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# Line 27 | Line 27 | acceleration along the direction of the force acting o
27   \end{equation}
28   A point mass interacting with other bodies moves with the
29   acceleration along the direction of the force acting on it. Let
30 < $F_ij$ be the force that particle $i$ exerts on particle $j$, and
31 < $F_ji$ be the force that particle $j$ exerts on particle $i$.
30 > $F_{ij}$ be the force that particle $i$ exerts on particle $j$, and
31 > $F_{ji}$ be the force that particle $j$ exerts on particle $i$.
32   Newton¡¯s third law states that
33   \begin{equation}
34 < F_ij = -F_ji
34 > F_{ij} = -F_{ji}
35   \label{introEquation:newtonThirdLaw}
36   \end{equation}
37  
# Line 315 | Line 315 | partition function like,
315   isolated and conserve energy, Microcanonical ensemble(NVE) has a
316   partition function like,
317   \begin{equation}
318 < \Omega (N,V,E) = e^{\beta TS}
319 < \label{introEqaution:NVEPartition}.
318 > \Omega (N,V,E) = e^{\beta TS} \label{introEquation:NVEPartition}.
319   \end{equation}
320   A canonical ensemble(NVT)is an ensemble of systems, each of which
321   can share its energy with a large heat reservoir. The distribution
# Line 394 | Line 393 | distribution,
393   \begin{equation}
394   \rho  \propto e^{ - \beta H}
395   \label{introEquation:densityAndHamiltonian}
396 + \end{equation}
397 +
398 + \subsubsection{\label{introSection:phaseSpaceConservation}Conservation of Phase Space}
399 + Lets consider a region in the phase space,
400 + \begin{equation}
401 + \delta v = \int { \ldots \int {dq_1 } ...dq_f dp_1 } ..dp_f .
402 + \end{equation}
403 + If this region is small enough, the density $\rho$ can be regarded
404 + as uniform over the whole phase space. Thus, the number of phase
405 + points inside this region is given by,
406 + \begin{equation}
407 + \delta N = \rho \delta v = \rho \int { \ldots \int {dq_1 } ...dq_f
408 + dp_1 } ..dp_f.
409 + \end{equation}
410 +
411 + \begin{equation}
412 + \frac{{d(\delta N)}}{{dt}} = \frac{{d\rho }}{{dt}}\delta v + \rho
413 + \frac{d}{{dt}}(\delta v) = 0.
414 + \end{equation}
415 + With the help of stationary assumption
416 + (\ref{introEquation:stationary}), we obtain the principle of the
417 + \emph{conservation of extension in phase space},
418 + \begin{equation}
419 + \frac{d}{{dt}}(\delta v) = \frac{d}{{dt}}\int { \ldots \int {dq_1 }
420 + ...dq_f dp_1 } ..dp_f  = 0.
421 + \label{introEquation:volumePreserving}
422   \end{equation}
423  
424 + \subsubsection{\label{introSection:liouvilleInOtherForms}Liouville's Theorem in Other Forms}
425 +
426   Liouville's theorem can be expresses in a variety of different forms
427   which are convenient within different contexts. For any two function
428   $F$ and $G$ of the coordinates and momenta of a system, the Poisson
# Line 431 | Line 458 | expressed as
458   \label{introEquation:liouvilleTheoremInOperator}
459   \end{equation}
460  
434
461   \subsection{\label{introSection:ergodic}The Ergodic Hypothesis}
462  
463   Various thermodynamic properties can be calculated from Molecular
# Line 544 | Line 570 | The most obvious change being that matrix $J$ now depe
570   \dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian}
571   \end{equation}
572   The most obvious change being that matrix $J$ now depends on $x$.
547 The free rigid body is an example of Poisson system (actually a
548 Lie-Poisson system) with Hamiltonian function of angular kinetic
549 energy.
550 \begin{equation}
551 J(\pi ) = \left( {\begin{array}{*{20}c}
552   0 & {\pi _3 } & { - \pi _2 }  \\
553   { - \pi _3 } & 0 & {\pi _1 }  \\
554   {\pi _2 } & { - \pi _1 } & 0  \\
555 \end{array}} \right)
556 \end{equation}
573  
574 < \begin{equation}
559 < H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \frac{{\pi _2^2
560 < }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
561 < \end{equation}
574 > \subsection{\label{introSection:exactFlow}Exact Flow}
575  
563 \subsection{\label{introSection:geometricProperties}Geometric Properties}
576   Let $x(t)$ be the exact solution of the ODE system,
577   \begin{equation}
578   \frac{{dx}}{{dt}} = f(x) \label{introEquation:ODE}
# Line 570 | Line 582 | where $\tau$ is a fixed time step and $\varphi$ is a m
582   x(t+\tau) =\varphi_\tau(x(t))
583   \]
584   where $\tau$ is a fixed time step and $\varphi$ is a map from phase
585 < space to itself. In most cases, it is not easy to find the exact
574 < flow $\varphi_\tau$. Instead, we use a approximate map, $\psi_\tau$,
575 < which is usually called integrator. The order of an integrator
576 < $\psi_\tau$ is $p$, if the Taylor series of $\psi_\tau$ agree to
577 < order $p$,
585 > space to itself. The flow has the continuous group property,
586   \begin{equation}
587 + \varphi _{\tau _1 }  \circ \varphi _{\tau _2 }  = \varphi _{\tau _1
588 + + \tau _2 } .
589 + \end{equation}
590 + In particular,
591 + \begin{equation}
592 + \varphi _\tau   \circ \varphi _{ - \tau }  = I
593 + \end{equation}
594 + Therefore, the exact flow is self-adjoint,
595 + \begin{equation}
596 + \varphi _\tau   = \varphi _{ - \tau }^{ - 1}.
