{\it reversible}, making it suitable for Hybrid |
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Monte Carlo applications, and |
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\item the error for a single time step is of order $\mathcal{O}\left(h^4\right)$ |
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for timesteps of length $h$. |
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\end{enumerate} |
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|
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The integration of the equations of motion is carried out in a |
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velocity-Verlet style 2-part algorithm, where $h= \delta t$: |
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|
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{\tt moveA:} |
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\begin{align*} |
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{\bf v}\left(t + h / 2\right) &\leftarrow {\bf v}(t) |
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+ \frac{h}{2} \left( {\bf f}(t) / m \right), \\ |
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% |
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{\bf r}(t + h) &\leftarrow {\bf r}(t) |
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+ h {\bf v}\left(t + h / 2 \right), \\ |
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% |
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{\bf j}\left(t + h / 2 \right) &\leftarrow {\bf j}(t) |
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+ \frac{h}{2} {\bf \tau}^b(t), \\ |
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% |
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\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left( h {\bf j} |
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(t + h / 2) \cdot \overleftrightarrow{\mathsf{I}}^{-1} \right). |
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\end{align*} |
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|
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In this context, the $\mathrm{rotate}$ function is the reversible |
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product of the three body-fixed rotations, |
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\begin{equation} |
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\mathrm{rotate}({\bf a}) = \mathsf{G}_x(a_x / 2) \cdot |
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\mathsf{G}_y(a_y / 2) \cdot \mathsf{G}_z(a_z) \cdot \mathsf{G}_y(a_y |
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/ 2) \cdot \mathsf{G}_x(a_x /2), |
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\end{equation} |
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where each rotational propagator, $\mathsf{G}_\alpha(\theta)$, |
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rotates both the rotation matrix ($\mathsf{A}$) and the body-fixed |
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angular momentum (${\bf j}$) by an angle $\theta$ around body-fixed |
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axis $\alpha$, |
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\begin{equation} |
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\mathsf{G}_\alpha( \theta ) = \left\{ |
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\begin{array}{lcl} |
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\mathsf{A}(t) & \leftarrow & \mathsf{A}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\ |
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{\bf j}(t) & \leftarrow & \mathsf{R}_\alpha(\theta) \cdot {\bf |
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j}(0). |
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\end{array} |
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\right. |
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\end{equation} |
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$\mathsf{R}_\alpha$ is a quadratic approximation to the single-axis |
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rotation matrix. For example, in the small-angle limit, the |
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rotation matrix around the body-fixed x-axis can be approximated as |
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\begin{equation} |
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\mathsf{R}_x(\theta) \approx \left( |
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\begin{array}{ccc} |
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1 & 0 & 0 \\ |
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0 & \frac{1-\theta^2 / 4}{1 + \theta^2 / 4} & -\frac{\theta}{1+ |
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\theta^2 / 4} \\ |
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0 & \frac{\theta}{1+ \theta^2 / 4} & \frac{1-\theta^2 / 4}{1 + |
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\theta^2 / 4} |
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\end{array} |
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\right). |
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\end{equation} |
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All other rotations follow in a straightforward manner. |
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|
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After the first part of the propagation, the forces and body-fixed |
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torques are calculated at the new positions and orientations |
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|
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{\tt doForces:} |
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\begin{align*} |
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{\bf f}(t + h) &\leftarrow |
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- \left(\frac{\partial V}{\partial {\bf r}}\right)_{{\bf r}(t + h)}, \\ |
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% |
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{\bf \tau}^{s}(t + h) &\leftarrow {\bf u}(t + h) |
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\times \frac{\partial V}{\partial {\bf u}}, \\ |
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% |
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{\bf \tau}^{b}(t + h) &\leftarrow \mathsf{A}(t + h) |
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\cdot {\bf \tau}^s(t + h). |
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\end{align*} |
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|
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{\sc oopse} automatically updates ${\bf u}$ when the rotation matrix |
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$\mathsf{A}$ is calculated in {\tt moveA}. Once the forces and |
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torques have been obtained at the new time step, the velocities can |
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be advanced to the same time value. |
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|
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{\tt moveB:} |
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\begin{align*} |
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{\bf v}\left(t + h \right) &\leftarrow {\bf v}\left(t + h / 2 |
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\right) |
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+ \frac{h}{2} \left( {\bf f}(t + h) / m \right), \\ |
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% |
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{\bf j}\left(t + h \right) &\leftarrow {\bf j}\left(t + h / 2 |
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\right) |
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+ \frac{h}{2} {\bf \tau}^b(t + h) . |
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\end{align*} |
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|
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The matrix rotations used in the DLM method end up being more costly |
