93 |
|
The actual trajectory, along which a dynamical system may move from |
94 |
|
one point to another within a specified time, is derived by finding |
95 |
|
the path which minimizes the time integral of the difference between |
96 |
< |
the kinetic, $K$, and potential energies, $U$ \cite{tolman79}. |
96 |
> |
the kinetic, $K$, and potential energies, $U$ \cite{Tolman1979}. |
97 |
|
\begin{equation} |
98 |
|
\delta \int_{t_1 }^{t_2 } {(K - U)dt = 0} , |
99 |
|
\label{introEquation:halmitonianPrinciple1} |
189 |
|
Eq.~\ref{introEquation:motionHamiltonianCoordinate} and |
190 |
|
Eq.~\ref{introEquation:motionHamiltonianMomentum} are Hamilton's |
191 |
|
equation of motion. Due to their symmetrical formula, they are also |
192 |
< |
known as the canonical equations of motions \cite{Goldstein01}. |
192 |
> |
known as the canonical equations of motions \cite{Goldstein2001}. |
193 |
|
|
194 |
|
An important difference between Lagrangian approach and the |
195 |
|
Hamiltonian approach is that the Lagrangian is considered to be a |
200 |
|
appropriate for application to statistical mechanics and quantum |
201 |
|
mechanics, since it treats the coordinate and its time derivative as |
202 |
|
independent variables and it only works with 1st-order differential |
203 |
< |
equations\cite{Marion90}. |
203 |
> |
equations\cite{Marion1990}. |
204 |
|
|
205 |
|
In Newtonian Mechanics, a system described by conservative forces |
206 |
|
conserves the total energy \ref{introEquation:energyConservation}. |
470 |
|
many-body system in Statistical Mechanics. Fortunately, Ergodic |
471 |
|
Hypothesis is proposed to make a connection between time average and |
472 |
|
ensemble average. It states that time average and average over the |
473 |
< |
statistical ensemble are identical \cite{Frenkel1996, leach01:mm}. |
473 |
> |
statistical ensemble are identical \cite{Frenkel1996, Leach2001}. |
474 |
|
\begin{equation} |
475 |
|
\langle A(q , p) \rangle_t = \mathop {\lim }\limits_{t \to \infty } |
476 |
|
\frac{1}{t}\int\limits_0^t {A(q(t),p(t))dt = \int\limits_\Gamma |
484 |
|
a properly weighted statistical average. This allows the researcher |
485 |
|
freedom of choice when deciding how best to measure a given |
486 |
|
observable. In case an ensemble averaged approach sounds most |
487 |
< |
reasonable, the Monte Carlo techniques\cite{metropolis:1949} can be |
487 |
> |
reasonable, the Monte Carlo techniques\cite{Metropolis1949} can be |
488 |
|
utilized. Or if the system lends itself to a time averaging |
489 |
|
approach, the Molecular Dynamics techniques in |
490 |
|
Sec.~\ref{introSection:molecularDynamics} will be the best |
498 |
|
within the equations. Since 1990, geometric integrators, which |
499 |
|
preserve various phase-flow invariants such as symplectic structure, |
500 |
|
volume and time reversal symmetry, are developed to address this |
501 |
< |
issue. The velocity verlet method, which happens to be a simple |
502 |
< |
example of symplectic integrator, continues to gain its popularity |
503 |
< |
in molecular dynamics community. This fact can be partly explained |
504 |
< |
by its geometric nature. |
501 |
> |
issue\cite{Dullweber1997, McLachlan1998, Leimkuhler1999}. The |
502 |
> |
velocity verlet method, which happens to be a simple example of |
503 |
> |
symplectic integrator, continues to gain its popularity in molecular |
504 |
> |
dynamics community. This fact can be partly explained by its |
505 |
> |
geometric nature. |
506 |
|
|
507 |
|
\subsection{\label{introSection:symplecticManifold}Symplectic Manifold} |
508 |
|
A \emph{manifold} is an abstract mathematical space. It locally |
566 |
|
\end{equation}In this case, $f$ is |
567 |
|
called a \emph{Hamiltonian vector field}. |
568 |
|
|
569 |
< |
Another generalization of Hamiltonian dynamics is Poisson Dynamics, |
569 |
> |
Another generalization of Hamiltonian dynamics is Poisson |
570 |
> |
Dynamics\cite{Olver1986}, |
571 |
|
\begin{equation} |
572 |
|
\dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian} |
573 |
|
\end{equation} |
614 |
|
|
615 |
|
\subsection{\label{introSection:geometricProperties}Geometric Properties} |
616 |
|
|
617 |
< |
The hidden geometric properties of ODE and its flow play important |
618 |
< |
roles in numerical studies. Many of them can be found in systems |
619 |
< |
which occur naturally in applications. |
617 |
> |
The hidden geometric properties\cite{Budd1999, Marsden1998} of ODE |
618 |
> |
and its flow play important roles in numerical studies. Many of them |
619 |
> |
can be found in systems which occur naturally in applications. |
620 |
|
|
621 |
|
Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is |
622 |
|
a \emph{symplectic} flow if it satisfies, |
660 |
|
which is the condition for conserving \emph{first integral}. For a |
661 |
|
canonical Hamiltonian system, the time evolution of an arbitrary |
662 |
|
smooth function $G$ is given by, |
663 |
< |
\begin{equation} |
664 |
< |
\begin{array}{c} |
665 |
< |
\frac{{dG(x(t))}}{{dt}} = [\nabla _x G(x(t))]^T \dot x(t) \\ |
666 |
< |
= [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\ |
665 |
< |
\end{array} |
663 |
> |
|
664 |
> |
\begin{eqnarray} |
665 |
> |
\frac{{dG(x(t))}}{{dt}} & = & [\nabla _x G(x(t))]^T \dot x(t) \\ |
666 |
> |
& = & [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\ |
667 |
|
\label{introEquation:firstIntegral1} |
668 |
< |
\end{equation} |
668 |
> |
\end{eqnarray} |
669 |
> |
|
670 |
> |
|
671 |
|
Using poisson bracket notion, Equation |
672 |
|
\ref{introEquation:firstIntegral1} can be rewritten as |
673 |
|
\[ |
682 |
|
is a \emph{first integral}, which is due to the fact $\{ H,H\} = |
683 |
|
0$. |
684 |
|
|
685 |
< |
|
683 |
< |
When designing any numerical methods, one should always try to |
685 |
> |
When designing any numerical methods, one should always try to |
686 |
|
preserve the structural properties of the original ODE and its flow. |
687 |
|
|
688 |
|
\subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods} |
699 |
|
\item Splitting methods |
700 |
|
\end{enumerate} |
701 |
|
|
702 |
< |
Generating function tends to lead to methods which are cumbersome |
703 |
< |
and difficult to use. In dissipative systems, variational methods |
704 |
< |
can capture the decay of energy accurately. Since their |
705 |
< |
geometrically unstable nature against non-Hamiltonian perturbations, |
706 |
< |
ordinary implicit Runge-Kutta methods are not suitable for |
707 |
< |
Hamiltonian system. Recently, various high-order explicit |
708 |
< |
Runge--Kutta methods have been developed to overcome this |
702 |
> |
Generating function\cite{Channell1990} tends to lead to methods |
703 |
> |
which are cumbersome and difficult to use. In dissipative systems, |
704 |
> |
variational methods can capture the decay of energy |
705 |
> |
accurately\cite{Kane2000}. Since their geometrically unstable nature |
706 |
> |
against non-Hamiltonian perturbations, ordinary implicit Runge-Kutta |
707 |
> |
methods are not suitable for Hamiltonian system. Recently, various |
708 |
> |
high-order explicit Runge-Kutta methods |
709 |
> |
\cite{Owren1992,Chen2003}have been developed to overcome this |
710 |
|
instability. However, due to computational penalty involved in |
711 |
|
implementing the Runge-Kutta methods, they do not attract too much |
712 |
|
attention from Molecular Dynamics community. Instead, splitting have |
713 |
|
been widely accepted since they exploit natural decompositions of |
714 |
< |
the system\cite{Tuckerman92}. |
714 |
> |
the system\cite{Tuckerman1992, McLachlan1998}. |
715 |
|
|
716 |
|
\subsubsection{\label{introSection:splittingMethod}Splitting Method} |
717 |
|
|
825 |
|
% |
826 |
|
q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot |
827 |
|
q(\Delta t)} \right]. % |
828 |
< |
\label{introEquation:positionVerlet1} |
828 |
> |
\label{introEquation:positionVerlet2} |
829 |
|
\end{align} |
830 |
|
|
831 |
|
\subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods} |
834 |
|
error of splitting method in terms of commutator of the |
835 |
|
operators(\ref{introEquation:exponentialOperator}) associated with |
836 |
|
the sub-flow. For operators $hX$ and $hY$ which are associate to |
837 |
< |
$\varphi_1(t)$ and $\varphi_2(t$ respectively , we have |