597 + \end{equation}
598 + The exact flow can also be written in terms of the of an operator,
599 + \begin{equation}
600 + \varphi _\tau  (x) = e^{\tau \sum\limits_i {f_i (x)\frac{\partial
601 + }{{\partial x_i }}} } (x) \equiv \exp (\tau f)(x).
602 + \label{introEquation:exponentialOperator}
603 + \end{equation}
604 +
605 + In most cases, it is not easy to find the exact flow $\varphi_\tau$.
606 + Instead, we use a approximate map, $\psi_\tau$, which is usually
607 + called integrator. The order of an integrator $\psi_\tau$ is $p$, if
608 + the Taylor series of $\psi_\tau$ agree to order $p$,
609 + \begin{equation}
610   \psi_tau(x) = x + \tau f(x) + O(\tau^{p+1})
611   \end{equation}
612  
613 + \subsection{\label{introSection:geometricProperties}Geometric Properties}
614 +
615   The hidden geometric properties of ODE and its flow play important
616 < roles in numerical studies. Let $\varphi$ be the flow of Hamiltonian
617 < vector field, $\varphi$ is a \emph{symplectic} flow if it satisfies,
616 > roles in numerical studies. Many of them can be found in systems
617 > which occur naturally in applications.
618 >
619 > Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is
620 > a \emph{symplectic} flow if it satisfies,
621   \begin{equation}
622 < '\varphi^T J '\varphi = J.
622 > {\varphi '}^T J \varphi ' = J.
623   \end{equation}
624   According to Liouville's theorem, the symplectic volume is invariant
625   under a Hamiltonian flow, which is the basis for classical
# Line 591 | Line 627 | symplectomorphism. As to the Poisson system,
627   field on a symplectic manifold can be shown to be a
628   symplectomorphism. As to the Poisson system,
629   \begin{equation}
630 < '\varphi ^T J '\varphi  = J \circ \varphi
630 > {\varphi '}^T J \varphi ' = J \circ \varphi
631   \end{equation}
632 < is the property must be preserved by the integrator. It is possible
633 < to construct a \emph{volume-preserving} flow for a source free($
634 < \nabla \cdot f = 0 $) ODE, if the flow satisfies $ \det d\varphi  =
635 < 1$. Changing the variables $y = h(x)$ in a
636 < ODE\ref{introEquation:ODE} will result in a new system,
632 > is the property must be preserved by the integrator.
633 >
634 > It is possible to construct a \emph{volume-preserving} flow for a
635 > source free($ \nabla \cdot f = 0 $) ODE, if the flow satisfies $
636 > \det d\varphi  = 1$. One can show easily that a symplectic flow will
637 > be volume-preserving.
638 >
639 > Changing the variables $y = h(x)$ in a ODE\ref{introEquation:ODE}
640 > will result in a new system,
641   \[
642   \dot y = \tilde f(y) = ((dh \cdot f)h^{ - 1} )(y).
643   \]
644   The vector filed $f$ has reversing symmetry $h$ if $f = - \tilde f$.
645   In other words, the flow of this vector field is reversible if and
646 < only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $. When
647 < designing any numerical methods, one should always try to preserve
648 < the structural properties of the original ODE and its flow.
646 > only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $.
647 >
648 > A \emph{first integral}, or conserved quantity of a general
649 > differential function is a function $ G:R^{2d}  \to R^d $ which is
650 > constant for all solutions of the ODE $\frac{{dx}}{{dt}} = f(x)$ ,
651 > \[
652 > \frac{{dG(x(t))}}{{dt}} = 0.
653 > \]
654 > Using chain rule, one may obtain,
655 > \[
656 > \sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \bullet \nabla G,
657 > \]
658 > which is the condition for conserving \emph{first integral}. For a
659 > canonical Hamiltonian system, the time evolution of an arbitrary
660 > smooth function $G$ is given by,
661 > \begin{equation}
662 > \begin{array}{c}
663 > \frac{{dG(x(t))}}{{dt}} = [\nabla _x G(x(t))]^T \dot x(t) \\
664 >  = [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\
665 > \end{array}
666 > \label{introEquation:firstIntegral1}
667 > \end{equation}
668 > Using poisson bracket notion, Equation
669 > \ref{introEquation:firstIntegral1} can be rewritten as
670 > \[
671 > \frac{d}{{dt}}G(x(t)) = \left\{ {G,H} \right\}(x(t)).
672 > \]
673 > Therefore, the sufficient condition for $G$ to be the \emph{first
674 > integral} of a Hamiltonian system is
675 > \[
676 > \left\{ {G,H} \right\} = 0.
677 > \]
678 > As well known, the Hamiltonian (or energy) H of a Hamiltonian system
679 > is a \emph{first integral}, which is due to the fact $\{ H,H\}  =
680 > 0$.
681  
682 +
683 + When designing any numerical methods, one should always try to
684 + preserve the structural properties of the original ODE and its flow.