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computationally than the simpler arithmetic quaternion propagation. |
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With the same time step, a 1000-molecule water simulation shows an |
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average 7\% increase in computation time using the DLM method in |
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place of quaternions. This cost is more than justified when |
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comparing the energy conservation of the two methods as illustrated |
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in Fig.~\ref{methodFig:timestep}. |
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|
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\begin{figure} |
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\centering |
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\includegraphics[width=\linewidth]{timeStep.eps} |
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\caption[Energy conservation for quaternion versus DLM |
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dynamics]{Energy conservation using quaternion based integration |
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versus the method proposed by Dullweber \emph{et al.} with |
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increasing time step. For each time step, the dotted line is total |
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energy using the DLM integrator, and the solid line comes from the |
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quaternion integrator. The larger time step plots are shifted up |
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from the true energy baseline for clarity.} |
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\label{methodFig:timestep} |
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\end{figure} |
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|
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In Fig.~\ref{methodFig:timestep}, the resulting energy drift at |
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various time steps for both the DLM and quaternion integration |
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schemes is compared. All of the 1000 molecule water simulations |
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started with the same configuration, and the only difference was the |
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method for handling rotational motion. At time steps of 0.1 and 0.5 |
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fs, both methods for propagating molecule rotation conserve energy |
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fairly well, with the quaternion method showing a slight energy |
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drift over time in the 0.5 fs time step simulation. At time steps of |
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1 and 2 fs, the energy conservation benefits of the DLM method are |
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clearly demonstrated. Thus, while maintaining the same degree of |
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energy conservation, one can take considerably longer time steps, |
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leading to an overall reduction in computation time. |
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|
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\subsection{\label{methodSection:NVT}Nos\'{e}-Hoover Thermostatting} |
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|
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The Nos\'e-Hoover equations of motion are given by\cite{Hoover1985} |
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\begin{eqnarray} |
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\dot{{\bf r}} & = & {\bf v}, \\ |
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\dot{{\bf v}} & = & \frac{{\bf f}}{m} - \chi {\bf v} ,\\ |
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\dot{\mathsf{A}} & = & \mathsf{A} \cdot |
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\mbox{ skew}\left(\overleftrightarrow{\mathsf{I}}^{-1} \cdot {\bf j}\right), \\ |
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\dot{{\bf j}} & = & {\bf j} \times \left( |
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\overleftrightarrow{\mathsf{I}}^{-1} \cdot {\bf j} \right) - \mbox{ |
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rot}\left(\mathsf{A}^{T} \cdot \frac{\partial V}{\partial |
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\mathsf{A}} \right) - \chi {\bf j}. \label{eq:nosehoovereom} |
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\end{eqnarray} |
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|
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$\chi$ is an ``extra'' variable included in the extended system, and |
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it is propagated using the first order equation of motion |
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\begin{equation} |
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\dot{\chi} = \frac{1}{\tau_{T}^2} \left( |
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\frac{T}{T_{\mathrm{target}}} - 1 \right). \label{eq:nosehooverext} |
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\end{equation} |
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|
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The instantaneous temperature $T$ is proportional to the total |
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kinetic energy (both translational and orientational) and is given |
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by |
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\begin{equation} |
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T = \frac{2 K}{f k_B} |
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\end{equation} |
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Here, $f$ is the total number of degrees of freedom in the system, |
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\begin{equation} |
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f = 3 N + 3 N_{\mathrm{orient}} - N_{\mathrm{constraints}}, |
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\end{equation} |
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and $K$ is the total kinetic energy, |
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\begin{equation} |
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K = \sum_{i=1}^{N} \frac{1}{2} m_i {\bf v}_i^T \cdot {\bf v}_i + |
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\sum_{i=1}^{N_{\mathrm{orient}}} \frac{1}{2} {\bf j}_i^T \cdot |
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\overleftrightarrow{\mathsf{I}}_i^{-1} \cdot {\bf j}_i. |
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\end{equation} |
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|
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In eq.(\ref{eq:nosehooverext}), $\tau_T$ is the time constant for |
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relaxation of the temperature to the target value. To set values |
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for $\tau_T$ or $T_{\mathrm{target}}$ in a simulation, one would use |
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the {\tt tauThermostat} and {\tt targetTemperature} keywords in the |
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{\tt .bass} file. The units for {\tt tauThermostat} are fs, and the |
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units for the {\tt targetTemperature} are degrees K. The |
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integration of the equations of motion is carried out in a |
| 215 |
velocity-Verlet style 2 part algorithm: |
| 216 |
|
| 217 |
{\tt moveA:} |
| 218 |
\begin{align*} |
| 219 |
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\ |
| 220 |
% |
| 221 |
{\bf v}\left(t + h / 2\right) &\leftarrow {\bf v}(t) |
| 222 |
+ \frac{h}{2} \left( \frac{{\bf f}(t)}{m} - {\bf v}(t) |