837 |
> |
$\varphi_1(t)$ and $\varphi_2(t)$ respectively , we have |
838 |
|
\begin{equation} |
839 |
|
\exp (hX + hY) = \exp (hZ) |
840 |
|
\end{equation} |
847 |
|
\[ |
848 |
|
[X,Y] = XY - YX . |
849 |
|
\] |
850 |
< |
Applying Baker-Campbell-Hausdorff formula to Sprang splitting, we |
851 |
< |
can obtain |
850 |
> |
Applying Baker-Campbell-Hausdorff formula\cite{Varadarajan1974} to |
851 |
> |
Sprang splitting, we can obtain |
852 |
|
\begin{eqnarray*} |
853 |
< |
\exp (h X/2)\exp (h Y)\exp (h X/2) & = & \exp (h X + h Y + h^2 |
854 |
< |
[X,Y]/4 + h^2 [Y,X]/4 \\ & & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\ |
855 |
< |
& & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3 [X,[X,Y]]/24 & & \mbox{} + |
853 |
< |
\ldots ) |
853 |
> |
\exp (h X/2)\exp (h Y)\exp (h X/2) & = & \exp (h X + h Y + h^2 [X,Y]/4 + h^2 [Y,X]/4 \\ |
854 |
> |
& & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\ |
855 |
> |
& & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3[X,[X,Y]]/24 + \ldots ) |
856 |
|
\end{eqnarray*} |
857 |
|
Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0\], the dominant local |
858 |
|
error of Spring splitting is proportional to $h^3$. The same |
861 |
|
\varphi _{b_m h}^2 \circ \varphi _{a_m h}^1 \circ \varphi _{b_{m - |
862 |
|
1} h}^2 \circ \ldots \circ \varphi _{a_1 h}^1 . |
863 |
|
\end{equation} |
864 |
< |
Careful choice of coefficient $a_1 ,\ldot , b_m$ will lead to higher |
864 |
> |
Careful choice of coefficient $a_1 \ldots b_m$ will lead to higher |
865 |
|
order method. Yoshida proposed an elegant way to compose higher |
866 |
< |
order methods based on symmetric splitting. Given a symmetric second |
867 |
< |
order base method $ \varphi _h^{(2)} $, a fourth-order symmetric |
868 |
< |
method can be constructed by composing, |
866 |
> |
order methods based on symmetric splitting\cite{Yoshida1990}. Given |
867 |
> |
a symmetric second order base method $ \varphi _h^{(2)} $, a |
868 |
> |
fourth-order symmetric method can be constructed by composing, |
869 |
|
\[ |
870 |
|
\varphi _h^{(4)} = \varphi _{\alpha h}^{(2)} \circ \varphi _{\beta |
871 |
|
h}^{(2)} \circ \varphi _{\alpha h}^{(2)} |
885 |
|
|
886 |
|
\section{\label{introSection:molecularDynamics}Molecular Dynamics} |
887 |
|
|
888 |
< |
As a special discipline of molecular modeling, Molecular dynamics |
889 |
< |
has proven to be a powerful tool for studying the functions of |
890 |
< |
biological systems, providing structural, thermodynamic and |
891 |
< |
dynamical information. |
888 |
> |
As one of the principal tools of molecular modeling, Molecular |
889 |
> |
dynamics has proven to be a powerful tool for studying the functions |
890 |
> |
of biological systems, providing structural, thermodynamic and |
891 |
> |
dynamical information. The basic idea of molecular dynamics is that |
892 |
> |
macroscopic properties are related to microscopic behavior and |
893 |
> |
microscopic behavior can be calculated from the trajectories in |
894 |
> |
simulations. For instance, instantaneous temperature of an |
895 |
> |
Hamiltonian system of $N$ particle can be measured by |
896 |
> |
\[ |
897 |
> |
T = \sum\limits_{i = 1}^N {\frac{{m_i v_i^2 }}{{fk_B }}} |
898 |
> |
\] |
899 |
> |
where $m_i$ and $v_i$ are the mass and velocity of $i$th particle |
900 |
> |
respectively, $f$ is the number of degrees of freedom, and $k_B$ is |
901 |
> |
the boltzman constant. |
902 |
|
|
903 |
< |
\subsection{\label{introSec:mdInit}Initialization} |
904 |
< |
|
905 |
< |
\subsection{\label{introSec:forceEvaluation}Force Evaluation} |
903 |
> |
A typical molecular dynamics run consists of three essential steps: |
904 |
> |
\begin{enumerate} |
905 |
> |
\item Initialization |
906 |
> |
\begin{enumerate} |
907 |
> |
\item Preliminary preparation |
908 |
> |
\item Minimization |
909 |
> |
\item Heating |
910 |
> |
\item Equilibration |
911 |
> |
\end{enumerate} |
912 |
> |
\item Production |
913 |
> |
\item Analysis |
914 |
> |
\end{enumerate} |
915 |
> |
These three individual steps will be covered in the following |
916 |
> |
sections. Sec.~\ref{introSec:initialSystemSettings} deals with the |
917 |
> |
initialization of a simulation. Sec.~\ref{introSec:production} will |
918 |
> |
discusses issues in production run. Sec.~\ref{introSection:Analysis} |
919 |
> |
provides the theoretical tools for trajectory analysis. |
920 |
|
|
921 |
< |
\subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion} |
921 |
> |
\subsection{\label{introSec:initialSystemSettings}Initialization} |
922 |
> |
|
923 |
> |
\subsubsection{Preliminary preparation} |
924 |
> |
|
925 |
> |
When selecting the starting structure of a molecule for molecular |
926 |
> |
simulation, one may retrieve its Cartesian coordinates from public |
927 |
> |
databases, such as RCSB Protein Data Bank \textit{etc}. Although |
928 |
> |
thousands of crystal structures of molecules are discovered every |
929 |
> |
year, many more remain unknown due to the difficulties of |
930 |
> |
purification and crystallization. Even for the molecule with known |
931 |
> |
structure, some important information is missing. For example, the |
932 |
> |
missing hydrogen atom which acts as donor in hydrogen bonding must |
933 |
> |
be added. Moreover, in order to include electrostatic interaction, |
934 |
> |
one may need to specify the partial charges for individual atoms. |
935 |
> |
Under some circumstances, we may even need to prepare the system in |
936 |
> |
a special setup. For instance, when studying transport phenomenon in |
937 |
> |
membrane system, we may prepare the lipids in bilayer structure |
938 |
> |
instead of placing lipids randomly in solvent, since we are not |
939 |
> |
interested in self-aggregation and it takes a long time to happen. |
940 |
> |
|
941 |
> |
\subsubsection{Minimization} |
942 |
> |
|
943 |
> |
It is quite possible that some of molecules in the system from |
944 |
> |
preliminary preparation may be overlapped with each other. This |
945 |
> |
close proximity leads to high potential energy which consequently |
946 |
> |
jeopardizes any molecular dynamics simulations. To remove these |
947 |
> |
steric overlaps, one typically performs energy minimization to find |
948 |
> |
a more reasonable conformation. Several energy minimization methods |
949 |
> |
have been developed to exploit the energy surface and to locate the |
950 |
> |
local minimum. While converging slowly near the minimum, steepest |
951 |
> |
descent method is extremely robust when systems are far from |
952 |
> |
harmonic. Thus, it is often used to refine structure from |
953 |
> |
crystallographic data. Relied on the gradient or hessian, advanced |
954 |
> |
methods like conjugate gradient and Newton-Raphson converge rapidly |
955 |
> |
to a local minimum, while become unstable if the energy surface is |
956 |
> |
far from quadratic. Another factor must be taken into account, when |
957 |
> |
choosing energy minimization method, is the size of the system. |
958 |
> |
Steepest descent and conjugate gradient can deal with models of any |
959 |
> |
size. Because of the limit of computation power to calculate hessian |
960 |
> |
matrix and insufficient storage capacity to store them, most |
961 |
> |
Newton-Raphson methods can not be used with very large models. |
962 |
> |
|
963 |
> |
\subsubsection{Heating} |
964 |
> |
|
965 |
> |
Typically, Heating is performed by assigning random velocities |
966 |
> |
according to a Gaussian distribution for a temperature. Beginning at |
967 |
> |
a lower temperature and gradually increasing the temperature by |
968 |
> |
assigning greater random velocities, we end up with setting the |
969 |
> |
temperature of the system to a final temperature at which the |
970 |
> |
simulation will be conducted. In heating phase, we should also keep |
971 |
> |
the system from drifting or rotating as a whole. Equivalently, the |
972 |
> |
net linear momentum and angular momentum of the system should be |
973 |
> |
shifted to zero. |
974 |
> |
|
975 |
> |
\subsubsection{Equilibration} |
976 |
> |
|
977 |
> |
The purpose of equilibration is to allow the system to evolve |
978 |
> |
spontaneously for a period of time and reach equilibrium. The |
979 |
> |
procedure is continued until various statistical properties, such as |