685 +
686   \subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods}
687   A lot of well established and very effective numerical methods have
688   been successful precisely because of their symplecticities even
# Line 622 | Line 698 | Generating function tends to lead to methods which are
698   \end{enumerate}
699  
700   Generating function tends to lead to methods which are cumbersome
701 < and difficult to use\cite{}. In dissipative systems, variational
702 < methods can capture the decay of energy accurately\cite{}. Since
703 < their geometrically unstable nature against non-Hamiltonian
704 < perturbations, ordinary implicit Runge-Kutta methods are not
705 < suitable for Hamiltonian system. Recently, various high-order
706 < explicit Runge--Kutta methods have been developed to overcome this
707 < instability \cite{}. However, due to computational penalty involved
708 < in implementing the Runge-Kutta methods, they do not attract too
709 < much attention from Molecular Dynamics community. Instead, splitting
710 < have been widely accepted since they exploit natural decompositions
711 < of the system\cite{Tuckerman92}. The main idea behind splitting
712 < methods is to decompose the discrete $\varphi_h$ as a composition of
713 < simpler flows,
701 > and difficult to use. In dissipative systems, variational methods
702 > can capture the decay of energy accurately. Since their
703 > geometrically unstable nature against non-Hamiltonian perturbations,
704 > ordinary implicit Runge-Kutta methods are not suitable for
705 > Hamiltonian system. Recently, various high-order explicit
706 > Runge--Kutta methods have been developed to overcome this
707 > instability. However, due to computational penalty involved in
708 > implementing the Runge-Kutta methods, they do not attract too much
709 > attention from Molecular Dynamics community. Instead, splitting have
710 > been widely accepted since they exploit natural decompositions of
711 > the system\cite{Tuckerman92}.
712 >
713 > \subsubsection{\label{introSection:splittingMethod}Splitting Method}
714 >
715 > The main idea behind splitting methods is to decompose the discrete
716 > $\varphi_h$ as a composition of simpler flows,
717   \begin{equation}
718   \varphi _h  = \varphi _{h_1 }  \circ \varphi _{h_2 }  \ldots  \circ
719   \varphi _{h_n }
720   \label{introEquation:FlowDecomposition}
721   \end{equation}
722   where each of the sub-flow is chosen such that each represent a
723 < simpler integration of the system. Let $\phi$ and $\psi$ both be
724 < symplectic maps, it is easy to show that any composition of
725 < symplectic flows yields a symplectic map,
723 > simpler integration of the system.
724 >
725 > Suppose that a Hamiltonian system takes the form,
726 > \[
727 > H = H_1 + H_2.
728 > \]
729 > Here, $H_1$ and $H_2$ may represent different physical processes of
730 > the system. For instance, they may relate to kinetic and potential
731 > energy respectively, which is a natural decomposition of the
732 > problem. If $H_1$ and $H_2$ can be integrated using exact flows
733 > $\varphi_1(t)$ and $\varphi_2(t)$, respectively, a simple first
734 > order is then given by the Lie-Trotter formula
735   \begin{equation}
736 + \varphi _h  = \varphi _{1,h}  \circ \varphi _{2,h},
737 + \label{introEquation:firstOrderSplitting}
738 + \end{equation}
739 + where $\varphi _h$ is the result of applying the corresponding
740 + continuous $\varphi _i$ over a time $h$. By definition, as
741 + $\varphi_i(t)$ is the exact solution of a Hamiltonian system, it
742 + must follow that each operator $\varphi_i(t)$ is a symplectic map.
743 + It is easy to show that any composition of symplectic flows yields a
744 + symplectic map,
745 + \begin{equation}
746   (\varphi '\phi ')^T J\varphi '\phi ' = \phi '^T \varphi '^T J\varphi
747 < '\phi ' = \phi '^T J\phi ' = J.
747 > '\phi ' = \phi '^T J\phi ' = J,
748   \label{introEquation:SymplecticFlowComposition}
749   \end{equation}
750 < Suppose that a Hamiltonian system has a form with $H = T + V$
750 > where $\phi$ and $\psi$ both are symplectic maps. Thus operator
751 > splitting in this context automatically generates a symplectic map.
752 >
753 > The Lie-Trotter splitting(\ref{introEquation:firstOrderSplitting})
754 > introduces local errors proportional to $h^2$, while Strang
755 > splitting gives a second-order decomposition,
756 > \begin{equation}
757 > \varphi _h  = \varphi _{1,h/2}  \circ \varphi _{2,h}  \circ \varphi
758 > _{1,h/2} , \label{introEquation:secondOrderSplitting}
759 > \end{equation}
760 > which has a local error proportional to $h^3$. Sprang splitting's
761 > popularity in molecular simulation community attribute to its
762 > symmetric property,
763 > \begin{equation}
764 > \varphi _h^{ - 1} = \varphi _{ - h}.
765 > \label{introEquation:timeReversible}
766 > \end{equation}
767 >
768 > \subsubsection{\label{introSection:exampleSplittingMethod}Example of Splitting Method}
769 > The classical equation for a system consisting of interacting
770 > particles can be written in Hamiltonian form,
771 > \[
772 > H = T + V
773 > \]
774 > where $T$ is the kinetic energy and $V$ is the potential energy.
775 > Setting $H_1 = T, H_2 = V$ and applying Strang splitting, one
776 > obtains the following:
777 > \begin{align}
778 > q(\Delta t) &= q(0) + \dot{q}(0)\Delta t +
779 >    \frac{F[q(0)]}{m}\frac{\Delta t^2}{2}, %
780 > \label{introEquation:Lp10a} \\%
781 > %
782 > \dot{q}(\Delta t) &= \dot{q}(0) + \frac{\Delta t}{2m}
783 >    \biggl [F[q(0)] + F[q(\Delta t)] \biggr]. %
784 > \label{introEquation:Lp10b}
785 > \end{align}
786 > where $F(t)$ is the force at time $t$. This integration scheme is
787 > known as \emph{velocity verlet} which is
788 > symplectic(\ref{introEquation:SymplecticFlowComposition}),
789 > time-reversible(\ref{introEquation:timeReversible}) and
790 > volume-preserving (\ref{introEquation:volumePreserving}). These
791 > geometric properties attribute to its long-time stability and its
792 > popularity in the community. However, the most commonly used
793 > velocity verlet integration scheme is written as below,
794 > \begin{align}
795 > \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) &=
796 >    \dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)], \label{introEquation:Lp9a}\\%
797 > %
798 > q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr ),%
799 >    \label{introEquation:Lp9b}\\%
800 > %
801 > \dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) +
802 >    \frac{\Delta t}{2m}\, F[q(0)]. \label{introEquation:Lp9c}
803 > \end{align}
804 > From the preceding splitting, one can see that the integration of
805 > the equations of motion would follow:
806 > \begin{enumerate}
807 > \item calculate the velocities at the half step, $\frac{\Delta t}{2}$, from the forces calculated at the initial position.