| 223 |
\chi(t)\right), \\ |
| 224 |
% |
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{\bf r}(t + h) &\leftarrow {\bf r}(t) |
| 226 |
+ h {\bf v}\left(t + h / 2 \right) ,\\ |
| 227 |
% |
| 228 |
{\bf j}\left(t + h / 2 \right) &\leftarrow {\bf j}(t) |
| 229 |
+ \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t) |
| 230 |
\chi(t) \right) ,\\ |
| 231 |
% |
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\mathsf{A}(t + h) &\leftarrow \mathrm{rotate} |
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\left(h * {\bf j}(t + h / 2) |
| 234 |
\overleftrightarrow{\mathsf{I}}^{-1} \right) ,\\ |
| 235 |
% |
| 236 |
\chi\left(t + h / 2 \right) &\leftarrow \chi(t) |
| 237 |
+ \frac{h}{2 \tau_T^2} \left( \frac{T(t)} |
| 238 |
{T_{\mathrm{target}}} - 1 \right) . |
| 239 |
\end{align*} |
| 240 |
|
| 241 |
Here $\mathrm{rotate}(h * {\bf j} |
| 242 |
\overleftrightarrow{\mathsf{I}}^{-1})$ is the same symplectic |
| 243 |
Trotter factorization of the three rotation operations that was |
| 244 |
discussed in the section on the DLM integrator. Note that this |
| 245 |
operation modifies both the rotation matrix $\mathsf{A}$ and the |
| 246 |
angular momentum ${\bf j}$. {\tt moveA} propagates velocities by a |
| 247 |
half time step, and positional degrees of freedom by a full time |
| 248 |
step. The new positions (and orientations) are then used to |
| 249 |
calculate a new set of forces and torques in exactly the same way |
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they are calculated in the {\tt doForces} portion of the DLM |
| 251 |
integrator. |
| 252 |
|
| 253 |
Once the forces and torques have been obtained at the new time step, |
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the temperature, velocities, and the extended system variable can be |
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advanced to the same time value. |
| 256 |
|
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{\tt moveB:} |
| 258 |
\begin{align*} |
| 259 |
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\}, |
| 260 |
\left\{{\bf j}(t + h)\right\}, \\ |
| 261 |
% |
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\chi\left(t + h \right) &\leftarrow \chi\left(t + h / |
| 263 |
2 \right) + \frac{h}{2 \tau_T^2} \left( \frac{T(t+h)} |
| 264 |
{T_{\mathrm{target}}} - 1 \right), \\ |
| 265 |
% |
| 266 |
{\bf v}\left(t + h \right) &\leftarrow {\bf v}\left(t |
| 267 |
+ h / 2 \right) + \frac{h}{2} \left( |
| 268 |
\frac{{\bf f}(t + h)}{m} - {\bf v}(t + h) |
| 269 |
\chi(t h)\right) ,\\ |
| 270 |
% |
| 271 |
{\bf j}\left(t + h \right) &\leftarrow {\bf j}\left(t |
| 272 |
+ h / 2 \right) + \frac{h}{2} |
| 273 |
\left( {\bf \tau}^b(t + h) - {\bf j}(t + h) |
| 274 |
\chi(t + h) \right) . |
| 275 |
\end{align*} |
| 276 |
|
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Since ${\bf v}(t + h)$ and ${\bf j}(t + h)$ are required to |
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caclculate $T(t + h)$ as well as $\chi(t + h)$, they indirectly |
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depend on their own values at time $t + h$. {\tt moveB} is |
| 280 |
therefore done in an iterative fashion until $\chi(t + h)$ becomes |
| 281 |
self-consistent. The relative tolerance for the self-consistency |
| 282 |
check defaults to a value of $\mbox{10}^{-6}$, but {\sc oopse} will |
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terminate the iteration after 4 loops even if the consistency check |
| 284 |
has not been satisfied. |
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|
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The Nos\'e-Hoover algorithm is known to conserve a Hamiltonian for |
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the extended system that is, to within a constant, identical to the |
| 288 |
Helmholtz free energy,\cite{Melchionna1993} |
| 289 |
\begin{equation} |
| 290 |
H_{\mathrm{NVT}} = V + K + f k_B T_{\mathrm{target}} \left( |
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\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
| 292 |
dt^\prime \right). |
| 293 |
\end{equation} |
| 294 |
Poor choices of $h$ or $\tau_T$ can result in non-conservation of |
| 295 |
$H_{\mathrm{NVT}}$, so the conserved quantity is maintained in the |
| 296 |
last column of the {\tt .stat} file to allow checks on the quality |
| 297 |
of the integration. |
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|
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\subsection{\label{methodSection:NPTi}Constant-pressure integration with |
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isotropic box deformations (NPTi)} |
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|
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To carry out isobaric-isothermal ensemble calculations {\sc oopse} |
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implements the Melchionna modifications to the |
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Nos\'e-Hoover-Andersen equations of motion,\cite{Melchionna1993} |
| 305 |
|
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\begin{eqnarray} |
| 307 |
\dot{{\bf r}} & = & {\bf v} + \eta \left( {\bf r} - {\bf R}_0 \right), \\ |
| 308 |
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - (\eta + \chi) {\bf v}, \\ |
| 309 |
\dot{\mathsf{A}} & = & \mathsf{A} \cdot |
| 310 |
\mbox{ skew}\left(\overleftrightarrow{I}^{-1} \cdot {\bf j}\right),\\ |
| 311 |
\dot{{\bf j}} & = & {\bf j} \times \left( |
| 312 |
\overleftrightarrow{I}^{-1} \cdot {\bf j} \right) - \mbox{ |
| 313 |
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial |
| 314 |
V}{\partial \mathsf{A}} \right) - \chi {\bf j}, \\ |
| 315 |
\dot{\chi} & = & \frac{1}{\tau_{T}^2} \left( |
| 316 |
\frac{T}{T_{\mathrm{target}}} - 1 \right) ,\\ |
| 317 |
\dot{\eta} & = & \frac{1}{\tau_{B}^2 f k_B T_{\mathrm{target}}} V |
| 318 |
\left( P - |
| 319 |
P_{\mathrm{target}} \right), \\ |
| 320 |
\dot{\mathcal{V}} & = & 3 \mathcal{V} \eta . \label{eq:melchionna1} |
| 321 |
\end{eqnarray} |
| 322 |
|
| 323 |
$\chi$ and $\eta$ are the ``extra'' degrees of freedom in the |
| 324 |
extended system. $\chi$ is a thermostat, and it has the same |
| 325 |
function as it does in the Nos\'e-Hoover NVT integrator. $\eta$ is |
| 326 |
a barostat which controls changes to the volume of the simulation |
| 327 |
box. ${\bf R}_0$ is the location of the center of mass for the |
| 328 |
entire system, and $\mathcal{V}$ is the volume of the simulation |
| 329 |
box. At any time, the volume can be calculated from the determinant |
| 330 |
of the matrix which describes the box shape: |
| 331 |
\begin{equation} |
| 332 |
\mathcal{V} = \det(\mathsf{H}). |
| 333 |
\end{equation} |
| 334 |
|
| 335 |
The NPTi integrator requires an instantaneous pressure. This |
| 336 |
quantity is calculated via the pressure tensor, |
| 337 |
\begin{equation} |
| 338 |
\overleftrightarrow{\mathsf{P}}(t) = \frac{1}{\mathcal{V}(t)} \left( |
| 339 |
\sum_{i=1}^{N} m_i {\bf v}_i(t) \otimes {\bf v}_i(t) \right) + |
| 340 |
\overleftrightarrow{\mathsf{W}}(t). |
| 341 |
\end{equation} |
| 342 |
The kinetic contribution to the pressure tensor utilizes the {\it |
| 343 |
outer} product of the velocities denoted by the $\otimes$ symbol. |
| 344 |
The stress tensor is calculated from another outer product of the |
| 345 |
inter-atomic separation vectors (${\bf r}_{ij} = {\bf r}_j - {\bf |
| 346 |
r}_i$) with the forces between the same two atoms, |
| 347 |
\begin{equation} |
| 348 |
\overleftrightarrow{\mathsf{W}}(t) = \sum_{i} \sum_{j>i} {\bf |
| 349 |
r}_{ij}(t) \otimes {\bf f}_{ij}(t). |
| 350 |
\end{equation} |
| 351 |
The instantaneous pressure is then simply obtained from the trace of |
| 352 |
the Pressure tensor, |
| 353 |
\begin{equation} |
| 354 |
P(t) = \frac{1}{3} \mathrm{Tr} \left( |
| 355 |
\overleftrightarrow{\mathsf{P}}(t). \right) |
| 356 |
\end{equation} |
| 357 |
|
| 358 |
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 |
| 364 |
integrator, the integration of the equations of motion is carried |
| 365 |
out in a velocity-Verlet style 2 part algorithm: |
| 366 |
|
| 367 |