980 |
> |
temperature, pressure, energy, volume and other structural |
981 |
> |
properties \textit{etc}, become independent of time. Strictly |
982 |
> |
speaking, minimization and heating are not necessary, provided the |
983 |
> |
equilibration process is long enough. However, these steps can serve |
984 |
> |
as a means to arrive at an equilibrated structure in an effective |
985 |
> |
way. |
986 |
> |
|
987 |
> |
\subsection{\label{introSection:production}Production} |
988 |
> |
|
989 |
> |
Production run is the most important step of the simulation, in |
990 |
> |
which the equilibrated structure is used as a starting point and the |
991 |
> |
motions of the molecules are collected for later analysis. In order |
992 |
> |
to capture the macroscopic properties of the system, the molecular |
993 |
> |
dynamics simulation must be performed in correct and efficient way. |
994 |
> |
|
995 |
> |
The most expensive part of a molecular dynamics simulation is the |
996 |
> |
calculation of non-bonded forces, such as van der Waals force and |
997 |
> |
Coulombic forces \textit{etc}. For a system of $N$ particles, the |
998 |
> |
complexity of the algorithm for pair-wise interactions is $O(N^2 )$, |
999 |
> |
which making large simulations prohibitive in the absence of any |
1000 |
> |
computation saving techniques. |
1001 |
> |
|
1002 |
> |
A natural approach to avoid system size issue is to represent the |
1003 |
> |
bulk behavior by a finite number of the particles. However, this |
1004 |
> |
approach will suffer from the surface effect. To offset this, |
1005 |
> |
\textit{Periodic boundary condition} (see Fig.~\ref{introFig:pbc}) |
1006 |
> |
is developed to simulate bulk properties with a relatively small |
1007 |
> |
number of particles. In this method, the simulation box is |
1008 |
> |
replicated throughout space to form an infinite lattice. During the |
1009 |
> |
simulation, when a particle moves in the primary cell, its image in |
1010 |
> |
other cells move in exactly the same direction with exactly the same |
1011 |
> |
orientation. Thus, as a particle leaves the primary cell, one of its |
1012 |
> |
images will enter through the opposite face. |
1013 |
> |
\begin{figure} |
1014 |
> |
\centering |
1015 |
> |
\includegraphics[width=\linewidth]{pbc.eps} |
1016 |
> |
\caption[An illustration of periodic boundary conditions]{A 2-D |
1017 |
> |
illustration of periodic boundary conditions. As one particle leaves |
1018 |
> |
the left of the simulation box, an image of it enters the right.} |
1019 |
> |
\label{introFig:pbc} |
1020 |
> |
\end{figure} |
1021 |
> |
|
1022 |
> |
%cutoff and minimum image convention |
1023 |
> |
Another important technique to improve the efficiency of force |
1024 |
> |
evaluation is to apply cutoff where particles farther than a |
1025 |
> |
predetermined distance, are not included in the calculation |
1026 |
> |
\cite{Frenkel1996}. The use of a cutoff radius will cause a |
1027 |
> |
discontinuity in the potential energy curve. Fortunately, one can |
1028 |
> |
shift the potential to ensure the potential curve go smoothly to |
1029 |
> |
zero at the cutoff radius. Cutoff strategy works pretty well for |
1030 |
> |
Lennard-Jones interaction because of its short range nature. |
1031 |
> |
However, simply truncating the electrostatic interaction with the |
1032 |
> |
use of cutoff has been shown to lead to severe artifacts in |
1033 |
> |
simulations. Ewald summation, in which the slowly conditionally |
1034 |
> |
convergent Coulomb potential is transformed into direct and |
1035 |
> |
reciprocal sums with rapid and absolute convergence, has proved to |
1036 |
> |
minimize the periodicity artifacts in liquid simulations. Taking the |
1037 |
> |
advantages of the fast Fourier transform (FFT) for calculating |
1038 |
> |
discrete Fourier transforms, the particle mesh-based |
1039 |
> |
methods\cite{Hockney1981,Shimada1993, Luty1994} are accelerated from |
1040 |
> |
$O(N^{3/2})$ to $O(N logN)$. An alternative approach is \emph{fast |
1041 |
> |
multipole method}\cite{Greengard1987, Greengard1994}, which treats |
1042 |
> |
Coulombic interaction exactly at short range, and approximate the |
1043 |
> |
potential at long range through multipolar expansion. In spite of |
1044 |
> |
their wide acceptances at the molecular simulation community, these |
1045 |
> |
two methods are hard to be implemented correctly and efficiently. |
1046 |
> |
Instead, we use a damped and charge-neutralized Coulomb potential |
1047 |
> |
method developed by Wolf and his coworkers\cite{Wolf1999}. The |
1048 |
> |
shifted Coulomb potential for particle $i$ and particle $j$ at |
1049 |
> |
distance $r_{rj}$ is given by: |
1050 |
> |
\begin{equation} |
1051 |
> |
V(r_{ij})= \frac{q_i q_j \textrm{erfc}(\alpha |
1052 |
> |
r_{ij})}{r_{ij}}-\lim_{r_{ij}\rightarrow |
1053 |
> |
R_\textrm{c}}\left\{\frac{q_iq_j \textrm{erfc}(\alpha |
1054 |
> |
r_{ij})}{r_{ij}}\right\}. \label{introEquation:shiftedCoulomb} |
1055 |
> |
\end{equation} |
1056 |
> |
where $\alpha$ is the convergence parameter. Due to the lack of |
1057 |
> |
inherent periodicity and rapid convergence,this method is extremely |
1058 |
> |
efficient and easy to implement. |
1059 |
> |
\begin{figure} |
1060 |
> |
\centering |
1061 |
> |
\includegraphics[width=\linewidth]{shifted_coulomb.eps} |
1062 |
> |
\caption[An illustration of shifted Coulomb potential]{An |
1063 |
> |
illustration of shifted Coulomb potential.} |
1064 |
> |
\label{introFigure:shiftedCoulomb} |
1065 |
> |
\end{figure} |
1066 |
|
|
1067 |
+ |
%multiple time step |
1068 |
+ |
|
1069 |
+ |
\subsection{\label{introSection:Analysis} Analysis} |
1070 |
+ |
|
1071 |
+ |
Recently, advanced visualization technique are widely applied to |
1072 |
+ |
monitor the motions of molecules. Although the dynamics of the |
1073 |
+ |
system can be described qualitatively from animation, quantitative |
1074 |
+ |
trajectory analysis are more appreciable. According to the |
1075 |
+ |
principles of Statistical Mechanics, |
1076 |
+ |
Sec.~\ref{introSection:statisticalMechanics}, one can compute |
1077 |
+ |
thermodynamics properties, analyze fluctuations of structural |
1078 |
+ |
parameters, and investigate time-dependent processes of the molecule |
1079 |
+ |
from the trajectories. |
1080 |
+ |
|
1081 |
+ |
\subsubsection{\label{introSection:thermodynamicsProperties}Thermodynamics Properties} |
1082 |
+ |
|
1083 |
+ |
Thermodynamics properties, which can be expressed in terms of some |
1084 |
+ |
function of the coordinates and momenta of all particles in the |
1085 |
+ |
system, can be directly computed from molecular dynamics. The usual |
1086 |
+ |
way to measure the pressure is based on virial theorem of Clausius |
1087 |
+ |
which states that the virial is equal to $-3Nk_BT$. For a system |
1088 |
+ |
with forces between particles, the total virial, $W$, contains the |
1089 |
+ |
contribution from external pressure and interaction between the |
1090 |
+ |
particles: |
1091 |
+ |
\[ |
1092 |
+ |
W = - 3PV + \left\langle {\sum\limits_{i < j} {r{}_{ij} \cdot |
1093 |
+ |
f_{ij} } } \right\rangle |
1094 |
+ |
\] |
1095 |
+ |
where $f_{ij}$ is the force between particle $i$ and $j$ at a |
1096 |
+ |
distance $r_{ij}$. Thus, the expression for the pressure is given |
1097 |
+ |
by: |
1098 |
+ |
\begin{equation} |
1099 |
+ |
P = \frac{{Nk_B T}}{V} - \frac{1}{{3V}}\left\langle {\sum\limits_{i |
1100 |
+ |
< j} {r{}_{ij} \cdot f_{ij} } } \right\rangle |
1101 |
+ |
\end{equation} |
1102 |
+ |
|
1103 |
+ |
\subsubsection{\label{introSection:structuralProperties}Structural Properties} |
1104 |
+ |
|
1105 |
+ |
Structural Properties of a simple fluid can be described by a set of |
1106 |
+ |
distribution functions. Among these functions,\emph{pair |
1107 |
+ |
distribution function}, also known as \emph{radial distribution |
1108 |
+ |
function}, is of most fundamental importance to liquid-state theory. |
1109 |
+ |
Pair distribution function can be gathered by Fourier transforming |
1110 |
+ |
raw data from a series of neutron diffraction experiments and |
1111 |
+ |
integrating over the surface factor \cite{Powles1973}. The |
1112 |
+ |
experiment result can serve as a criterion to justify the |
1113 |
+ |