808  
809 + \item Use the half step velocities to move positions one whole step, $\Delta t$.
810 +
811 + \item Evaluate the forces at the new positions, $\mathbf{r}(\Delta t)$, and use the new forces to complete the velocity move.
812 +
813 + \item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values.
814 + \end{enumerate}
815 +
816 + Simply switching the order of splitting and composing, a new
817 + integrator, the \emph{position verlet} integrator, can be generated,
818 + \begin{align}
819 + \dot q(\Delta t) &= \dot q(0) + \Delta tF(q(0))\left[ {q(0) +
820 + \frac{{\Delta t}}{{2m}}\dot q(0)} \right], %
821 + \label{introEquation:positionVerlet1} \\%
822 + %
823 + q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
824 + q(\Delta t)} \right]. %
825 + \label{introEquation:positionVerlet1}
826 + \end{align}
827 +
828 + \subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods}
829 +
830 + Baker-Campbell-Hausdorff formula can be used to determine the local
831 + error of splitting method in terms of commutator of the
832 + operators(\ref{introEquation:exponentialOperator}) associated with
833 + the sub-flow. For operators $hX$ and $hY$ which are associate to
834 + $\varphi_1(t)$ and $\varphi_2(t$ respectively , we have
835 + \begin{equation}
836 + \exp (hX + hY) = \exp (hZ)
837 + \end{equation}
838 + where
839 + \begin{equation}
840 + hZ = hX + hY + \frac{{h^2 }}{2}[X,Y] + \frac{{h^3 }}{2}\left(
841 + {[X,[X,Y]] + [Y,[Y,X]]} \right) +  \ldots .
842 + \end{equation}
843 + Here, $[X,Y]$ is the commutators of operator $X$ and $Y$ given by
844 + \[
845 + [X,Y] = XY - YX .
846 + \]
847 + Applying Baker-Campbell-Hausdorff formula to Sprang splitting, we
848 + can obtain
849 + \begin{eqnarray*}
850 + \exp (h X/2)\exp (h Y)\exp (h X/2) & = & \exp (h X + h Y + h^2
851 + [X,Y]/4 + h^2 [Y,X]/4 \\ & & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\
852 + & & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3 [X,[X,Y]]/24 & & \mbox{} +
853 + \ldots )
854 + \end{eqnarray*}
855 + Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0\], the dominant local
856 + error of Spring splitting is proportional to $h^3$. The same
857 + procedure can be applied to general splitting,  of the form
858 + \begin{equation}
859 + \varphi _{b_m h}^2  \circ \varphi _{a_m h}^1  \circ \varphi _{b_{m -
860 + 1} h}^2  \circ  \ldots  \circ \varphi _{a_1 h}^1 .
861 + \end{equation}
862 + Careful choice of coefficient $a_1 ,\ldot , b_m$ will lead to higher
863 + order method. Yoshida proposed an elegant way to compose higher
864 + order methods based on symmetric splitting. Given a symmetric second
865 + order base method $ \varphi _h^{(2)} $, a fourth-order symmetric
866 + method can be constructed by composing,
867 + \[
868 + \varphi _h^{(4)}  = \varphi _{\alpha h}^{(2)}  \circ \varphi _{\beta
869 + h}^{(2)}  \circ \varphi _{\alpha h}^{(2)}
870 + \]
871 + where $ \alpha  =  - \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$ and $ \beta
872 + = \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$. Moreover, a symmetric
873 + integrator $ \varphi _h^{(2n + 2)}$ can be composed by
874 + \begin{equation}
875 + \varphi _h^{(2n + 2)}  = \varphi _{\alpha h}^{(2n)}  \circ \varphi
876 + _{\beta h}^{(2n)}  \circ \varphi _{\alpha h}^{(2n)}
877 + \end{equation}
878 + , if the weights are chosen as
879 + \[
880 + \alpha  =  - \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }},\beta =
881 + \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }} .
882 + \]
883 +
884   \section{\label{introSection:molecularDynamics}Molecular Dynamics}
885  
886   As a special discipline of molecular modeling, Molecular dynamics
# Line 660 | Line 890 | dynamical information.
890  
891   \subsection{\label{introSec:mdInit}Initialization}
892  
893 + \subsection{\label{introSec:forceEvaluation}Force Evaluation}
894 +
895   \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
896  
897   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
898  
899 < A rigid body is a body in which the distance between any two given
900 < points of a rigid body remains constant regardless of external
901 < forces exerted on it. A rigid body therefore conserves its shape
902 < during its motion.
899 > Rigid bodies are frequently involved in the modeling of different
900 > areas, from engineering, physics, to chemistry. For example,
901 > missiles and vehicle are usually modeled by rigid bodies.  The
902 > movement of the objects in 3D gaming engine or other physics
903 > simulator is governed by the rigid body dynamics. In molecular
904 > simulation, rigid body is used to simplify the model in
905 > protein-protein docking study{\cite{Gray03}}.