{\tt moveA:} |
| 368 |
\begin{align*} |
| 369 |
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\ |
| 370 |
% |
| 371 |
P(t) &\leftarrow \left\{{\bf r}(t)\right\}, \left\{{\bf v}(t)\right\} ,\\ |
| 372 |
% |
| 373 |
{\bf v}\left(t + h / 2\right) &\leftarrow {\bf v}(t) |
| 374 |
+ \frac{h}{2} \left( \frac{{\bf f}(t)}{m} - {\bf v}(t) |
| 375 |
\left(\chi(t) + \eta(t) \right) \right), \\ |
| 376 |
% |
| 377 |
{\bf j}\left(t + h / 2 \right) &\leftarrow {\bf j}(t) |
| 378 |
+ \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t) |
| 379 |
\chi(t) \right), \\ |
| 380 |
% |
| 381 |
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left(h * |
| 382 |
{\bf j}(t + h / 2) \overleftrightarrow{\mathsf{I}}^{-1} |
| 383 |
\right) ,\\ |
| 384 |
% |
| 385 |
\chi\left(t + h / 2 \right) &\leftarrow \chi(t) + |
| 386 |
\frac{h}{2 \tau_T^2} \left( \frac{T(t)}{T_{\mathrm{target}}} - 1 |
| 387 |
\right) ,\\ |
| 388 |
% |
| 389 |
\eta(t + h / 2) &\leftarrow \eta(t) + \frac{h |
| 390 |
\mathcal{V}(t)}{2 N k_B T(t) \tau_B^2} \left( P(t) |
| 391 |
- P_{\mathrm{target}} \right), \\ |
| 392 |
% |
| 393 |
{\bf r}(t + h) &\leftarrow {\bf r}(t) + h |
| 394 |
\left\{ {\bf v}\left(t + h / 2 \right) |
| 395 |
+ \eta(t + h / 2)\left[ {\bf r}(t + h) |
| 396 |
- {\bf R}_0 \right] \right\} ,\\ |
| 397 |
% |
| 398 |
\mathsf{H}(t + h) &\leftarrow e^{-h \eta(t + h / 2)} |
| 399 |
\mathsf{H}(t). |
| 400 |
\end{align*} |
| 401 |
|
| 402 |
Most of these equations are identical to their counterparts in the |
| 403 |
NVT integrator, but the propagation of positions to time $t + h$ |
| 404 |
depends on the positions at the same time. {\sc oopse} carries out |
| 405 |
this step iteratively (with a limit of 5 passes through the |
| 406 |
iterative loop). Also, the simulation box $\mathsf{H}$ is scaled |
| 407 |
uniformly for one full time step by an exponential factor that |
| 408 |
depends on the value of $\eta$ at time $t + h / 2$. Reshaping the |
| 409 |
box uniformly also scales the volume of the box by |
| 410 |
\begin{equation} |
| 411 |
\mathcal{V}(t + h) \leftarrow e^{ - 3 h \eta(t + h /2)}. |
| 412 |
\mathcal{V}(t) |
| 413 |
\end{equation} |
| 414 |
|
| 415 |
The {\tt doForces} step for the NPTi integrator is exactly the same |
| 416 |
as in both the DLM and NVT integrators. Once the forces and torques |
| 417 |
have been obtained at the new time step, the velocities can be |
| 418 |
advanced to the same time value. |
| 419 |
|
| 420 |
{\tt moveB:} |
| 421 |
\begin{align*} |
| 422 |
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\}, |
| 423 |
\left\{{\bf j}(t + h)\right\} ,\\ |
| 424 |
% |
| 425 |
P(t + h) &\leftarrow \left\{{\bf r}(t + h)\right\}, |
| 426 |
\left\{{\bf v}(t + h)\right\}, \\ |
| 427 |
% |
| 428 |
\chi\left(t + h \right) &\leftarrow \chi\left(t + h / |
| 429 |
2 \right) + \frac{h}{2 \tau_T^2} \left( \frac{T(t+h)} |
| 430 |
{T_{\mathrm{target}}} - 1 \right), \\ |
| 431 |
% |
| 432 |
\eta(t + h) &\leftarrow \eta(t + h / 2) + |
| 433 |
\frac{h \mathcal{V}(t + h)}{2 N k_B T(t + h) |
| 434 |
\tau_B^2} \left( P(t + h) - P_{\mathrm{target}} \right), \\ |
| 435 |
% |
| 436 |
{\bf v}\left(t + h \right) &\leftarrow {\bf v}\left(t |
| 437 |
+ h / 2 \right) + \frac{h}{2} \left( |
| 438 |
\frac{{\bf f}(t + h)}{m} - {\bf v}(t + h) |
| 439 |
(\chi(t + h) + \eta(t + h)) \right) ,\\ |
| 440 |
% |
| 441 |
{\bf j}\left(t + h \right) &\leftarrow {\bf j}\left(t |
| 442 |
+ h / 2 \right) + \frac{h}{2} \left( {\bf |
| 443 |
\tau}^b(t + h) - {\bf j}(t + h) |
| 444 |
\chi(t + h) \right) . |
| 445 |
\end{align*} |
| 446 |
|
| 447 |
Once again, since ${\bf v}(t + h)$ and ${\bf j}(t + h)$ are required |
| 448 |
to caclculate $T(t + h)$, $P(t + h)$, $\chi(t + h)$, and $\eta(t + |
| 449 |
h)$, they indirectly depend on their own values at time $t + h$. |
| 450 |
{\tt moveB} is therefore done in an iterative fashion until $\chi(t |
| 451 |
+ h)$ 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. |
| 455 |
|
| 456 |
The Melchionna modification of the Nos\'e-Hoover-Andersen algorithm |
| 457 |
is known to conserve a Hamiltonian for the extended system that is, |
| 458 |
to within a constant, identical to the Gibbs free energy, |
| 459 |
\begin{equation} |
| 460 |
H_{\mathrm{NPTi}} = V + K + f k_B T_{\mathrm{target}} \left( |
| 461 |
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
| 462 |
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t). |
| 463 |
\end{equation} |
| 464 |
Poor choices of $\delta t$, $\tau_T$, or $\tau_B$ can result in |
| 465 |
non-conservation of $H_{\mathrm{NPTi}}$, so the conserved quantity |
| 466 |
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), |
| 470 |
\begin{equation} |
| 471 |
H_{\mathrm{NPTi}} = V + K + \frac{f k_B T_{\mathrm{target}}}{2} |
| 472 |
\left( \chi^2 \tau_T^2 + \eta^2 \tau_B^2 \right) + |
| 473 |
P_{\mathrm{target}} \mathcal{V}(t). |
| 474 |
\end{equation} |
| 475 |
|
| 476 |
Bond constraints are applied at the end of both the {\tt moveA} and |
| 477 |
{\tt moveB} portions of the algorithm. |
| 478 |
|
| 479 |
\subsection{\label{methodSection:NPTf}Constant-pressure integration with a |
| 480 |
flexible box (NPTf)} |
| 481 |
|
| 482 |
There is a relatively simple generalization of the |
| 483 |
Nos\'e-Hoover-Andersen method to include changes in the simulation |
| 484 |
box {\it shape} as well as in the volume of the box. This method |
| 485 |
utilizes the full $3 \times 3$ pressure tensor and introduces a |
| 486 |
tensor of extended variables ($\overleftrightarrow{\eta}$) to |
| 487 |
control changes to the box shape. The equations of motion for this |
| 488 |
method are |
| 489 |
\begin{eqnarray} |
| 490 |
\dot{{\bf r}} & = & {\bf v} + \overleftrightarrow{\eta} \cdot \left( {\bf r} - {\bf R}_0 \right), \\ |
| 491 |
\dot{{\bf v}} & = & \frac{{\bf f}}{m} - (\overleftrightarrow{\eta} + |
| 492 |
\chi \cdot \mathsf{1}) {\bf v}, \\ |
| 493 |
\dot{\mathsf{A}} & = & \mathsf{A} \cdot |
| 494 |
\mbox{ skew}\left(\overleftrightarrow{I}^{-1} \cdot {\bf j}\right) ,\\ |
| 495 |
\dot{{\bf j}} & = & {\bf j} \times \left( |
| 496 |
\overleftrightarrow{I}^{-1} \cdot {\bf j} \right) - \mbox{ |
| 497 |
rot}\left(\mathsf{A}^{T} \cdot \frac{\partial |
| 498 |
V}{\partial \mathsf{A}} \right) - \chi {\bf j} ,\\ |
| 499 |
\dot{\chi} & = & \frac{1}{\tau_{T}^2} \left( |
| 500 |
\frac{T}{T_{\mathrm{target}}} - 1 \right) ,\\ |
| 501 |
\dot{\overleftrightarrow{\eta}} & = & \frac{1}{\tau_{B}^2 f k_B |
| 502 |
T_{\mathrm{target}}} V \left( \overleftrightarrow{\mathsf{P}} - P_{\mathrm{target}}\mathsf{1} \right) ,\\ |
| 503 |
\dot{\mathsf{H}} & = & \overleftrightarrow{\eta} \cdot \mathsf{H} . |
| 504 |
\label{eq:melchionna2} |
| 505 |
\end{eqnarray} |
| 506 |
|
| 507 |
Here, $\mathsf{1}$ is the unit matrix and |
| 508 |
$\overleftrightarrow{\mathsf{P}}$ is the pressure tensor. Again, |
| 509 |
the volume, $\mathcal{V} = \det \mathsf{H}$. |
| 510 |
|
| 511 |
The propagation of the equations of motion is nearly identical to |
| 512 |
the NPTi integration: |
| 513 |
|
| 514 |
{\tt moveA:} |
| 515 |
\begin{align*} |
| 516 |
T(t) &\leftarrow \left\{{\bf v}(t)\right\}, \left\{{\bf j}(t)\right\} ,\\ |
| 517 |
% |
| 518 |
\overleftrightarrow{\mathsf{P}}(t) &\leftarrow \left\{{\bf |
| 519 |
r}(t)\right\}, |
| 520 |
\left\{{\bf v}(t)\right\} ,\\ |
| 521 |
% |
| 522 |
{\bf v}\left(t + h / 2\right) &\leftarrow {\bf v}(t) |
| 523 |
+ \frac{h}{2} \left( \frac{{\bf f}(t)}{m} - |
| 524 |
\left(\chi(t)\mathsf{1} + \overleftrightarrow{\eta}(t) \right) \cdot |
| 525 |
{\bf v}(t) \right), \\ |
| 526 |
% |
| 527 |
{\bf j}\left(t + h / 2 \right) &\leftarrow {\bf j}(t) |
| 528 |
+ \frac{h}{2} \left( {\bf \tau}^b(t) - {\bf j}(t) |
| 529 |
\chi(t) \right), \\ |
| 530 |
% |
| 531 |
\mathsf{A}(t + h) &\leftarrow \mathrm{rotate}\left(h * |
| 532 |
{\bf j}(t + h / 2) \overleftrightarrow{\mathsf{I}}^{-1} |
| 533 |
\right), \\ |
| 534 |
% |
| 535 |
\chi\left(t + h / 2 \right) &\leftarrow \chi(t) + |
| 536 |
\frac{h}{2 \tau_T^2} \left( \frac{T(t)}{T_{\mathrm{target}}} |
| 537 |
- 1 \right), \\ |
| 538 |
% |
| 539 |
\overleftrightarrow{\eta}(t + h / 2) &\leftarrow |
| 540 |
\overleftrightarrow{\eta}(t) + \frac{h \mathcal{V}(t)}{2 N k_B |
| 541 |