correctness of the theory. Moreover, various equilibrium |
1114 |
+ |
thermodynamic and structural properties can also be expressed in |
1115 |
+ |
terms of radial distribution function \cite{Allen1987}. |
1116 |
+ |
|
1117 |
+ |
A pair distribution functions $g(r)$ gives the probability that a |
1118 |
+ |
particle $i$ will be located at a distance $r$ from a another |
1119 |
+ |
particle $j$ in the system |
1120 |
+ |
\[ |
1121 |
+ |
g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j |
1122 |
+ |
\ne i} {\delta (r - r_{ij} )} } } \right\rangle. |
1123 |
+ |
\] |
1124 |
+ |
Note that the delta function can be replaced by a histogram in |
1125 |
+ |
computer simulation. Figure |
1126 |
+ |
\ref{introFigure:pairDistributionFunction} shows a typical pair |
1127 |
+ |
distribution function for the liquid argon system. The occurrence of |
1128 |
+ |
several peaks in the plot of $g(r)$ suggests that it is more likely |
1129 |
+ |
to find particles at certain radial values than at others. This is a |
1130 |
+ |
result of the attractive interaction at such distances. Because of |
1131 |
+ |
the strong repulsive forces at short distance, the probability of |
1132 |
+ |
locating particles at distances less than about 2.5{\AA} from each |
1133 |
+ |
other is essentially zero. |
1134 |
+ |
|
1135 |
+ |
%\begin{figure} |
1136 |
+ |
%\centering |
1137 |
+ |
%\includegraphics[width=\linewidth]{pdf.eps} |
1138 |
+ |
%\caption[Pair distribution function for the liquid argon |
1139 |
+ |
%]{Pair distribution function for the liquid argon} |
1140 |
+ |
%\label{introFigure:pairDistributionFunction} |
1141 |
+ |
%\end{figure} |
1142 |
+ |
|
1143 |
+ |
\subsubsection{\label{introSection:timeDependentProperties}Time-dependent |
1144 |
+ |
Properties} |
1145 |
+ |
|
1146 |
+ |
Time-dependent properties are usually calculated using \emph{time |
1147 |
+ |
correlation function}, which correlates random variables $A$ and $B$ |
1148 |
+ |
at two different time |
1149 |
+ |
\begin{equation} |
1150 |
+ |
C_{AB} (t) = \left\langle {A(t)B(0)} \right\rangle. |
1151 |
+ |
\label{introEquation:timeCorrelationFunction} |
1152 |
+ |
\end{equation} |
1153 |
+ |
If $A$ and $B$ refer to same variable, this kind of correlation |
1154 |
+ |
function is called \emph{auto correlation function}. One example of |
1155 |
+ |
auto correlation function is velocity auto-correlation function |
1156 |
+ |
which is directly related to transport properties of molecular |
1157 |
+ |
liquids: |
1158 |
+ |
\[ |
1159 |
+ |
D = \frac{1}{3}\int\limits_0^\infty {\left\langle {v(t) \cdot v(0)} |
1160 |
+ |
\right\rangle } dt |
1161 |
+ |
\] |
1162 |
+ |
where $D$ is diffusion constant. Unlike velocity autocorrelation |
1163 |
+ |
function which is averaging over time origins and over all the |
1164 |
+ |
atoms, dipole autocorrelation are calculated for the entire system. |
1165 |
+ |
The dipole autocorrelation function is given by: |
1166 |
+ |
\[ |
1167 |
+ |
c_{dipole} = \left\langle {u_{tot} (t) \cdot u_{tot} (t)} |
1168 |
+ |
\right\rangle |
1169 |
+ |
\] |
1170 |
+ |
Here $u_{tot}$ is the net dipole of the entire system and is given |
1171 |
+ |
by |
1172 |
+ |
\[ |
1173 |
+ |
u_{tot} (t) = \sum\limits_i {u_i (t)} |
1174 |
+ |
\] |
1175 |
+ |
In principle, many time correlation functions can be related with |
1176 |
+ |
Fourier transforms of the infrared, Raman, and inelastic neutron |
1177 |
+ |
scattering spectra of molecular liquids. In practice, one can |
1178 |
+ |
extract the IR spectrum from the intensity of dipole fluctuation at |
1179 |
+ |
each frequency using the following relationship: |
1180 |
+ |
\[ |
1181 |
+ |
\hat c_{dipole} (v) = \int_{ - \infty }^\infty {c_{dipole} (t)e^{ - |
1182 |
+ |
i2\pi vt} dt} |
1183 |
+ |
\] |
1184 |
+ |
|
1185 |
|
\section{\label{introSection:rigidBody}Dynamics of Rigid Bodies} |
1186 |
|
|
1187 |
|
Rigid bodies are frequently involved in the modeling of different |
1190 |
|
movement of the objects in 3D gaming engine or other physics |
1191 |
|
simulator is governed by the rigid body dynamics. In molecular |
1192 |
|
simulation, rigid body is used to simplify the model in |
1193 |
< |
protein-protein docking study{\cite{Gray03}}. |
1193 |
> |
protein-protein docking study\cite{Gray2003}. |
1194 |
|
|
1195 |
|
It is very important to develop stable and efficient methods to |
1196 |
|
integrate the equations of motion of orientational degrees of |
1198 |
|
rotational degrees of freedom. However, due to its singularity, the |
1199 |
|
numerical integration of corresponding equations of motion is very |
1200 |
|
inefficient and inaccurate. Although an alternative integrator using |
1201 |
< |
different sets of Euler angles can overcome this difficulty\cite{}, |
1202 |
< |
the computational penalty and the lost of angular momentum |
1203 |
< |
conservation still remain. A singularity free representation |
1204 |
< |
utilizing quaternions was developed by Evans in 1977. Unfortunately, |
1205 |
< |
this approach suffer from the nonseparable Hamiltonian resulted from |
1206 |
< |
quaternion representation, which prevents the symplectic algorithm |
1207 |
< |
to be utilized. Another different approach is to apply holonomic |
1208 |
< |
constraints to the atoms belonging to the rigid body. Each atom |
1209 |
< |
moves independently under the normal forces deriving from potential |
1210 |
< |
energy and constraint forces which are used to guarantee the |
1211 |
< |
rigidness. However, due to their iterative nature, SHAKE and Rattle |
1212 |
< |
algorithm converge very slowly when the number of constraint |
1213 |
< |
increases. |
1201 |
> |
different sets of Euler angles can overcome this |
1202 |
> |
difficulty\cite{Barojas1973}, the computational penalty and the lost |
1203 |
> |
of angular momentum conservation still remain. A singularity free |
1204 |
> |
representation utilizing quaternions was developed by Evans in |
1205 |
> |
1977\cite{Evans1977}. Unfortunately, this approach suffer from the |
1206 |
> |
nonseparable Hamiltonian resulted from quaternion representation, |
1207 |
> |
which prevents the symplectic algorithm to be utilized. Another |
1208 |
> |
different approach is to apply holonomic constraints to the atoms |
1209 |
> |
belonging to the rigid body. Each atom moves independently under the |
1210 |
> |
normal forces deriving from potential energy and constraint forces |
1211 |
> |
which are used to guarantee the rigidness. However, due to their |
1212 |
> |
iterative nature, SHAKE and Rattle algorithm converge very slowly |
1213 |
> |
when the number of constraint increases\cite{Ryckaert1977, |
1214 |
> |
Andersen1983}. |
1215 |
|
|
1216 |
|
The break through in geometric literature suggests that, in order to |
1217 |
|
develop a long-term integration scheme, one should preserve the |
1218 |
|
symplectic structure of the flow. Introducing conjugate momentum to |
1219 |
< |
rotation matrix $A$ and re-formulating Hamiltonian's equation, a |
1220 |
< |
symplectic integrator, RSHAKE, was proposed to evolve the |
1221 |
< |
Hamiltonian system in a constraint manifold by iteratively |
1222 |
< |
satisfying the orthogonality constraint $A_t A = 1$. An alternative |
1223 |
< |
method using quaternion representation was developed by Omelyan. |
1224 |
< |
However, both of these methods are iterative and inefficient. In |
1225 |
< |
this section, we will present a symplectic Lie-Poisson integrator |
1226 |
< |
for rigid body developed by Dullweber and his |
1227 |
< |
coworkers\cite{Dullweber1997} in depth. |
1219 |
> |
rotation matrix $Q$ and re-formulating Hamiltonian's equation, a |
1220 |
> |
symplectic integrator, RSHAKE\cite{Kol1997}, was proposed to evolve |
1221 |
> |
the Hamiltonian system in a constraint manifold by iteratively |
1222 |
> |
satisfying the orthogonality constraint $Q_T Q = 1$. An alternative |
1223 |
> |
method using quaternion representation was developed by |
1224 |
> |
Omelyan\cite{Omelyan1998}. However, both of these methods are |
1225 |
> |
iterative and inefficient. In this section, we will present a |
1226 |
> |
symplectic Lie-Poisson integrator for rigid body developed by |
1227 |
> |
Dullweber and his coworkers\cite{Dullweber1997} in depth. |
1228 |
|
|
1229 |
|
\subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body} |
1230 |