906  
907 < Applications of dynamics of rigid bodies.
907 > It is very important to develop stable and efficient methods to
908 > integrate the equations of motion of orientational degrees of
909 > freedom. Euler angles are the nature choice to describe the
910 > rotational degrees of freedom. However, due to its singularity, the
911 > numerical integration of corresponding equations of motion is very
912 > inefficient and inaccurate. Although an alternative integrator using
913 > different sets of Euler angles can overcome this difficulty\cite{},
914 > the computational penalty and the lost of angular momentum
915 > conservation still remain. A singularity free representation
916 > utilizing quaternions was developed by Evans in 1977. Unfortunately,
917 > this approach suffer from the nonseparable Hamiltonian resulted from
918 > quaternion representation, which prevents the symplectic algorithm
919 > to be utilized. Another different approach is to apply holonomic
920 > constraints to the atoms belonging to the rigid body. Each atom
921 > moves independently under the normal forces deriving from potential
922 > energy and constraint forces which are used to guarantee the
923 > rigidness. However, due to their iterative nature, SHAKE and Rattle
924 > algorithm converge very slowly when the number of constraint
925 > increases.
926  
927 < \subsection{\label{introSection:lieAlgebra}Lie Algebra}
927 > The break through in geometric literature suggests that, in order to
928 > develop a long-term integration scheme, one should preserve the
929 > symplectic structure of the flow. Introducing conjugate momentum to
930 > rotation matrix $A$ and re-formulating Hamiltonian's equation, a
931 > symplectic integrator, RSHAKE, was proposed to evolve the
932 > Hamiltonian system in a constraint manifold by iteratively
933 > satisfying the orthogonality constraint $A_t A = 1$. An alternative
934 > method using quaternion representation was developed by Omelyan.
935 > However, both of these methods are iterative and inefficient. In
936 > this section, we will present a symplectic Lie-Poisson integrator
937 > for rigid body developed by Dullweber and his
938 > coworkers\cite{Dullweber1997} in depth.
939  
940 < \subsection{\label{introSection:DLMMotionEquation}The Euler Equations of Rigid Body Motion}
940 > \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body}
941 > The motion of the rigid body is Hamiltonian with the Hamiltonian
942 > function
943 > \begin{equation}
944 > H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
945 > V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
946 > \label{introEquation:RBHamiltonian}
947 > \end{equation}
948 > Here, $q$ and $Q$  are the position and rotation matrix for the
949 > rigid-body, $p$ and $P$  are conjugate momenta to $q$  and $Q$ , and
950 > $J$, a diagonal matrix, is defined by
951 > \[
952 > I_{ii}^{ - 1}  = \frac{1}{2}\sum\limits_{i \ne j} {J_{jj}^{ - 1} }
953 > \]
954 > where $I_{ii}$ is the diagonal element of the inertia tensor. This
955 > constrained Hamiltonian equation subjects to a holonomic constraint,
956 > \begin{equation}
957 > Q^T Q = 1$, \label{introEquation:orthogonalConstraint}
958 > \end{equation}
959 > which is used to ensure rotation matrix's orthogonality.
960 > Differentiating \ref{introEquation:orthogonalConstraint} and using
961 > Equation \ref{introEquation:RBMotionMomentum}, one may obtain,
962 > \begin{equation}
963 > Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0 . \\
964 > \label{introEquation:RBFirstOrderConstraint}
965 > \end{equation}
966  
967 < \subsection{\label{introSection:otherRBMotionEquation}Other Formulations for Rigid Body Motion}
967 > Using Equation (\ref{introEquation:motionHamiltonianCoordinate},
968 > \ref{introEquation:motionHamiltonianMomentum}), one can write down
969 > the equations of motion,
970 > \[
971 > \begin{array}{c}
972 > \frac{{dq}}{{dt}} = \frac{p}{m} \label{introEquation:RBMotionPosition}\\
973 > \frac{{dp}}{{dt}} =  - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\
974 > \frac{{dQ}}{{dt}} = PJ^{ - 1}  \label{introEquation:RBMotionRotation}\\
975 > \frac{{dP}}{{dt}} =  - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\
976 > \end{array}
977 > \]
978  
979 < %\subsection{\label{introSection:poissonBrackets}Poisson Brackets}
979 > In general, there are two ways to satisfy the holonomic constraints.
980 > We can use constraint force provided by lagrange multiplier on the
981 > normal manifold to keep the motion on constraint space. Or we can
982 > simply evolve the system in constraint manifold. The two method are
983 > proved to be equivalent. The holonomic constraint and equations of
984 > motions define a constraint manifold for rigid body
985 > \[
986 > M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0}
987 > \right\}.
988 > \]
989  
990 < \section{\label{introSection:correlationFunctions}Correlation Functions}
990 > Unfortunately, this constraint manifold is not the cotangent bundle
991 > $T_{\star}SO(3)$. However, it turns out that under symplectic
992 > transformation, the cotangent space and the phase space are
993 > diffeomorphic. Introducing
994 > \[
995 > \tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right),
996 > \]
997 > the mechanical system subject to a holonomic constraint manifold $M$
998 > can be re-formulated as a Hamiltonian system on the cotangent space
999 > \[
1000 > T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q =
1001 > 1,\tilde Q^T \tilde PJ^{ - 1}  + J^{ - 1} P^T \tilde Q = 0} \right\}
1002 > \]
1003  
1004 < \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1004 > For a body fixed vector $X_i$ with respect to the center of mass of
1005 > the rigid body, its corresponding lab fixed vector $X_0^{lab}$  is
1006 > given as
1007 > \begin{equation}
1008 > X_i^{lab} = Q X_i + q.