T(t) \tau_B^2} \left( \overleftrightarrow{\mathsf{P}}(t) |
| 542 |
- P_{\mathrm{target}}\mathsf{1} \right), \\ |
| 543 |
% |
| 544 |
{\bf r}(t + h) &\leftarrow {\bf r}(t) + h \left\{ {\bf v} |
| 545 |
\left(t + h / 2 \right) + \overleftrightarrow{\eta}(t + |
| 546 |
h / 2) \cdot \left[ {\bf r}(t + h) |
| 547 |
- {\bf R}_0 \right] \right\}, \\ |
| 548 |
% |
| 549 |
\mathsf{H}(t + h) &\leftarrow \mathsf{H}(t) \cdot e^{-h |
| 550 |
\overleftrightarrow{\eta}(t + h / 2)} . |
| 551 |
\end{align*} |
| 552 |
{\sc oopse} uses a power series expansion truncated at second order |
| 553 |
for the exponential operation which scales the simulation box. |
| 554 |
|
| 555 |
The {\tt moveB} portion of the algorithm is largely unchanged from |
| 556 |
the NPTi integrator: |
| 557 |
|
| 558 |
{\tt moveB:} |
| 559 |
\begin{align*} |
| 560 |
T(t + h) &\leftarrow \left\{{\bf v}(t + h)\right\}, |
| 561 |
\left\{{\bf j}(t + h)\right\}, \\ |
| 562 |
% |
| 563 |
\overleftrightarrow{\mathsf{P}}(t + h) &\leftarrow \left\{{\bf r} |
| 564 |
(t + h)\right\}, \left\{{\bf v}(t |
| 565 |
+ h)\right\}, \left\{{\bf f}(t + h)\right\} ,\\ |
| 566 |
% |
| 567 |
\chi\left(t + h \right) &\leftarrow \chi\left(t + h / |
| 568 |
2 \right) + \frac{h}{2 \tau_T^2} \left( \frac{T(t+ |
| 569 |
h)}{T_{\mathrm{target}}} - 1 \right), \\ |
| 570 |
% |
| 571 |
\overleftrightarrow{\eta}(t + h) &\leftarrow |
| 572 |
\overleftrightarrow{\eta}(t + h / 2) + |
| 573 |
\frac{h \mathcal{V}(t + h)}{2 N k_B T(t + h) |
| 574 |
\tau_B^2} \left( \overleftrightarrow{P}(t + h) |
| 575 |
- P_{\mathrm{target}}\mathsf{1} \right) ,\\ |
| 576 |
% |
| 577 |
{\bf v}\left(t + h \right) &\leftarrow {\bf v}\left(t |
| 578 |
+ h / 2 \right) + \frac{h}{2} \left( |
| 579 |
\frac{{\bf f}(t + h)}{m} - |
| 580 |
(\chi(t + h)\mathsf{1} + \overleftrightarrow{\eta}(t |
| 581 |
+ h)) \right) \cdot {\bf v}(t + h), \\ |
| 582 |
% |
| 583 |
{\bf j}\left(t + h \right) &\leftarrow {\bf j}\left(t |
| 584 |
+ h / 2 \right) + \frac{h}{2} \left( {\bf \tau}^b(t |
| 585 |
+ h) - {\bf j}(t + h) \chi(t + h) \right) . |
| 586 |
\end{align*} |
| 587 |
|
| 588 |
The iterative schemes for both {\tt moveA} and {\tt moveB} are |
| 589 |
identical to those described for the NPTi integrator. |
| 590 |
|
| 591 |
The NPTf integrator is known to conserve the following Hamiltonian: |
| 592 |
\begin{equation} |
| 593 |
H_{\mathrm{NPTf}} = V + K + f k_B T_{\mathrm{target}} \left( |
| 594 |
\frac{\tau_{T}^2 \chi^2(t)}{2} + \int_{0}^{t} \chi(t^\prime) |
| 595 |
dt^\prime \right) + P_{\mathrm{target}} \mathcal{V}(t) + \frac{f k_B |
| 596 |
T_{\mathrm{target}}}{2} |
| 597 |
\mathrm{Tr}\left[\overleftrightarrow{\eta}(t)\right]^2 \tau_B^2. |
| 598 |
\end{equation} |
| 599 |
|
| 600 |
This integrator must be used with care, particularly in liquid |
| 601 |
simulations. Liquids have very small restoring forces in the |
| 602 |
off-diagonal directions, and the simulation box can very quickly |
| 603 |
form elongated and sheared geometries which become smaller than the |
| 604 |
electrostatic or Lennard-Jones cutoff radii. The NPTf integrator |
| 605 |
finds most use in simulating crystals or liquid crystals which |
| 606 |
assume non-orthorhombic geometries. |
| 607 |
|
| 608 |
\subsection{\label{methodSection:otherSpecialEnsembles}Other Special Ensembles} |
| 609 |
|
| 610 |
\subsubsection{\label{methodSection:NPAT}\textbf{NPAT Ensemble}} |
| 611 |
|
| 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 |
|
| 621 |
\begin{equation} |
| 622 |
\dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll} |
| 623 |
\frac{{V(P_{\alpha \beta } - P_{{\rm{target}}} )}}{{\tau_{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}} }} |
| 624 |
& \mbox{if $ \alpha = \beta = z$}\\ |
| 625 |
0 & \mbox{otherwise}\\ |
| 626 |
\end{array} |
| 627 |
\right. |
| 628 |
\end{equation} |
| 629 |
|
| 630 |
Note that the iterative schemes for NPAT are identical to those |
| 631 |
described for the NPTi integrator. |
| 632 |
|
| 633 |
\subsubsection{\label{methodSection:NPrT}\textbf{NP$\gamma$T Ensemble}} |
| 634 |
|
| 635 |
Theoretically, the surface tension $\gamma$ of a stress free |
| 636 |
membrane system should be zero since its surface free energy $G$ is |
| 637 |
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 |
| 647 |
\begin{equation} |
| 648 |
\dot {\overleftrightarrow{\eta}} _{\alpha \beta}=\left\{\begin{array}{ll} |
| 649 |
- A_{xy} (\gamma _\alpha - \gamma _{{\rm{target}}} ) & \mbox{$\alpha = \beta = x$ or $\alpha = \beta = y$}\\ |
| 650 |
\frac{{V(P_{\alpha \beta } - P_{{\rm{target}}} )}}{{\tau _{\rm{B}}^{\rm{2}} fk_B T_{{\rm{target}}}}} & \mbox{$\alpha = \beta = z$} \\ |
| 651 |
0 & \mbox{$\alpha \ne \beta$} \\ |
| 652 |
\end{array} |
| 653 |
\right. |
| 654 |
\end{equation} |
| 655 |
where $ \gamma _{{\rm{target}}}$ is the external surface tension and |
| 656 |
the instantaneous surface tensor $\gamma _\alpha$ is given by |
| 657 |
\begin{equation} |
| 658 |
\gamma _\alpha = - h_z( \overleftrightarrow{P} _{\alpha \alpha } |
| 659 |
- P_{{\rm{target}}} ) |
| 660 |
\label{methodEquation:instantaneousSurfaceTensor} |
| 661 |
\end{equation} |
| 662 |
|
| 663 |
There is one additional extended system integrator (NPTxyz), in |
| 664 |
which each attempt to preserve the target pressure along the box |
| 665 |
walls perpendicular to that particular axis. The lengths of the box |
| 666 |
axes are allowed to fluctuate independently, but the angle between |
| 667 |
the box axes does not change. It should be noted that the NPTxyz |
| 668 |
integrator is a special case of $NP\gamma T$ if the surface tension |
| 669 |
$\gamma$ is set to zero. |
| 670 |
|
| 671 |
\section{\label{methodSection:zcons}Z-Constraint Method} |
| 672 |
|
| 673 |
Based on the fluctuation-dissipation theorem, a force |
| 674 |
auto-correlation method was developed by Roux and Karplus to |
| 675 |
investigate the dynamics of ions inside ion channels\cite{Roux1991}. |
| 676 |
The time-dependent friction coefficient can be calculated from the |
| 677 |
deviation of the instantaneous force from its mean force. |
| 678 |
\begin{equation} |
| 679 |
\xi(z,t)=\langle\delta F(z,t)\delta F(z,0)\rangle/k_{B}T, |
| 680 |
\end{equation} |
| 681 |
where% |
| 682 |
\begin{equation} |
| 683 |
\delta F(z,t)=F(z,t)-\langle F(z,t)\rangle. |
| 684 |
\end{equation} |
| 685 |
|
| 686 |
If the time-dependent friction decays rapidly, the static friction |
| 687 |
coefficient can be approximated by |
| 688 |
\begin{equation} |
| 689 |
\xi_{\text{static}}(z)=\int_{0}^{\infty}\langle\delta F(z,t)\delta |
| 690 |
F(z,0)\rangle dt. |
| 691 |
\end{equation} |
| 692 |
Allowing diffusion constant to then be calculated through the |
| 693 |
Einstein relation:\cite{Marrink1994} |
| 694 |
\begin{equation} |
| 695 |
D(z)=\frac{k_{B}T}{\xi_{\text{static}}(z)}=\frac{(k_{B}T)^{2}}{\int_{0}^{\infty |
| 696 |
}\langle\delta F(z,t)\delta F(z,0)\rangle dt}.% |
| 697 |
\end{equation} |
| 698 |
|
| 699 |
The Z-Constraint method, which fixes the z coordinates of the |
| 700 |
molecules with respect to the center of the mass of the system, has |
| 701 |
been a method suggested to obtain the forces required for the force |
| 702 |
auto-correlation calculation.\cite{Marrink1994} However, simply |
| 703 |
resetting the coordinate will move the center of the mass of the |
| 704 |
whole system. To avoid this problem, we reset the forces of |
| 705 |
z-constrained molecules as well as subtract the total constraint |
| 706 |
forces from the rest of the system after the force calculation at |
| 707 |
each time step instead of resetting the coordinate. |
| 708 |
|
| 709 |
After the force calculation, define $G_\alpha$ as |
| 710 |
\begin{equation} |
| 711 |
G_{\alpha} = \sum_i F_{\alpha i}, \label{oopseEq:zc1} |
| 712 |
\end{equation} |
| 713 |
where $F_{\alpha i}$ is the force in the z direction of atom $i$ in |
| 714 |
z-constrained molecule $\alpha$. The forces of the z constrained |
| 715 |
molecule are then set to: |
| 716 |
\begin{equation} |
| 717 |
F_{\alpha i} = F_{\alpha i} - |
| 718 |
\frac{m_{\alpha i} G_{\alpha}}{\sum_i m_{\alpha i}}. |
| 719 |
\end{equation} |
| 720 |
Here, $m_{\alpha i}$ is the mass of atom $i$ in the z-constrained |