|
The motion of the rigid body is Hamiltonian with the Hamiltonian |
1243 |
|
where $I_{ii}$ is the diagonal element of the inertia tensor. This |
1244 |
|
constrained Hamiltonian equation subjects to a holonomic constraint, |
1245 |
|
\begin{equation} |
1246 |
< |
Q^T Q = 1$, \label{introEquation:orthogonalConstraint} |
1246 |
> |
Q^T Q = 1, \label{introEquation:orthogonalConstraint} |
1247 |
|
\end{equation} |
1248 |
|
which is used to ensure rotation matrix's orthogonality. |
1249 |
|
Differentiating \ref{introEquation:orthogonalConstraint} and using |
1256 |
|
Using Equation (\ref{introEquation:motionHamiltonianCoordinate}, |
1257 |
|
\ref{introEquation:motionHamiltonianMomentum}), one can write down |
1258 |
|
the equations of motion, |
970 |
– |
\[ |
971 |
– |
\begin{array}{c} |
972 |
– |
\frac{{dq}}{{dt}} = \frac{p}{m} \label{introEquation:RBMotionPosition}\\ |
973 |
– |
\frac{{dp}}{{dt}} = - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\ |
974 |
– |
\frac{{dQ}}{{dt}} = PJ^{ - 1} \label{introEquation:RBMotionRotation}\\ |
975 |
– |
\frac{{dP}}{{dt}} = - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\ |
976 |
– |
\end{array} |
977 |
– |
\] |
1259 |
|
|
1260 |
+ |
\begin{eqnarray} |
1261 |
+ |
\frac{{dq}}{{dt}} & = & \frac{p}{m} \label{introEquation:RBMotionPosition}\\ |
1262 |
+ |
\frac{{dp}}{{dt}} & = & - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\ |
1263 |
+ |
\frac{{dQ}}{{dt}} & = & PJ^{ - 1} \label{introEquation:RBMotionRotation}\\ |
1264 |
+ |
\frac{{dP}}{{dt}} & = & - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP} |
1265 |
+ |
\end{eqnarray} |
1266 |
+ |
|
1267 |
|
In general, there are two ways to satisfy the holonomic constraints. |
1268 |
|
We can use constraint force provided by lagrange multiplier on the |
1269 |
|
normal manifold to keep the motion on constraint space. Or we can |
1270 |
< |
simply evolve the system in constraint manifold. The two method are |
1271 |
< |
proved to be equivalent. The holonomic constraint and equations of |
1272 |
< |
motions define a constraint manifold for rigid body |
1270 |
> |
simply evolve the system in constraint manifold. These two methods |
1271 |
> |
are proved to be equivalent. The holonomic constraint and equations |
1272 |
> |
of motions define a constraint manifold for rigid body |
1273 |
|
\[ |
1274 |
|
M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1} + J^{ - 1} P^T Q = 0} |
1275 |
|
\right\}. |
1343 |
|
\[ |
1344 |
|
\hat vu = v \times u |
1345 |
|
\] |
1058 |
– |
|
1346 |
|
Using \ref{introEqaution:RBMotionPI}, one can construct a skew |
1347 |
|
matrix, |
1348 |
|
\begin{equation} |
1349 |
< |
(\mathop \Pi \limits^ \bullet - \mathop \Pi \limits^ \bullet ^T |
1349 |
> |
(\mathop \Pi \limits^ \bullet - \mathop \Pi \limits^ {\bullet ^T} |
1350 |
|
){\rm{ }} = {\rm{ }}(\Pi - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi + \Pi J^{ |
1351 |
|
- 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T - X_i F_i (r,Q)^T Q]} - |
1352 |
|
(\Lambda - \Lambda ^T ) . \label{introEquation:skewMatrixPI} |
1355 |
|
\ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange |
1356 |
|
multiplier $\Lambda$ is absent from the equations of motion. This |
1357 |
|
unique property eliminate the requirement of iterations which can |
1358 |
< |
not be avoided in other methods\cite{}. |
1358 |
> |
not be avoided in other methods\cite{Kol1997, Omelyan1998}. |
1359 |
|
|
1360 |
|
Applying hat-map isomorphism, we obtain the equation of motion for |
1361 |
|
angular momentum on body frame |
1423 |
|
0 & { - \sin \theta _1 } & {\cos \theta _1 } \\ |
1424 |
|
\end{array}} \right),\theta _1 = \frac{{\pi _1 }}{{I_1 }}\Delta t. |
1425 |
|
\] |
1426 |
< |
To reduce the cost of computing expensive functions in e^{\Delta |
1427 |
< |
tR_1 }, we can use Cayley transformation, |
1426 |
> |
To reduce the cost of computing expensive functions in $e^{\Delta |
1427 |
> |
tR_1 }$, we can use Cayley transformation, |
1428 |
|
\[ |
1429 |
|
e^{\Delta tR_1 } \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1 |
1430 |
|
) |
1431 |
|
\] |
1432 |
< |
|
1146 |
< |
The flow maps for $T_2^r$ and $T_2^r$ can be found in the same |
1432 |
> |
The flow maps for $T_2^r$ and $T_3^r$ can be found in the same |
1433 |
|
manner. |
1434 |
|
|
1435 |
|
In order to construct a second-order symplectic method, we split the |
1481 |
|
\] |
1482 |
|
The equations of motion corresponding to potential energy and |
1483 |
|
kinetic energy are listed in the below table, |
1484 |
+ |
\begin{table} |
1485 |
+ |
\caption{Equations of motion due to Potential and Kinetic Energies} |
1486 |
|
\begin{center} |
1487 |
|
\begin{tabular}{|l|l|} |
1488 |
|
\hline |
1495 |
|
\hline |
1496 |
|
\end{tabular} |
1497 |
|
\end{center} |
1498 |
< |
A second-order symplectic method is now obtained by the composition |
1499 |
< |
of the flow maps, |
1498 |
> |
\end{table} |
1499 |
> |
A second-order symplectic method is now obtained by the |
1500 |
> |
composition of the flow maps, |
1501 |
|
\[ |
1502 |
|
\varphi _{\Delta t} = \varphi _{\Delta t/2,V} \circ \varphi |
1503 |
|
_{\Delta t,T} \circ \varphi _{\Delta t/2,V}. |
1504 |
|
\] |
1505 |
< |
Moreover, \varphi _{\Delta t/2,V} can be divided into two sub-flows |
1506 |
< |
which corresponding to force and torque respectively, |
1505 |
> |
Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two |
1506 |
> |
sub-flows which corresponding to force and torque respectively, |
1507 |
|
\[ |
1508 |
|
\varphi _{\Delta t/2,V} = \varphi _{\Delta t/2,F} \circ \varphi |
1509 |
|
_{\Delta t/2,\tau }. |
1510 |
|
\] |
1511 |
|
Since the associated operators of $\varphi _{\Delta t/2,F} $ and |
1512 |
|
$\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition |
1513 |
< |
order inside \varphi _{\Delta t/2,V} does not matter. |
1513 |
> |
order inside $\varphi _{\Delta t/2,V}$ does not matter. |
1514 |
|
|
1515 |
|
Furthermore, kinetic potential can be separated to translational |
1516 |
|
kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$, |
1540 |
|
mimics a simple heat bath with stochastic and dissipative forces, |
1541 |
|
has been applied in a variety of studies. This section will review |
1542 |
|
the theory of Langevin dynamics simulation. A brief derivation of |
1543 |
< |
generalized Langevin Dynamics will be given first. Follow that, we |
1543 |
> |
generalized Langevin equation will be given first. Follow that, we |
1544 |
|
will discuss the physical meaning of the terms appearing in the |
1545 |
|
equation as well as the calculation of friction tensor from |
1546 |
|
hydrodynamics theory. |
1547 |
|
|
1548 |
< |
\subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics} |
1548 |
> |
\subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation} |
1549 |
|
|
1550 |
+ |
Harmonic bath model, in which an effective set of harmonic |
1551 |
+ |
oscillators are used to mimic the effect of a linearly responding |
1552 |
+ |
environment, has been widely used in quantum chemistry and |
1553 |
+ |
statistical mechanics. One of the successful applications of |
1554 |
+ |
Harmonic bath model is the derivation of Deriving Generalized |
1555 |
+ |
Langevin Dynamics. Lets consider a system, in which the degree of |
1556 |
+ |
freedom $x$ is assumed to couple to the bath linearly, giving a |
1557 |
+ |
Hamiltonian of the form |
1558 |
|
\begin{equation} |
1559 |
|
H = \frac{{p^2 }}{{2m}} + U(x) + H_B + \Delta U(x,x_1 , \ldots x_N) |
1560 |
< |
\label{introEquation:bathGLE} |
1560 |
> |
\label{introEquation:bathGLE}. |
1561 |
|
\end{equation} |
1562 |
< |
where $H_B$ is harmonic bath Hamiltonian, |
1562 |
> |
Here $p$ is a momentum conjugate to $q$, $m$ is the mass associated |
1563 |
> |
with this degree of freedom, $H_B$ is harmonic bath Hamiltonian, |
1564 |
|
\[ |
1565 |
< |
H_B =\sum\limits_{\alpha = 1}^N {\left\{ {\frac{{p_\alpha ^2 |
1566 |
< |
}}{{2m_\alpha }} + \frac{1}{2}m_\alpha w_\alpha ^2 } \right\}} |
1565 |
> |
H_B = \sum\limits_{\alpha = 1}^N {\left\{ {\frac{{p_\alpha ^2 |
1566 |
> |
}}{{2m_\alpha }} + \frac{1}{2}m_\alpha \omega _\alpha ^2 } |
1567 |
> |
\right\}} |
1568 |
|
\] |
1569 |
< |
and $\Delta U$ is bilinear system-bath coupling, |
1569 |
> |
where the index $\alpha$ runs over all the bath degrees of freedom, |
1570 |
> |