1009 > \end{equation}
1010 > Therefore, potential energy $V(q,Q)$ is defined by
1011 > \[
1012 > V(q,Q) = V(Q X_0 + q).
1013 > \]
1014 > Hence, the force and torque are given by
1015 > \[
1016 > \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)},
1017 > \]
1018 > and
1019 > \[
1020 > \nabla _Q V(q,Q) = F(q,Q)X_i^t
1021 > \]
1022 > respectively.
1023  
1024 < \subsection{\label{introSection:LDIntroduction}Introduction and application of Langevin Dynamics}
1024 > As a common choice to describe the rotation dynamics of the rigid
1025 > body, angular momentum on body frame $\Pi  = Q^t P$ is introduced to
1026 > rewrite the equations of motion,
1027 > \begin{equation}
1028 > \begin{array}{l}
1029 > \mathop \Pi \limits^ \bullet   = J^{ - 1} \Pi ^T \Pi  + Q^T \sum\limits_i {F_i (q,Q)X_i^T }  - \Lambda  \\
1030 > \mathop Q\limits^{{\rm{   }} \bullet }  = Q\Pi {\rm{ }}J^{ - 1}  \\
1031 > \end{array}
1032 > \label{introEqaution:RBMotionPI}
1033 > \end{equation}
1034 > , as well as holonomic constraints,
1035 > \[
1036 > \begin{array}{l}
1037 > \Pi J^{ - 1}  + J^{ - 1} \Pi ^t  = 0 \\
1038 > Q^T Q = 1 \\
1039 > \end{array}
1040 > \]
1041 >
1042 > For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in
1043 > so(3)^ \star$, the hat-map isomorphism,
1044 > \begin{equation}
1045 > v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left(
1046 > {\begin{array}{*{20}c}
1047 >   0 & { - v_3 } & {v_2 }  \\
1048 >   {v_3 } & 0 & { - v_1 }  \\
1049 >   { - v_2 } & {v_1 } & 0  \\
1050 > \end{array}} \right),
1051 > \label{introEquation:hatmapIsomorphism}
1052 > \end{equation}
1053 > will let us associate the matrix products with traditional vector
1054 > operations
1055 > \[
1056 > \hat vu = v \times u
1057 > \]
1058 >
1059 > Using \ref{introEqaution:RBMotionPI}, one can construct a skew
1060 > matrix,
1061 > \begin{equation}
1062 > (\mathop \Pi \limits^ \bullet   - \mathop \Pi \limits^ \bullet  ^T
1063 > ){\rm{ }} = {\rm{ }}(\Pi  - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi  + \Pi J^{
1064 > - 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T  - X_i F_i (r,Q)^T Q]} -
1065 > (\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
1066 > \end{equation}
1067 > Since $\Lambda$ is symmetric, the last term of Equation
1068 > \ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange
1069 > multiplier $\Lambda$ is absent from the equations of motion. This
1070 > unique property eliminate the requirement of iterations which can
1071 > not be avoided in other methods\cite{}.
1072 >
1073 > Applying hat-map isomorphism, we obtain the equation of motion for
1074 > angular momentum on body frame
1075 > \begin{equation}
1076 > \dot \pi  = \pi  \times I^{ - 1} \pi  + \sum\limits_i {\left( {Q^T
1077 > F_i (r,Q)} \right) \times X_i }.
1078 > \label{introEquation:bodyAngularMotion}
1079 > \end{equation}
1080 > In the same manner, the equation of motion for rotation matrix is
1081 > given by
1082 > \[
1083 > \dot Q = Qskew(I^{ - 1} \pi )
1084 > \]
1085 >
1086 > \subsection{\label{introSection:SymplecticFreeRB}Symplectic
1087 > Lie-Poisson Integrator for Free Rigid Body}
1088 >
1089 > If there is not external forces exerted on the rigid body, the only
1090 > contribution to the rotational is from the kinetic potential (the
1091 > first term of \ref{ introEquation:bodyAngularMotion}). The free
1092 > rigid body is an example of Lie-Poisson system with Hamiltonian
1093 > function
1094 > \begin{equation}
1095 > T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 )
1096 > \label{introEquation:rotationalKineticRB}
1097 > \end{equation}
1098 > where $T_i^r (\pi _i ) = \frac{{\pi _i ^2 }}{{2I_i }}$ and
1099 > Lie-Poisson structure matrix,
1100 > \begin{equation}
1101 > J(\pi ) = \left( {\begin{array}{*{20}c}
1102 >   0 & {\pi _3 } & { - \pi _2 }  \\
1103 >   { - \pi _3 } & 0 & {\pi _1 }  \\
1104 >   {\pi _2 } & { - \pi _1 } & 0  \\
1105 > \end{array}} \right)
1106 > \end{equation}
1107 > Thus, the dynamics of free rigid body is governed by
1108 > \begin{equation}
1109 > \frac{d}{{dt}}\pi  = J(\pi )\nabla _\pi  T^r (\pi )
1110 > \end{equation}
1111 >
1112 > One may notice that each $T_i^r$ in Equation
1113 > \ref{introEquation:rotationalKineticRB} can be solved exactly. For
1114 > instance, the equations of motion due to $T_1^r$ are given by
1115 > \begin{equation}
1116 > \frac{d}{{dt}}\pi  = R_1 \pi ,\frac{d}{{dt}}Q = QR_1
1117 > \label{introEqaution:RBMotionSingleTerm}
1118 > \end{equation}
1119 > where
1120 > \[ R_1  = \left( {\begin{array}{*{20}c}
1121 >   0 & 0 & 0  \\
1122 >   0 & 0 & {\pi _1 }  \\
1123 >   0 & { - \pi _1 } & 0  \\
1124 > \end{array}} \right).