| 721 |
molecule. Having rescaled the forces, the velocities must also be |
| 722 |
rescaled to subtract out any center of mass velocity in the z |
| 723 |
direction. |
| 724 |
\begin{equation} |
| 725 |
v_{\alpha i} = v_{\alpha i} - |
| 726 |
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}, |
| 727 |
\end{equation} |
| 728 |
where $v_{\alpha i}$ is the velocity of atom $i$ in the z direction. |
| 729 |
Lastly, all of the accumulated z constrained forces must be |
| 730 |
subtracted from the system to keep the system center of mass from |
| 731 |
drifting. |
| 732 |
\begin{equation} |
| 733 |
F_{\beta i} = F_{\beta i} - \frac{m_{\beta i} \sum_{\alpha} |
| 734 |
G_{\alpha}} |
| 735 |
{\sum_{\beta}\sum_i m_{\beta i}}, |
| 736 |
\end{equation} |
| 737 |
where $\beta$ are all of the unconstrained molecules in the system. |
| 738 |
Similarly, the velocities of the unconstrained molecules must also |
| 739 |
be scaled. |
| 740 |
\begin{equation} |
| 741 |
v_{\beta i} = v_{\beta i} + \sum_{\alpha} |
| 742 |
\frac{\sum_i m_{\alpha i} v_{\alpha i}}{\sum_i m_{\alpha i}}. |
| 743 |
\end{equation} |
| 744 |
|
| 745 |
At the very beginning of the simulation, the molecules may not be at |
| 746 |
their constrained positions. To move a z-constrained molecule to its |
| 747 |
specified position, a simple harmonic potential is used |
| 748 |
\begin{equation} |
| 749 |
U(t)=\frac{1}{2}k_{\text{Harmonic}}(z(t)-z_{\text{cons}})^{2},% |
| 750 |
\end{equation} |
| 751 |
where $k_{\text{Harmonic}}$ is the harmonic force constant, $z(t)$ |
| 752 |
is the current $z$ coordinate of the center of mass of the |
| 753 |
constrained molecule, and $z_{\text{cons}}$ is the constrained |
| 754 |
position. The harmonic force operating on the z-constrained molecule |
| 755 |
at time $t$ can be calculated by |
| 756 |
\begin{equation} |
| 757 |
F_{z_{\text{Harmonic}}}(t)=-\frac{\partial U(t)}{\partial z(t)}= |
| 758 |
-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 |
%review rigid body dynamics |
| 814 |
Rigid bodies are frequently involved in the modeling of different |
| 815 |
areas, from engineering, physics, to chemistry. For example, |
| 816 |
missiles and vehicle are usually modeled by rigid bodies. The |
| 817 |
movement of the objects in 3D gaming engine or other physics |
| 818 |
simulator is governed by the rigid body dynamics. In molecular |
| 819 |
simulation, rigid body is used to simplify the model in |
| 820 |
protein-protein docking study\cite{Gray2003}. |
| 821 |
|
| 822 |
It is very important to develop stable and efficient methods to |
| 823 |
integrate the equations of motion of orientational degrees of |
| 824 |
freedom. Euler angles are the nature choice to describe the |
| 825 |
rotational degrees of freedom. However, due to its singularity, the |
| 826 |
numerical integration of corresponding equations of motion is very |
| 827 |
inefficient and inaccurate. Although an alternative integrator using |
| 828 |
different sets of Euler angles can overcome this |
| 829 |
difficulty\cite{Ryckaert1977, Andersen1983}, the computational |
| 830 |
penalty and the lost of angular momentum conservation still remain. |
| 831 |
In 1977, a singularity free representation utilizing quaternions was |
| 832 |
developed by Evans\cite{Evans1977}. Unfortunately, this approach |
| 833 |
suffer from the nonseparable Hamiltonian resulted from quaternion |
| 834 |
representation, which prevents the symplectic algorithm to be |
| 835 |
utilized. Another different approach is to apply holonomic |
| 836 |
constraints to the atoms belonging to the rigid |
| 837 |
body\cite{Barojas1973}. Each atom moves independently under the |
| 838 |
normal forces deriving from potential energy and constraint forces |
| 839 |
which are used to guarantee the rigidness. However, due to their |
| 840 |
iterative nature, SHAKE and Rattle algorithm converge very slowly |
| 841 |
when the number of constraint increases. |
| 842 |
|
| 843 |
The break through in geometric literature suggests that, in order to |
| 844 |
develop a long-term integration scheme, one should preserve the |
| 845 |
geometric structure of the flow. Matubayasi and Nakahara developed a |
| 846 |
time-reversible integrator for rigid bodies in quaternion |
| 847 |
representation. Although it is not symplectic, this integrator still |
| 848 |
demonstrates a better long-time energy conservation than traditional |
| 849 |
methods because of the time-reversible nature. Extending |
| 850 |
Trotter-Suzuki to general system with a flat phase space, Miller and |
| 851 |
his colleagues devised an novel symplectic, time-reversible and |
| 852 |
volume-preserving integrator in quaternion representation. However, |
| 853 |
all of the integrators in quaternion representation suffer from the |
| 854 |
computational penalty of constructing a rotation matrix from |
| 855 |
quaternions to evolve coordinates and velocities at every time step. |
| 856 |
An alternative integration scheme utilizing rotation matrix directly |
| 857 |
is RSHAKE\cite{Kol1997}, in which a conjugate momentum to rotation |
| 858 |
matrix is introduced to re-formulate the Hamiltonian's equation and |
| 859 |
the Hamiltonian is evolved in a constraint manifold by iteratively |
| 860 |
satisfying the orthogonality constraint. However, RSHAKE is |
| 861 |
inefficient because of the iterative procedure. An extremely |
| 862 |
efficient integration scheme in rotation matrix representation, |
| 863 |
which also preserves the same structural properties of the |
| 864 |
Hamiltonian flow as Miller's integrator, is proposed by Dullweber, |
| 865 |
Leimkuhler and McLachlan (DLM)\cite{Dullweber1997}. |
| 866 |
|
| 867 |
%review langevin/browninan dynamics for arbitrarily shaped rigid body |
| 868 |
Combining Langevin or Brownian dynamics with rigid body dynamics, |
| 869 |
one can study the slow processes in biomolecular systems. Modeling |
| 870 |
the DNA as a chain of rigid spheres beads, which subject to harmonic |
| 871 |
potentials as well as excluded volume potentials, Mielke and his |
| 872 |
coworkers discover rapid superhelical stress generations from the |
| 873 |
stochastic simulation of twin supercoiling DNA with response to |
| 874 |
induced torques\cite{Mielke2004}. Membrane fusion is another key |
| 875 |
biological process which controls a variety of physiological |
| 876 |
functions, such as release of neurotransmitters \textit{etc}. A |
| 877 |
typical fusion event happens on the time scale of millisecond, which |
| 878 |
is impracticable to study using all atomistic model with newtonian |
| 879 |
mechanics. With the help of coarse-grained rigid body model and |
| 880 |
stochastic dynamics, the fusion pathways were exploited by many |
| 881 |
researchers\cite{Noguchi2001,Noguchi2002,Shillcock2005}. Due to the |
| 882 |
difficulty of numerical integration of anisotropy rotation, most of |
| 883 |
the rigid body models are simply modeled by sphere, cylinder, |
| 884 |
ellipsoid or other regular shapes in stochastic simulations. In an |
| 885 |
effort to account for the diffusion anisotropy of the arbitrary |
| 886 |
particles, Fernandes and de la Torre improved the original Brownian |
| 887 |
dynamics simulation algorithm\cite{Ermak1978,Allison1991} by |
| 888 |
incorporating a generalized $6\times6$ diffusion tensor and |
| 889 |
introducing a simple rotation evolution scheme consisting of three |
| 890 |
consecutive rotations\cite{Fernandes2002}. Unfortunately, unexpected |
| 891 |
error and bias are introduced into the system due to the arbitrary |
| 892 |
order of applying the noncommuting rotation |
| 893 |
operators\cite{Beard2003}. Based on the observation the momentum |
| 894 |
relaxation time is much less than the time step, one may ignore the |
| 895 |
inertia in Brownian dynamics. However, assumption of the zero |
| 896 |
average acceleration is not always true for cooperative motion which |
| 897 |
is common in protein motion. An inertial Brownian dynamics (IBD) was |
| 898 |
proposed to address this issue by adding an inertial correction |
| 899 |