$\omega _\alpha$ are the harmonic bath frequencies, $m_\alpha$ are |
1571 |
> |
the harmonic bath masses, and $\Delta U$ is bilinear system-bath |
1572 |
> |
coupling, |
1573 |
|
\[ |
1574 |
|
\Delta U = - \sum\limits_{\alpha = 1}^N {g_\alpha x_\alpha x} |
1575 |
|
\] |
1576 |
< |
Completing the square, |
1576 |
> |
where $g_\alpha$ are the coupling constants between the bath and the |
1577 |
> |
coordinate $x$. Introducing |
1578 |
|
\[ |
1579 |
< |
H_B + \Delta U = \sum\limits_{\alpha = 1}^N {\left\{ |
1580 |
< |
{\frac{{p_\alpha ^2 }}{{2m_\alpha }} + \frac{1}{2}m_\alpha |
1581 |
< |
w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha }}{{m_\alpha |
1582 |
< |
w_\alpha ^2 }}x} \right)^2 } \right\}} - \sum\limits_{\alpha = |
1583 |
< |
1}^N {\frac{{g_\alpha ^2 }}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1281 |
< |
\] |
1282 |
< |
and putting it back into Eq.~\ref{introEquation:bathGLE}, |
1579 |
> |
W(x) = U(x) - \sum\limits_{\alpha = 1}^N {\frac{{g_\alpha ^2 |
1580 |
> |
}}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1581 |
> |
\] and combining the last two terms in Equation |
1582 |
> |
\ref{introEquation:bathGLE}, we may rewrite the Harmonic bath |
1583 |
> |
Hamiltonian as |
1584 |
|
\[ |
1585 |
|
H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha = 1}^N |
1586 |
|
{\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha }} + \frac{1}{2}m_\alpha |
1587 |
|
w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha }}{{m_\alpha |
1588 |
|
w_\alpha ^2 }}x} \right)^2 } \right\}} |
1589 |
|
\] |
1289 |
– |
where |
1290 |
– |
\[ |
1291 |
– |
W(x) = U(x) - \sum\limits_{\alpha = 1}^N {\frac{{g_\alpha ^2 |
1292 |
– |
}}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1293 |
– |
\] |
1590 |
|
Since the first two terms of the new Hamiltonian depend only on the |
1591 |
|
system coordinates, we can get the equations of motion for |
1592 |
|
Generalized Langevin Dynamics by Hamilton's equations |
1593 |
|
\ref{introEquation:motionHamiltonianCoordinate, |
1594 |
|
introEquation:motionHamiltonianMomentum}, |
1595 |
< |
\begin{align} |
1596 |
< |
\dot p &= - \frac{{\partial H}}{{\partial x}} |
1597 |
< |
&= m\ddot x |
1598 |
< |
&= - \frac{{\partial W(x)}}{{\partial x}} - \sum\limits_{\alpha = 1}^N {g_\alpha \left( {x_\alpha - \frac{{g_\alpha }}{{m_\alpha w_\alpha ^2 }}x} \right)} |
1599 |
< |
\label{introEquation:Lp5} |
1600 |
< |
\end{align} |
1601 |
< |
, and |
1602 |
< |
\begin{align} |
1603 |
< |
\dot p_\alpha &= - \frac{{\partial H}}{{\partial x_\alpha }} |
1604 |
< |
&= m\ddot x_\alpha |
1605 |
< |
&= \- m_\alpha w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha}}{{m_\alpha w_\alpha ^2 }}x} \right) |
1606 |
< |
\end{align} |
1595 |
> |
\begin{equation} |
1596 |
> |
m\ddot x = - \frac{{\partial W(x)}}{{\partial x}} - |
1597 |
> |
\sum\limits_{\alpha = 1}^N {g_\alpha \left( {x_\alpha - |
1598 |
> |
\frac{{g_\alpha }}{{m_\alpha w_\alpha ^2 }}x} \right)}, |
1599 |
> |
\label{introEquation:coorMotionGLE} |
1600 |
> |
\end{equation} |
1601 |
> |
and |
1602 |
> |
\begin{equation} |
1603 |
> |
m\ddot x_\alpha = - m_\alpha w_\alpha ^2 \left( {x_\alpha - |
1604 |
> |
\frac{{g_\alpha }}{{m_\alpha w_\alpha ^2 }}x} \right). |
1605 |
> |
\label{introEquation:bathMotionGLE} |
1606 |
> |
\end{equation} |
1607 |
|
|
1608 |
< |
\subsection{\label{introSection:laplaceTransform}The Laplace Transform} |
1608 |
> |
In order to derive an equation for $x$, the dynamics of the bath |
1609 |
> |
variables $x_\alpha$ must be solved exactly first. As an integral |
1610 |
> |
transform which is particularly useful in solving linear ordinary |
1611 |
> |
differential equations, Laplace transform is the appropriate tool to |
1612 |
> |
solve this problem. The basic idea is to transform the difficult |
1613 |
> |
differential equations into simple algebra problems which can be |
1614 |
> |
solved easily. Then applying inverse Laplace transform, also known |
1615 |
> |
as the Bromwich integral, we can retrieve the solutions of the |
1616 |
> |
original problems. |
1617 |
|
|
1618 |
< |
\[ |
1619 |
< |
L(x) = \int_0^\infty {x(t)e^{ - pt} dt} |
1316 |
< |
\] |
1317 |
< |
|
1618 |
> |
Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace |
1619 |
> |
transform of f(t) is a new function defined as |
1620 |
|
\[ |
1621 |
< |
L(x + y) = L(x) + L(y) |
1621 |
> |
L(f(t)) \equiv F(p) = \int_0^\infty {f(t)e^{ - pt} dt} |
1622 |
|
\] |
1623 |
+ |
where $p$ is real and $L$ is called the Laplace Transform |
1624 |
+ |
Operator. Below are some important properties of Laplace transform |
1625 |
|
|
1626 |
< |
\[ |
1627 |
< |
L(ax) = aL(x) |
1628 |
< |
\] |
1626 |
> |
\begin{eqnarray*} |
1627 |
> |
L(x + y) & = & L(x) + L(y) \\ |
1628 |
> |
L(ax) & = & aL(x) \\ |
1629 |
> |
L(\dot x) & = & pL(x) - px(0) \\ |
1630 |
> |
L(\ddot x)& = & p^2 L(x) - px(0) - \dot x(0) \\ |
1631 |
> |
L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right)& = & G(p)H(p) \\ |
1632 |
> |
\end{eqnarray*} |
1633 |
|
|
1326 |
– |
\[ |
1327 |
– |
L(\dot x) = pL(x) - px(0) |
1328 |
– |
\] |
1634 |
|
|
1635 |
< |
\[ |
1636 |
< |
L(\ddot x) = p^2 L(x) - px(0) - \dot x(0) |
1637 |
< |
\] |
1635 |
> |
Applying Laplace transform to the bath coordinates, we obtain |
1636 |
> |
\begin{eqnarray*} |
1637 |
> |
p^2 L(x_\alpha ) - px_\alpha (0) - \dot x_\alpha (0) & = & - \omega _\alpha ^2 L(x_\alpha ) + \frac{{g_\alpha }}{{\omega _\alpha }}L(x) \\ |
1638 |
> |
L(x_\alpha ) & = & \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }} \\ |
1639 |
> |
\end{eqnarray*} |
1640 |
|
|
1641 |
< |
\[ |
1642 |
< |
L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p) |
1643 |
< |
\] |
1641 |
> |
By the same way, the system coordinates become |
1642 |
> |
\begin{eqnarray*} |
1643 |
> |
mL(\ddot x) & = & - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\ |
1644 |
> |
& & \mbox{} - \sum\limits_{\alpha = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}\frac{p}{{p^2 + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2 + \omega _\alpha ^2 }}g_\alpha x_\alpha (0) - \frac{1}{{p^2 + \omega _\alpha ^2 }}g_\alpha \dot x_\alpha (0)} \right\}} \\ |
1645 |
> |
\end{eqnarray*} |
1646 |
|
|
1647 |
< |
Some relatively important transformation, |
1647 |
> |
With the help of some relatively important inverse Laplace |
1648 |
> |
transformations: |
1649 |
|
\[ |
1650 |
< |
L(\cos at) = \frac{p}{{p^2 + a^2 }} |
1650 |
> |
\begin{array}{c} |
1651 |
> |
L(\cos at) = \frac{p}{{p^2 + a^2 }} \\ |
1652 |
> |
L(\sin at) = \frac{a}{{p^2 + a^2 }} \\ |
1653 |
> |
L(1) = \frac{1}{p} \\ |
1654 |
> |
\end{array} |
1655 |
|
\] |
1656 |
< |
|
1657 |
< |
\[ |
1658 |
< |
L(\sin at) = \frac{a}{{p^2 + a^2 }} |
1345 |
< |
\] |
1346 |
< |
|
1347 |
< |
\[ |
1348 |
< |
L(1) = \frac{1}{p} |
1349 |
< |
\] |
1350 |
< |
|
1351 |
< |
First, the bath coordinates, |
1352 |
< |
\[ |
1353 |
< |
p^2 L(x_\alpha ) - px_\alpha (0) - \dot x_\alpha (0) = - \omega |
1354 |
< |
_\alpha ^2 L(x_\alpha ) + \frac{{g_\alpha }}{{\omega _\alpha |
1355 |
< |
}}L(x) |
1356 |
< |
\] |
1357 |
< |
\[ |
1358 |
< |
L(x_\alpha ) = \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + |
1359 |
< |
px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }} |
1360 |
< |
\] |
1361 |
< |
Then, the system coordinates, |
1362 |
< |
\begin{align} |
1363 |
< |
mL(\ddot x) &= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} - |
1364 |
< |
\sum\limits_{\alpha = 1}^N {\left\{ {\frac{{\frac{{g_\alpha |
1365 |
< |
}}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha |
1366 |
< |
(0)}}{{p^2 + \omega _\alpha ^2 }} - \frac{{g_\alpha ^2 }}{{m_\alpha |
1367 |
< |
}}\omega _\alpha ^2 L(x)} \right\}} |
1368 |
< |
% |
1369 |
< |
&= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} - |
1370 |
< |
\sum\limits_{\alpha = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}\frac{p}{{p^2 + \omega _\alpha ^2 }}pL(x) |
1371 |
< |
- \frac{p}{{p^2 + \omega _\alpha ^2 }}g_\alpha x_\alpha (0) |
1372 |
< |
- \frac{1}{{p^2 + \omega _\alpha ^2 }}g_\alpha \dot x_\alpha (0)} \right\}} |
1373 |
< |
\end{align} |
1374 |
< |
Then, the inverse transform, |
1375 |
< |
|
1376 |
< |
\begin{align} |
1377 |
< |
m\ddot x &= - \frac{{\partial W(x)}}{{\partial x}} - |
1656 |
> |
, we obtain |
1657 |
> |
\begin{eqnarray*} |
1658 |
> |
m\ddot x & = & - \frac{{\partial W(x)}}{{\partial x}} - |
1659 |
|