1125 > \]
1126 > The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is
1127 > \[
1128 > \pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) =
1129 > Q(0)e^{\Delta tR_1 }
1130 > \]
1131 > with
1132 > \[
1133 > e^{\Delta tR_1 }  = \left( {\begin{array}{*{20}c}
1134 >   0 & 0 & 0  \\
1135 >   0 & {\cos \theta _1 } & {\sin \theta _1 }  \\
1136 >   0 & { - \sin \theta _1 } & {\cos \theta _1 }  \\
1137 > \end{array}} \right),\theta _1  = \frac{{\pi _1 }}{{I_1 }}\Delta t.
1138 > \]
1139 > To reduce the cost of computing expensive functions in e^{\Delta
1140 > tR_1 }, we can use Cayley transformation,
1141 > \[
1142 > e^{\Delta tR_1 }  \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1
1143 > )
1144 > \]
1145 >
1146 > The flow maps for $T_2^r$ and $T_2^r$ can be found in the same
1147 > manner.
1148 >
1149 > In order to construct a second-order symplectic method, we split the
1150 > angular kinetic Hamiltonian function can into five terms
1151 > \[
1152 > T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2
1153 > ) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r
1154 > (\pi _1 )
1155 > \].
1156 > Concatenating flows corresponding to these five terms, we can obtain
1157 > an symplectic integrator,
1158 > \[
1159 > \varphi _{\Delta t,T^r }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1160 > \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
1161 > \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1162 > _1 }.
1163 > \]
1164 >
1165 > The non-canonical Lie-Poisson bracket ${F, G}$ of two function
1166 > $F(\pi )$ and $G(\pi )$ is defined by
1167 > \[
1168 > \{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
1169 > )
1170 > \]
1171 > If the Poisson bracket of a function $F$ with an arbitrary smooth
1172 > function $G$ is zero, $F$ is a \emph{Casimir}, which is the
1173 > conserved quantity in Poisson system. We can easily verify that the
1174 > norm of the angular momentum, $\parallel \pi
1175 > \parallel$, is a \emph{Casimir}. Let$ F(\pi ) = S(\frac{{\parallel
1176 > \pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
1177 > then by the chain rule
1178 > \[
1179 > \nabla _\pi  F(\pi ) = S'(\frac{{\parallel \pi \parallel ^2
1180 > }}{2})\pi
1181 > \]
1182 > Thus $ [\nabla _\pi  F(\pi )]^T J(\pi ) =  - S'(\frac{{\parallel \pi
1183 > \parallel ^2 }}{2})\pi  \times \pi  = 0 $. This explicit
1184 > Lie-Poisson integrator is found to be extremely efficient and stable
1185 > which can be explained by the fact the small angle approximation is
1186 > used and the norm of the angular momentum is conserved.
1187 >
1188 > \subsection{\label{introSection:RBHamiltonianSplitting} Hamiltonian
1189 > Splitting for Rigid Body}
1190 >
1191 > The Hamiltonian of rigid body can be separated in terms of kinetic
1192 > energy and potential energy,
1193 > \[
1194 > H = T(p,\pi ) + V(q,Q)
1195 > \]
1196 > The equations of motion corresponding to potential energy and
1197 > kinetic energy are listed in the below table,
1198 > \begin{center}
1199 > \begin{tabular}{|l|l|}
1200 >  \hline
1201 >  % after \\: \hline or \cline{col1-col2} \cline{col3-col4} ...
1202 >  Potential & Kinetic \\
1203 >  $\frac{{dq}}{{dt}} = \frac{p}{m}$ & $\frac{d}{{dt}}q = p$ \\
1204 >  $\frac{d}{{dt}}p =  - \frac{{\partial V}}{{\partial q}}$ & $ \frac{d}{{dt}}p = 0$ \\
1205 >  $\frac{d}{{dt}}Q = 0$ & $ \frac{d}{{dt}}Q = Qskew(I^{ - 1} j)$ \\
1206 >  $ \frac{d}{{dt}}\pi  = \sum\limits_i {\left( {Q^T F_i (r,Q)} \right) \times X_i }$ & $\frac{d}{{dt}}\pi  = \pi  \times I^{ - 1} \pi$\\
1207 >  \hline
1208 > \end{tabular}
1209 > \end{center}
1210 > A second-order symplectic method is now obtained by the composition
1211 > of the flow maps,
1212 > \[
1213 > \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1214 > _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}.
1215 > \]
1216 > Moreover, \varphi _{\Delta t/2,V} can be divided into two sub-flows
1217 > which corresponding to force and torque respectively,
1218 > \[
1219 > \varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
1220 > _{\Delta t/2,\tau }.
1221 > \]
1222 > Since the associated operators of $\varphi _{\Delta t/2,F} $ and
1223 > $\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition
1224 > order inside \varphi _{\Delta t/2,V} does not matter.
1225 >
1226 > Furthermore, kinetic potential can be separated to translational
1227 > kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$,
1228 > \begin{equation}
1229 > T(p,\pi ) =T^t (p) + T^r (\pi ).
1230 > \end{equation}
1231 > where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is
1232 > defined by \ref{introEquation:rotationalKineticRB}. Therefore, the
1233 > corresponding flow maps are given by
1234 > \[
1235 > \varphi _{\Delta t,T}  = \varphi _{\Delta t,T^t }  \circ \varphi
1236 > _{\Delta t,T^r }.