term\cite{Beard2003}. As a complement to IBD which has a lower bound |
| 900 |
in time step because of the inertial relaxation time, long-time-step |
| 901 |
inertial dynamics (LTID) can be used to investigate the inertial |
| 902 |
behavior of the polymer segments in low friction |
| 903 |
regime\cite{Beard2001}. LTID can also deal with the rotational |
| 904 |
dynamics for nonskew bodies without translation-rotation coupling by |
| 905 |
separating the translation and rotation motion and taking advantage |
| 906 |
of the analytical solution of hydrodynamics properties. However, |
| 907 |
typical nonskew bodies like cylinder and ellipsoid are inadequate to |
| 908 |
represent most complex macromolecule assemblies. These intricate |
| 909 |
molecules have been represented by a set of beads and their |
| 910 |
hydrodynamics properties can be calculated using variant |
| 911 |
hydrodynamic interaction tensors. |
| 912 |
|
| 913 |
The goal of the present work is to develop a Langevin dynamics |
| 914 |
algorithm for arbitrary rigid particles by integrating the accurate |
| 915 |
estimation of friction tensor from hydrodynamics theory into the |
| 916 |
sophisticated rigid body dynamics. |
| 917 |
|
| 918 |
|
| 919 |
\subsection{Friction Tensor} |
| 920 |
|
| 921 |
For an arbitrary rigid body moves in a fluid, it may experience |
| 922 |
friction force $f_r$ or friction torque $\tau _r$ along the opposite |
| 923 |
direction of the velocity $v$ or angular velocity $\omega$ at |
| 924 |
arbitrary origin $P$, |
| 925 |
\begin{equation} |
| 926 |
\left( \begin{array}{l} |
| 927 |
f_r \\ |
| 928 |
\tau _r \\ |
| 929 |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
| 930 |
{\Xi _{P,t} } & {\Xi _{P,c}^T } \\ |
| 931 |
{\Xi _{P,c} } & {\Xi _{P,r} } \\ |
| 932 |
\end{array}} \right)\left( \begin{array}{l} |
| 933 |
\nu \\ |
| 934 |
\omega \\ |
| 935 |
\end{array} \right) |
| 936 |
\end{equation} |
| 937 |
where $\Xi _{P,t}t$ is the translation friction tensor, $\Xi _{P,r}$ |
| 938 |
is the rotational friction tensor and $\Xi _{P,c}$ is the |
| 939 |
translation-rotation coupling tensor. The procedure of calculating |
| 940 |
friction tensor using hydrodynamic tensor and comparison between |
| 941 |
bead model and shell model were elaborated by Carrasco \textit{et |
| 942 |
al}\cite{Carrasco1999}. An important property of the friction tensor |
| 943 |
is that the translational friction tensor is independent of origin |
| 944 |
while the rotational and coupling are sensitive to the choice of the |
| 945 |
origin \cite{Brenner1967}, which can be described by |
| 946 |
\begin{equation} |
| 947 |
\begin{array}{c} |
| 948 |
\Xi _{P,t} = \Xi _{O,t} = \Xi _t \\ |
| 949 |
\Xi _{P,c} = \Xi _{O,c} - r_{OP} \times \Xi _t \\ |
| 950 |
\Xi _{P,r} = \Xi _{O,r} - r_{OP} \times \Xi _t \times r_{OP} + \Xi _{O,c} \times r_{OP} - r_{OP} \times \Xi _{O,c}^T \\ |
| 951 |
\end{array} |
| 952 |
\end{equation} |
| 953 |
Where $O$ is another origin and $r_{OP}$ is the vector joining $O$ |
| 954 |
and $P$. It is also worthy of mention that both of translational and |
| 955 |
rotational frictional tensors are always symmetric. In contrast, |
| 956 |
coupling tensor is only symmetric at center of reaction: |
| 957 |
\begin{equation} |
| 958 |
\Xi _{R,c} = \Xi _{R,c}^T |
| 959 |
\end{equation} |
| 960 |
The proper location for applying friction force is the center of |
| 961 |
reaction, at which the trace of rotational resistance tensor reaches |
| 962 |
minimum. |
| 963 |
|
| 964 |
\subsection{Rigid body dynamics} |
| 965 |
|
| 966 |
The Hamiltonian of rigid body can be separated in terms of potential |
| 967 |
energy $V(r,A)$ and kinetic energy $T(p,\pi)$, |
| 968 |
\[ |
| 969 |
H = V(r,A) + T(v,\pi ) |
| 970 |
\] |
| 971 |
A second-order symplectic method is now obtained by the composition |
| 972 |
of the flow maps, |
| 973 |
\[ |
| 974 |
\varphi _{\Delta t} = \varphi _{\Delta t/2,V} \circ \varphi |
| 975 |
_{\Delta t,T} \circ \varphi _{\Delta t/2,V}. |
| 976 |
\] |
| 977 |
Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two |
| 978 |
sub-flows which corresponding to force and torque respectively, |
| 979 |
\[ |
| 980 |
\varphi _{\Delta t/2,V} = \varphi _{\Delta t/2,F} \circ \varphi |
| 981 |
_{\Delta t/2,\tau }. |
| 982 |
\] |
| 983 |
Since the associated operators of $\varphi _{\Delta t/2,F} $ and |
| 984 |
$\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition |
| 985 |
order inside $\varphi _{\Delta t/2,V}$ does not matter. |
| 986 |
|
| 987 |
Furthermore, kinetic potential can be separated to translational |
| 988 |
kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$, |
| 989 |
\begin{equation} |
| 990 |
T(p,\pi ) =T^t (p) + T^r (\pi ). |
| 991 |
\end{equation} |
| 992 |
where $ T^t (p) = \frac{1}{2}v^T m v $ and $T^r (\pi )$ is defined |
| 993 |
by \ref{introEquation:rotationalKineticRB}. Therefore, the |
| 994 |
corresponding flow maps are given by |
| 995 |
\[ |
| 996 |
\varphi _{\Delta t,T} = \varphi _{\Delta t,T^t } \circ \varphi |
| 997 |
_{\Delta t,T^r }. |
| 998 |
\] |
| 999 |
The free rigid body is an example of Lie-Poisson system with |
| 1000 |
Hamiltonian function |
| 1001 |
\begin{equation} |
| 1002 |
T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 ) |
| 1003 |
\label{introEquation:rotationalKineticRB} |
| 1004 |
\end{equation} |
| 1005 |
where $T_i^r (\pi _i ) = \frac{{\pi _i ^2 }}{{2I_i }}$ and |
| 1006 |
Lie-Poisson structure matrix, |
| 1007 |
\begin{equation} |
| 1008 |
J(\pi ) = \left( {\begin{array}{*{20}c} |
| 1009 |
0 & {\pi _3 } & { - \pi _2 } \\ |
| 1010 |
{ - \pi _3 } & 0 & {\pi _1 } \\ |
| 1011 |
{\pi _2 } & { - \pi _1 } & 0 \\ |
| 1012 |
\end{array}} \right) |
| 1013 |
\end{equation} |
| 1014 |
Thus, the dynamics of free rigid body is governed by |
| 1015 |
\begin{equation} |
| 1016 |
\frac{d}{{dt}}\pi = J(\pi )\nabla _\pi T^r (\pi ) |
| 1017 |
\end{equation} |
| 1018 |
One may notice that each $T_i^r$ in Equation |
| 1019 |
\ref{introEquation:rotationalKineticRB} can be solved exactly. For |
| 1020 |
instance, the equations of motion due to $T_1^r$ are given by |
| 1021 |
\begin{equation} |
| 1022 |
\frac{d}{{dt}}\pi = R_1 \pi ,\frac{d}{{dt}}A = AR_1 |
| 1023 |
\label{introEqaution:RBMotionSingleTerm} |
| 1024 |
\end{equation} |
| 1025 |
where |
| 1026 |
\[ R_1 = \left( {\begin{array}{*{20}c} |
| 1027 |
0 & 0 & 0 \\ |
| 1028 |
0 & 0 & {\pi _1 } \\ |
| 1029 |
0 & { - \pi _1 } & 0 \\ |
| 1030 |
\end{array}} \right). |
| 1031 |
\] |
| 1032 |
The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is |
| 1033 |
\[ |
| 1034 |
\pi (\Delta t) = e^{\Delta tR_1 } \pi (0),A(\Delta t) = |
| 1035 |
A(0)e^{\Delta tR_1 } |
| 1036 |
\] |
| 1037 |
with |
| 1038 |
\[ |
| 1039 |
e^{\Delta tR_1 } = \left( {\begin{array}{*{20}c} |
| 1040 |
0 & 0 & 0 \\ |
| 1041 |
0 & {\cos \theta _1 } & {\sin \theta _1 } \\ |
| 1042 |
0 & { - \sin \theta _1 } & {\cos \theta _1 } \\ |
| 1043 |
\end{array}} \right),\theta _1 = \frac{{\pi _1 }}{{I_1 }}\Delta t. |
| 1044 |
\] |
| 1045 |
To reduce the cost of computing expensive functions in $e^{\Delta |
| 1046 |
tR_1 }$, we can use Cayley transformation, |
| 1047 |
\[ |
| 1048 |
e^{\Delta tR_1 } \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1 |
| 1049 |
) |
| 1050 |
\] |
| 1051 |
The flow maps for $T_2^r$ and $T_3^r$ can be found in the same |
| 1052 |
manner. |
| 1053 |
|
| 1054 |
In order to construct a second-order symplectic method, we split the |
| 1055 |
angular kinetic Hamiltonian function into five terms |
| 1056 |
\[ |
| 1057 |
T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2 |
| 1058 |
) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r |
| 1059 |
(\pi _1 ) |
| 1060 |
\]. |
| 1061 |
Concatenating flows corresponding to these five terms, we can obtain |
| 1062 |
the flow map for free rigid body, |
| 1063 |
\[ |
| 1064 |
\varphi _{\Delta t,T^r } = \varphi _{\Delta t/2,\pi _1 } \circ |
| 1065 |
\varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t,\pi _3 } |
| 1066 |
\circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t/2,\pi |
| 1067 |
_1 }. |
| 1068 |
\] |
| 1069 |
|
| 1070 |
The equations of motion corresponding to potential energy and |
| 1071 |