\sum\limits_{\alpha = 1}^N {\left\{ {\left( { - \frac{{g_\alpha ^2 |
1660 |
|
}}{{m_\alpha \omega _\alpha ^2 }}} \right)\int_0^t {\cos (\omega |
1661 |
< |
_\alpha t)\dot x(t - \tau )d\tau - \left[ {g_\alpha x_\alpha (0) |
1662 |
< |
- \frac{{g_\alpha }}{{m_\alpha \omega _\alpha }}} \right]\cos |
1663 |
< |
(\omega _\alpha t) - \frac{{g_\alpha \dot x_\alpha (0)}}{{\omega |
1664 |
< |
_\alpha }}\sin (\omega _\alpha t)} } \right\}} |
1665 |
< |
% |
1666 |
< |
&= - \frac{{\partial W(x)}}{{\partial x}} - \int_0^t |
1661 |
> |
_\alpha t)\dot x(t - \tau )d\tau } } \right\}} \\ |
1662 |
> |
& & + \sum\limits_{\alpha = 1}^N {\left\{ {\left[ {g_\alpha |
1663 |
> |
x_\alpha (0) - \frac{{g_\alpha }}{{m_\alpha \omega _\alpha }}} |
1664 |
> |
\right]\cos (\omega _\alpha t) + \frac{{g_\alpha \dot x_\alpha |
1665 |
> |
(0)}}{{\omega _\alpha }}\sin (\omega _\alpha t)} \right\}} |
1666 |
> |
\end{eqnarray*} |
1667 |
> |
\begin{eqnarray*} |
1668 |
> |
m\ddot x & = & - \frac{{\partial W(x)}}{{\partial x}} - \int_0^t |
1669 |
|
{\sum\limits_{\alpha = 1}^N {\left( { - \frac{{g_\alpha ^2 |
1670 |
|
}}{{m_\alpha \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha |
1671 |
< |
t)\dot x(t - \tau )d} \tau } + \sum\limits_{\alpha = 1}^N {\left\{ |
1672 |
< |
{\left[ {g_\alpha x_\alpha (0) - \frac{{g_\alpha }}{{m_\alpha |
1673 |
< |
\omega _\alpha }}} \right]\cos (\omega _\alpha t) + |
1674 |
< |
\frac{{g_\alpha \dot x_\alpha (0)}}{{\omega _\alpha }}\sin |
1675 |
< |
(\omega _\alpha t)} \right\}} |
1676 |
< |
\end{align} |
1677 |
< |
|
1671 |
> |
t)\dot x(t - \tau )d} \tau } \\ |
1672 |
> |
& & + \sum\limits_{\alpha = 1}^N {\left\{ {\left[ {g_\alpha |
1673 |
> |
x_\alpha (0) - \frac{{g_\alpha }}{{m_\alpha \omega _\alpha }}} |
1674 |
> |
\right]\cos (\omega _\alpha t) + \frac{{g_\alpha \dot x_\alpha |
1675 |
> |
(0)}}{{\omega _\alpha }}\sin (\omega _\alpha t)} \right\}} |
1676 |
> |
\end{eqnarray*} |
1677 |
> |
Introducing a \emph{dynamic friction kernel} |
1678 |
|
\begin{equation} |
1679 |
+ |
\xi (t) = \sum\limits_{\alpha = 1}^N {\left( { - \frac{{g_\alpha ^2 |
1680 |
+ |
}}{{m_\alpha \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha t)} |
1681 |
+ |
\label{introEquation:dynamicFrictionKernelDefinition} |
1682 |
+ |
\end{equation} |
1683 |
+ |
and \emph{a random force} |
1684 |
+ |
\begin{equation} |
1685 |
+ |
R(t) = \sum\limits_{\alpha = 1}^N {\left( {g_\alpha x_\alpha (0) |
1686 |
+ |
- \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}x(0)} |
1687 |
+ |
\right)\cos (\omega _\alpha t)} + \frac{{\dot x_\alpha |
1688 |
+ |
(0)}}{{\omega _\alpha }}\sin (\omega _\alpha t), |
1689 |
+ |
\label{introEquation:randomForceDefinition} |
1690 |
+ |
\end{equation} |
1691 |
+ |
the equation of motion can be rewritten as |
1692 |
+ |
\begin{equation} |
1693 |
|
m\ddot x = - \frac{{\partial W}}{{\partial x}} - \int_0^t {\xi |
1694 |
|
(t)\dot x(t - \tau )d\tau } + R(t) |
1695 |
|
\label{introEuqation:GeneralizedLangevinDynamics} |
1696 |
|
\end{equation} |
1697 |
< |
%where $ {\xi (t)}$ is friction kernel, $R(t)$ is random force and |
1698 |
< |
%$W$ is the potential of mean force. $W(x) = - kT\ln p(x)$ |
1697 |
> |
which is known as the \emph{generalized Langevin equation}. |
1698 |
> |
|
1699 |
> |
\subsubsection{\label{introSection:randomForceDynamicFrictionKernel}Random Force and Dynamic Friction Kernel} |
1700 |
> |
|
1701 |
> |
One may notice that $R(t)$ depends only on initial conditions, which |
1702 |
> |
implies it is completely deterministic within the context of a |
1703 |
> |
harmonic bath. However, it is easy to verify that $R(t)$ is totally |
1704 |
> |
uncorrelated to $x$ and $\dot x$, |
1705 |
|
\[ |
1706 |
< |
\xi (t) = \sum\limits_{\alpha = 1}^N {\left( { - \frac{{g_\alpha ^2 |
1707 |
< |
}}{{m_\alpha \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha t)} |
1706 |
> |
\begin{array}{l} |
1707 |
> |
\left\langle {x(t)R(t)} \right\rangle = 0, \\ |
1708 |
> |
\left\langle {\dot x(t)R(t)} \right\rangle = 0. \\ |
1709 |
> |
\end{array} |
1710 |
|
\] |
1711 |
< |
For an infinite harmonic bath, we can use the spectral density and |
1712 |
< |
an integral over frequencies. |
1711 |
> |
This property is what we expect from a truly random process. As long |
1712 |
> |
as the model, which is gaussian distribution in general, chosen for |
1713 |
> |
$R(t)$ is a truly random process, the stochastic nature of the GLE |
1714 |
> |
still remains. |
1715 |
|
|
1716 |
+ |
%dynamic friction kernel |
1717 |
+ |
The convolution integral |
1718 |
|
\[ |
1719 |
< |
R(t) = \sum\limits_{\alpha = 1}^N {\left( {g_\alpha x_\alpha (0) |
1411 |
< |
- \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}x(0)} |
1412 |
< |
\right)\cos (\omega _\alpha t)} + \frac{{\dot x_\alpha |
1413 |
< |
(0)}}{{\omega _\alpha }}\sin (\omega _\alpha t) |
1719 |
> |
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } |
1720 |
|
\] |
1721 |
< |
The random forces depend only on initial conditions. |
1721 |
> |
depends on the entire history of the evolution of $x$, which implies |
1722 |
> |
that the bath retains memory of previous motions. In other words, |
1723 |
> |
the bath requires a finite time to respond to change in the motion |
1724 |
> |
of the system. For a sluggish bath which responds slowly to changes |
1725 |
> |
in the system coordinate, we may regard $\xi(t)$ as a constant |
1726 |
> |
$\xi(t) = \Xi_0$. Hence, the convolution integral becomes |
1727 |
> |
\[ |
1728 |
> |
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } = \xi _0 (x(t) - x(0)) |
1729 |
> |
\] |
1730 |
> |
and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1731 |
> |
\[ |
1732 |
> |
m\ddot x = - \frac{\partial }{{\partial x}}\left( {W(x) + |
1733 |
> |
\frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t), |
1734 |
> |
\] |
1735 |
> |
which can be used to describe dynamic caging effect. The other |
1736 |
> |
extreme is the bath that responds infinitely quickly to motions in |
1737 |
> |
the system. Thus, $\xi (t)$ can be taken as a $delta$ function in |
1738 |
> |
time: |
1739 |
> |
\[ |
1740 |
> |
\xi (t) = 2\xi _0 \delta (t) |
1741 |
> |
\] |
1742 |
> |
Hence, the convolution integral becomes |
1743 |
> |
\[ |
1744 |
> |
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } = 2\xi _0 \int_0^t |
1745 |
> |
{\delta (t)\dot x(t - \tau )d\tau } = \xi _0 \dot x(t), |
1746 |
> |
\] |
1747 |
> |
and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1748 |
> |
\begin{equation} |
1749 |
> |
m\ddot x = - \frac{{\partial W(x)}}{{\partial x}} - \xi _0 \dot |
1750 |
> |
x(t) + R(t) \label{introEquation:LangevinEquation} |
1751 |
> |
\end{equation} |
1752 |
> |
which is known as the Langevin equation. The static friction |
1753 |
> |
coefficient $\xi _0$ can either be calculated from spectral density |
1754 |
> |
or be determined by Stokes' law for regular shaped particles.A |
1755 |
> |
briefly review on calculating friction tensor for arbitrary shaped |
1756 |
> |
particles is given in Sec.~\ref{introSection:frictionTensor}. |
1757 |
|
|
1758 |
|
\subsubsection{\label{introSection:secondFluctuationDissipation}The Second Fluctuation Dissipation Theorem} |
1759 |
< |
So we can define a new set of coordinates, |
1759 |
> |
|
1760 |
> |
Defining a new set of coordinates, |
1761 |
|
\[ |
1762 |
|
q_\alpha (t) = x_\alpha (t) - \frac{1}{{m_\alpha \omega _\alpha |
1763 |
|
^2 }}x(0) |
1764 |
< |
\] |
1765 |
< |
This makes |
1764 |
> |
\], |
1765 |
> |
we can rewrite $R(T)$ as |
1766 |
|
\[ |
1767 |
< |
R(t) = \sum\limits_{\alpha = 1}^N {g_\alpha q_\alpha (t)} |
1767 |
> |
R(t) = \sum\limits_{\alpha = 1}^N {g_\alpha q_\alpha (t)}. |
1768 |
|
\] |
1769 |
|
And since the $q$ coordinates are harmonic oscillators, |
1428 |
– |
\[ |
1429 |
– |
\begin{array}{l} |
1430 |
– |
\left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle = \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t) \\ |
1431 |
– |
\left\langle {q_\alpha (t)q_\beta (0)} \right\rangle = \delta _{\alpha \beta } \left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle \\ |
1432 |
– |
\end{array} |
1433 |
– |
\] |
1770 |
|
|
1771 |
< |
\begin{align} |
1772 |
< |
\left\langle {R(t)R(0)} \right\rangle &= \sum\limits_\alpha |
1773 |
< |
{\sum\limits_\beta {g_\alpha g_\beta \left\langle {q_\alpha |
1774 |
< |
(t)q_\beta (0)} \right\rangle } } |
1775 |
< |
% |
1776 |
< |
&= \sum\limits_\alpha {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} |
1777 |
< |
\right\rangle \cos (\omega _\alpha t)} |
1778 |
< |
% |
1443 |
< |
&= kT\xi (t) |
1444 |
< |
\end{align} |
1771 |
> |
\begin{eqnarray*} |
1772 |
> |
\left\langle {q_\alpha ^2 } \right\rangle & = & \frac{{kT}}{{m_\alpha \omega _\alpha ^2 }} \\ |
1773 |
> |
\left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle & = & \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t) \\ |
1774 |
> |
\left\langle {q_\alpha (t)q_\beta (0)} \right\rangle & = &\delta _{\alpha \beta } \left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle \\ |
1775 |
> |
\left\langle {R(t)R(0)} \right\rangle & = & \sum\limits_\alpha {\sum\limits_\beta {g_\alpha g_\beta \left\langle {q_\alpha (t)q_\beta (0)} \right\rangle } } \\ |
1776 |
> |
& = &\sum\limits_\alpha {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t)} \\ |
1777 |
> |
& = &kT\xi (t) \\ |
1778 |
> |
\end{eqnarray*} |
1779 |
|
|
1780 |
+ |
Thus, we recover the \emph{second fluctuation dissipation theorem} |
1781 |
|
\begin{equation} |
1782 |
|
\xi (t) = \left\langle {R(t)R(0)} \right\rangle |
1783 |
< |
\label{introEquation:secondFluctuationDissipation} |
1783 |
> |
\label{introEquation:secondFluctuationDissipation}. |
1784 |
|
\end{equation} |
1785 |
+ |
In effect, it acts as a constraint on the possible ways in which one |
1786 |
+ |
can model the random force and friction kernel. |
1787 |
|
|
1788 |
|
\subsection{\label{introSection:frictionTensor} Friction Tensor} |
1789 |
|
Theoretically, the friction kernel can be determined using velocity |
1791 |
|
when the system become more and more complicate. Instead, various |
1792 |
|
approaches based on hydrodynamics have been developed to calculate |
1793 |
|
the friction coefficients. The friction effect is isotropic in |
1794 |
< |
Equation, \zeta can be taken as a scalar. In general, friction |
1795 |
< |
tensor \Xi is a $6\times 6$ matrix given by |
1794 |
> |
Equation, $\zeta$ can be taken as a scalar. In general, friction |
1795 |
> |
tensor $\Xi$ is a $6\times 6$ matrix given by |
1796 |
|
\[ |
1797 |
|
\Xi = \left( {\begin{array}{*{20}c} |
1798 |
|
{\Xi _{}^{tt} } & {\Xi _{}^{rt} } \\ |
1848 |
|
hydrodynamics theory, because their properties can be calculated |
1849 |
|
exactly. In 1936, Perrin extended Stokes's law to general ellipsoid, |
1850 |
|
also called a triaxial ellipsoid, which is given in Cartesian |
1851 |
< |
coordinates by |
1851 |
> |
coordinates by\cite{Perrin1934, Perrin1936} |
1852 |
|
\[ |
1853 |
|
\frac{{x^2 }}{{a^2 }} + \frac{{y^2 }}{{b^2 }} + \frac{{z^2 }}{{c^2 |
1854 |
|
}} = 1 |
1892 |
|
hydrodynamic properties of rigid bodies. However, since the mapping |
1893 |
|
from all possible ellipsoidal space, $r$-space, to all possible |
1894 |
|
combination of rotational diffusion coefficients, $D$-space is not |
1895 |
< |
unique\cite{Wegener79} as well as the intrinsic coupling between |
1896 |
< |
translational and rotational motion of rigid body\cite{}, general |
1897 |
< |
ellipsoid is not always suitable for modeling arbitrarily shaped |
1898 |
< |
rigid molecule. A number of studies have been devoted to determine |
1899 |
< |
the friction tensor for irregularly shaped rigid bodies using more |
1900 |
< |
advanced method\cite{} where the molecule of interest was modeled by |
1901 |
< |
combinations of spheres(beads)\cite{} and the hydrodynamics |
1902 |
< |
properties of the molecule can be calculated using the hydrodynamic |
1903 |
< |
interaction tensor. Let us consider a rigid assembly of $N$ beads |
1904 |
< |
immersed in a continuous medium. Due to hydrodynamics interaction, |
1905 |
< |
the ``net'' velocity of $i$th bead, $v'_i$ is different than its |
1906 |
< |
unperturbed velocity $v_i$, |
1895 |
> |
unique\cite{Wegener1979} as well as the intrinsic coupling between |
1896 |
> |
translational and rotational motion of rigid body, general ellipsoid |
1897 |
> |
is not always suitable for modeling arbitrarily shaped rigid |
1898 |
> |
molecule. A number of studies have been devoted to determine the |
1899 |
> |
friction tensor for irregularly shaped rigid bodies using more |
1900 |
> |
advanced method where the molecule of interest was modeled by |
1901 |
> |
combinations of spheres(beads)\cite{Carrasco1999} and the |
1902 |
> |
hydrodynamics properties of the molecule can be calculated using the |
1903 |
> |
hydrodynamic interaction tensor. Let us consider a rigid assembly of |
1904 |
> |
$N$ beads immersed in a continuous medium. Due to hydrodynamics |
1905 |
> |
interaction, the ``net'' velocity of $i$th bead, $v'_i$ is different |
1906 |
> |
than its unperturbed velocity $v_i$, |
1907 |
|
\[ |
1908 |
|
v'_i = v_i - \sum\limits_{j \ne i} {T_{ij} F_j } |
1909 |
|
\] |
1924 |
|
\end{equation} |
1925 |
|
Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$. |
1926 |
|
A second order expression for element of different size was |
1927 |
< |
introduced by Rotne and Prager\cite{} and improved by Garc\'{i}a de |
1928 |
< |
la Torre and Bloomfield, |
1927 |
> |
introduced by Rotne and Prager\cite{Rotne1969} and improved by |
1928 |
> |
Garc\'{i}a de la Torre and Bloomfield\cite{Torre1977}, |
1929 |
|
\begin{equation} |
1930 |
|
T_{ij} = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I + |
1931 |
|
\frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma |
1959 |
|
B_{ij} = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij} |
1960 |
|
)T_{ij} |
1961 |
|
\] |
1962 |
< |
where \delta _{ij} is Kronecker delta function. Inverting matrix |
1962 |
> |
where $\delta _{ij}$ is Kronecker delta function. Inverting matrix |
1963 |
|
$B$, we obtain |
1964 |
|
|
1965 |
|
\[ |
2003 |
|
Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin}, |
2004 |
|
we can easily find out that the translational resistance tensor is |
2005 |
|
origin independent, while the rotational resistance tensor and |
2006 |
< |
translation-rotation coupling resistance tensor do depend on the |
2006 |
> |
translation-rotation coupling resistance tensor depend on the |
2007 |
|
origin. Given resistance tensor at an arbitrary origin $O$, and a |
2008 |
|
vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can |
2009 |
|
obtain the resistance tensor at $P$ by |
2011 |
|
\begin{array}{l} |
2012 |
|
\Xi _P^{tt} = \Xi _O^{tt} \\ |
2013 |
|
\Xi _P^{tr} = \Xi _P^{rt} = \Xi _O^{tr} - U_{OP} \Xi _O^{tt} \\ |
2014 |
< |
\Xi _P^{rr} = \Xi _O^{rr} - U_{OP} \Xi _O^{tt} U_{OP} + \Xi _O^{tr} U_{OP} - U_{OP} \Xi _O^{tr} ^{^T } \\ |
2014 |
> |
\Xi _P^{rr} = \Xi _O^{rr} - U_{OP} \Xi _O^{tt} U_{OP} + \Xi _O^{tr} U_{OP} - U_{OP} \Xi _O^{{tr} ^{^T }} \\ |
2015 |
|
\end{array} |
2016 |
|
\label{introEquation:resistanceTensorTransformation} |
2017 |
|
\end{equation} |
2026 |
|
Using Equations \ref{introEquation:definitionCR} and |
2027 |
|
\ref{introEquation:resistanceTensorTransformation}, one can locate |
2028 |
|
the position of center of resistance, |
2029 |
< |
\[ |
2030 |
< |
\left( \begin{array}{l} |
2029 |
> |
\begin{eqnarray*} |
2030 |
> |
\left( \begin{array}{l} |
2031 |
|
x_{OR} \\ |
2032 |
|
y_{OR} \\ |
2033 |
|
z_{OR} \\ |
2034 |
< |
\end{array} \right) = \left( {\begin{array}{*{20}c} |
2034 |
> |
\end{array} \right) & = &\left( {\begin{array}{*{20}c} |
2035 |
|
{(\Xi _O^{rr} )_{yy} + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} } \\ |
2036 |
|
{ - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz} + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} } \\ |
2037 |
|
{ - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx} + (\Xi _O^{rr} )_{yy} } \\ |
2038 |
< |
\end{array}} \right)^{ - 1} \left( \begin{array}{l} |
2038 |
> |
\end{array}} \right)^{ - 1} \\ |
2039 |
> |
& & \left( \begin{array}{l} |
2040 |
|
(\Xi _O^{tr} )_{yz} - (\Xi _O^{tr} )_{zy} \\ |
2041 |
|
(\Xi _O^{tr} )_{zx} - (\Xi _O^{tr} )_{xz} \\ |
2042 |
|
(\Xi _O^{tr} )_{xy} - (\Xi _O^{tr} )_{yx} \\ |
2043 |
< |
\end{array} \right). |
2044 |
< |
\] |
2043 |
> |
\end{array} \right) \\ |
2044 |
> |
\end{eqnarray*} |
2045 |
> |
|
2046 |
> |
|
2047 |
> |
|
2048 |
|
where $x_OR$, $y_OR$, $z_OR$ are the components of the vector |
2049 |
|
joining center of resistance $R$ and origin $O$. |
1709 |
– |
|
1710 |
– |
%\section{\label{introSection:correlationFunctions}Correlation Functions} |