kinetic energy are listed in the below table, |
| 1072 |
\begin{center} |
| 1073 |
\begin{tabular}{|l|l|} |
| 1074 |
\hline |
| 1075 |
% after \\: \hline or \cline{col1-col2} \cline{col3-col4} ... |
| 1076 |
Potential & Kinetic \\ |
| 1077 |
$\frac{{dq}}{{dt}} = \frac{p}{m}$ & $\frac{d}{{dt}}q = p$ \\ |
| 1078 |
$\frac{d}{{dt}}p = - \frac{{\partial V}}{{\partial q}}$ & $ \frac{d}{{dt}}p = 0$ \\ |
| 1079 |
$\frac{d}{{dt}}Q = 0$ & $ \frac{d}{{dt}}Q = Qskew(I^{ - 1} j)$ \\ |
| 1080 |
$ \frac{d}{{dt}}\pi = \sum\limits_i {\left( {Q^T F_i (r,Q)} \right) \times X_i }$ & $\frac{d}{{dt}}\pi = \pi \times I^{ - 1} \pi$\\ |
| 1081 |
\hline |
| 1082 |
\end{tabular} |
| 1083 |
\end{center} |
| 1084 |
|
| 1085 |
Finally, we obtain the overall symplectic flow maps for free moving |
| 1086 |
rigid body |
| 1087 |
\begin{align*} |
| 1088 |
\varphi _{\Delta t} = &\varphi _{\Delta t/2,F} \circ \varphi _{\Delta t/2,\tau } \circ \\ |
| 1089 |
&\varphi _{\Delta t,T^t } \circ \varphi _{\Delta t/2,\pi _1 } \circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t,\pi _3 } \circ \varphi _{\Delta t/2,\pi _2 } \circ \varphi _{\Delta t/2,\pi _1 } \circ \\ |
| 1090 |
&\varphi _{\Delta t/2,\tau } \circ \varphi _{\Delta t/2,F} .\\ |
| 1091 |
\label{introEquation:overallRBFlowMaps} |
| 1092 |
\end{align*} |
| 1093 |
|
| 1094 |
\subsection{Langevin dynamics for rigid particles of arbitrary shape} |
| 1095 |
|
| 1096 |
Consider a Langevin equation of motions in generalized coordinates |
| 1097 |
\begin{equation} |
| 1098 |
M_i \dot V_i (t) = F_{s,i} (t) + F_{f,i(t)} + F_{r,i} (t) |
| 1099 |
\label{LDGeneralizedForm} |
| 1100 |
\end{equation} |
| 1101 |
where $M_i$ is a $6\times6$ generalized diagonal mass (include mass |
| 1102 |
and moment of inertial) matrix and $V_i$ is a generalized velocity, |
| 1103 |
$V_i = V_i(v_i,\omega _i)$. The right side of Eq. |
| 1104 |
(\ref{LDGeneralizedForm}) consists of three generalized forces in |
| 1105 |
lab-fixed frame, systematic force $F_{s,i}$, dissipative force |
| 1106 |
$F_{f,i}$ and stochastic force $F_{r,i}$. While the evolution of the |
| 1107 |
system in Newtownian mechanics typically refers to lab-fixed frame, |
| 1108 |
it is also convenient to handle the rotation of rigid body in |
| 1109 |
body-fixed frame. Thus the friction and random forces are calculated |
| 1110 |
in body-fixed frame and converted back to lab-fixed frame by: |
| 1111 |
\[ |
| 1112 |
\begin{array}{l} |
| 1113 |
F_{f,i}^l (t) = A^T F_{f,i}^b (t), \\ |
| 1114 |
F_{r,i}^l (t) = A^T F_{r,i}^b (t) \\ |
| 1115 |
\end{array}. |
| 1116 |
\] |
| 1117 |
Here, the body-fixed friction force $F_{r,i}^b$ is proportional to |
| 1118 |
the body-fixed velocity at center of resistance $v_{R,i}^b$ and |
| 1119 |
angular velocity $\omega _i$, |
| 1120 |
\begin{equation} |
| 1121 |
F_{r,i}^b (t) = \left( \begin{array}{l} |
| 1122 |
f_{r,i}^b (t) \\ |
| 1123 |
\tau _{r,i}^b (t) \\ |
| 1124 |
\end{array} \right) = - \left( {\begin{array}{*{20}c} |
| 1125 |
{\Xi _{R,t} } & {\Xi _{R,c}^T } \\ |
| 1126 |
{\Xi _{R,c} } & {\Xi _{R,r} } \\ |
| 1127 |
\end{array}} \right)\left( \begin{array}{l} |
| 1128 |
v_{R,i}^b (t) \\ |
| 1129 |
\omega _i (t) \\ |
| 1130 |
\end{array} \right), |
| 1131 |
\end{equation} |
| 1132 |
while the random force $F_{r,i}^l$ is a Gaussian stochastic variable |
| 1133 |
with zero mean and variance |
| 1134 |
\begin{equation} |
| 1135 |
\left\langle {F_{r,i}^l (t)(F_{r,i}^l (t'))^T } \right\rangle = |
| 1136 |
\left\langle {F_{r,i}^b (t)(F_{r,i}^b (t'))^T } \right\rangle = |
| 1137 |
2k_B T\Xi _R \delta (t - t'). |
| 1138 |
\end{equation} |
| 1139 |
The equation of motion for $v_i$ can be written as |
| 1140 |
\begin{equation} |
| 1141 |
m\dot v_i (t) = f_{t,i} (t) = f_{s,i} (t) + f_{f,i}^l (t) + |
| 1142 |
f_{r,i}^l (t) |
| 1143 |
\end{equation} |
| 1144 |
Since the frictional force is applied at the center of resistance |
| 1145 |
which generally does not coincide with the center of mass, an extra |
| 1146 |
torque is exerted at the center of mass. Thus, the net body-fixed |
| 1147 |
frictional torque at the center of mass, $\tau _{n,i}^b (t)$, is |
| 1148 |
given by |
| 1149 |
\begin{equation} |
| 1150 |
\tau _{r,i}^b = \tau _{r,i}^b +r_{MR} \times f_{r,i}^b |
| 1151 |
\end{equation} |
| 1152 |
where $r_{MR}$ is the vector from the center of mass to the center |
| 1153 |
of the resistance. Instead of integrating angular velocity in |
| 1154 |
lab-fixed frame, we consider the equation of motion of angular |
| 1155 |
momentum in body-fixed frame |
| 1156 |
\begin{equation} |
| 1157 |
\dot \pi _i (t) = \tau _{t,i} (t) = \tau _{s,i} (t) + \tau _{f,i}^b |
| 1158 |
(t) + \tau _{r,i}^b(t) |
| 1159 |
\end{equation} |
| 1160 |
|
| 1161 |
Embedding the friction terms into force and torque, one can |
| 1162 |
integrate the langevin equations of motion for rigid body of |
| 1163 |
arbitrary shape in a velocity-Verlet style 2-part algorithm, where |
| 1164 |
$h= \delta t$: |
| 1165 |
|
| 1166 |
{\tt part one:} |
| 1167 |
\begin{align*} |
| 1168 |
v_i (t + h/2) &\leftarrow v_i (t) + \frac{{hf_{t,i}^l (t)}}{{2m_i }} \\ |
| 1169 |
\pi _i (t + h/2) &\leftarrow \pi _i (t) + \frac{{h\tau _{t,i}^b (t)}}{2} \\ |
| 1170 |
r_i (t + h) &\leftarrow r_i (t) + hv_i (t + h/2) \\ |
| 1171 |
A_i (t + h) &\leftarrow rotate\left( {h\pi _i (t + h/2)I^{ - 1} } \right) \\ |
| 1172 |
\end{align*} |
| 1173 |
In this context, the $\mathrm{rotate}$ function is the reversible |
| 1174 |
product of five consecutive body-fixed rotations, |
| 1175 |
\begin{equation} |
| 1176 |
\mathrm{rotate}({\bf a}) = \mathsf{G}_x(a_x / 2) \cdot |
| 1177 |
\mathsf{G}_y(a_y / 2) \cdot \mathsf{G}_z(a_z) \cdot \mathsf{G}_y(a_y |
| 1178 |
/ 2) \cdot \mathsf{G}_x(a_x /2), |
| 1179 |
\end{equation} |
| 1180 |
where each rotational propagator, $\mathsf{G}_\alpha(\theta)$, |
| 1181 |
rotates both the rotation matrix ($\mathsf{A}$) and the body-fixed |
| 1182 |
angular momentum ($\pi$) by an angle $\theta$ around body-fixed axis |
| 1183 |
$\alpha$, |
| 1184 |
\begin{equation} |
| 1185 |
\mathsf{G}_\alpha( \theta ) = \left\{ |
| 1186 |
\begin{array}{lcl} |
| 1187 |
\mathsf{A}(t) & \leftarrow & \mathsf{A}(0) \cdot \mathsf{R}_\alpha(\theta)^T, \\ |
| 1188 |
{\bf j}(t) & \leftarrow & \mathsf{R}_\alpha(\theta) \cdot {\bf |
| 1189 |
j}(0). |
| 1190 |
\end{array} |
| 1191 |
\right. |
| 1192 |
\end{equation} |
| 1193 |
$\mathsf{R}_\alpha$ is a quadratic approximation to the single-axis |
| 1194 |
rotation matrix. For example, in the small-angle limit, the |
| 1195 |
rotation matrix around the body-fixed x-axis can be approximated as |
| 1196 |
\begin{equation} |
| 1197 |
\mathsf{R}_x(\theta) \approx \left( |
| 1198 |
\begin{array}{ccc} |
| 1199 |
1 & 0 & 0 \\ |
| 1200 |
0 & \frac{1-\theta^2 / 4}{1 + \theta^2 / 4} & -\frac{\theta}{1+ |
| 1201 |
\theta^2 / 4} \\ |
| 1202 |
0 & \frac{\theta}{1+ \theta^2 / 4} & \frac{1-\theta^2 / 4}{1 + |
| 1203 |
\theta^2 / 4} |
| 1204 |
\end{array} |
| 1205 |
\right). |
| 1206 |
\end{equation} |
| 1207 |
All other rotations follow in a straightforward manner. |
| 1208 |
|
| 1209 |
After the first part of the propagation, the friction and random |
| 1210 |
forces are generated at the center of resistance in body-fixed frame |
| 1211 |
and converted back into lab-fixed frame |
| 1212 |
\[ |
| 1213 |
f_{t,i}^l (t + h) = - \left( {\frac{{\partial V}}{{\partial r_i }}} |
| 1214 |
\right)_{r_i (t + h)} + A_i^T (t + h)[F_{f,i}^b (t + h) + F_{r,i}^b |
| 1215 |
(t + h)], |
| 1216 |
\] |
| 1217 |
while the system torque in lab-fixed frame is transformed into |
| 1218 |
body-fixed frame, |
| 1219 |
\[ |
| 1220 |
\tau _{t,i}^b (t + h) = A\tau _{s,i}^l (t) + \tau _{n,i}^b (t) + |
| 1221 |
\tau _{r,i}^b (t). |
| 1222 |
\] |
| 1223 |
Once the forces and torques have been obtained at the new time step, |
| 1224 |
the velocities can be advanced to the same time value. |
| 1225 |
|
| 1226 |
{\tt part two:} |
| 1227 |
\begin{align*} |
| 1228 |
v_i (t) &\leftarrow v_i (t + h/2) + \frac{{hf_{t,i}^l (t + h)}}{{2m_i }} \\ |
| 1229 |
\pi _i (t) &\leftarrow \pi _i (t + h/2) + \frac{{h\tau _{t,i}^b (t + h)}}{2} \\ |
| 1230 |
\end{align*} |
| 1231 |
|
| 1232 |
\subsection{Results and discussion} |