1237 > \]
1238 > Finally, we obtain the overall symplectic flow maps for free moving
1239 > rigid body
1240 > \begin{equation}
1241 > \begin{array}{c}
1242 > \varphi _{\Delta t}  = \varphi _{\Delta t/2,F}  \circ \varphi _{\Delta t/2,\tau }  \\
1243 >  \circ \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 }  \\
1244 >  \circ \varphi _{\Delta t/2,\tau }  \circ \varphi _{\Delta t/2,F}  .\\
1245 > \end{array}
1246 > \label{introEquation:overallRBFlowMaps}
1247 > \end{equation}
1248 >
1249 > \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1250 > As an alternative to newtonian dynamics, Langevin dynamics, which
1251 > mimics a simple heat bath with stochastic and dissipative forces,
1252 > has been applied in a variety of studies. This section will review
1253 > the theory of Langevin dynamics simulation. A brief derivation of
1254 > generalized Langevin Dynamics will be given first. Follow that, we
1255 > will discuss the physical meaning of the terms appearing in the
1256 > equation as well as the calculation of friction tensor from
1257 > hydrodynamics theory.
1258  
1259   \subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics}
1260  
# Line 729 | Line 1300 | introEquation:motionHamiltonianMomentum},
1300   \dot p &=  - \frac{{\partial H}}{{\partial x}}
1301         &= m\ddot x
1302         &= - \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)}
1303 < \label{introEq:Lp5}
1303 > \label{introEquation:Lp5}
1304   \end{align}
1305   , and
1306   \begin{align}
# Line 877 | Line 1448 | And since the $q$ coordinates are harmonic oscillators
1448   \label{introEquation:secondFluctuationDissipation}
1449   \end{equation}
1450  
880 \section{\label{introSection:hydroynamics}Hydrodynamics}
881
1451   \subsection{\label{introSection:frictionTensor} Friction Tensor}
1452 < \subsection{\label{introSection:analyticalApproach}Analytical
1453 < Approach}
1452 > Theoretically, the friction kernel can be determined using velocity
1453 > autocorrelation function. However, this approach become impractical
1454 > when the system become more and more complicate. Instead, various
1455 > approaches based on hydrodynamics have been developed to calculate
1456 > the friction coefficients. The friction effect is isotropic in
1457 > Equation, \zeta can be taken as a scalar. In general, friction
1458 > tensor \Xi is a $6\times 6$ matrix given by
1459 > \[
1460 > \Xi  = \left( {\begin{array}{*{20}c}
1461 >   {\Xi _{}^{tt} } & {\Xi _{}^{rt} }  \\
1462 >   {\Xi _{}^{tr} } & {\Xi _{}^{rr} }  \\
1463 > \end{array}} \right).
1464 > \]
1465 > Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction
1466 > tensor and rotational friction tensor respectively, while ${\Xi^{tr}
1467 > }$ is translation-rotation coupling tensor and $ {\Xi^{rt} }$ is
1468 > rotation-translation coupling tensor.
1469  
1470 < \subsection{\label{introSection:approximationApproach}Approximation
1471 < Approach}
1470 > \[
1471 > \left( \begin{array}{l}
1472 > F_t  \\
1473 > \tau  \\
1474 > \end{array} \right) =  - \left( {\begin{array}{*{20}c}
1475 >   {\Xi ^{tt} } & {\Xi ^{rt} }  \\
1476 >   {\Xi ^{tr} } & {\Xi ^{rr} }  \\
1477 > \end{array}} \right)\left( \begin{array}{l}
1478 > v \\
1479 > w \\
1480 > \end{array} \right)
1481 > \]
1482  
1483 < \subsection{\label{introSection:centersRigidBody}Centers of Rigid
1484 < Body}
1483 > \subsubsection{\label{introSection:analyticalApproach}The Friction Tensor for Regular Shape}
1484 > For a spherical particle, the translational and rotational friction
1485 > constant can be calculated from Stoke's law,
1486 > \[
1487 > \Xi ^{tt}  = \left( {\begin{array}{*{20}c}
1488 >   {6\pi \eta R} & 0 & 0  \\
1489 >   0 & {6\pi \eta R} & 0  \\
1490 >   0 & 0 & {6\pi \eta R}  \\
1491 > \end{array}} \right)
1492 > \]
1493 > and
1494 > \[
1495 > \Xi ^{rr}  = \left( {\begin{array}{*{20}c}
1496 >   {8\pi \eta R^3 } & 0 & 0  \\
1497 >   0 & {8\pi \eta R^3 } & 0  \\
1498 >   0 & 0 & {8\pi \eta R^3 }  \\
1499 > \end{array}} \right)
1500 > \]
1501 > where $\eta$ is the viscosity of the solvent and $R$ is the
1502 > hydrodynamics radius.
1503 >
1504 > Other non-spherical particles have more complex properties.
1505 >
1506 > \[
1507 > S = \frac{2}{{\sqrt {a^2  - b^2 } }}\ln \frac{{a + \sqrt {a^2  - b^2
1508 > } }}{b}
1509 > \]
1510 >
1511 >
1512 > \[
1513 > S = \frac{2}{{\sqrt {b^2  - a^2 } }}arctg\frac{{\sqrt {b^2  - a^2 }
1514 > }}{a}
1515 > \]
1516 >
1517 > \[
1518 > \begin{array}{l}
1519 > \Xi _a^{tt}  = 16\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - b^2 )S - 2a}} \\
1520 > \Xi _b^{tt}  = \Xi _c^{tt}  = 32\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - 3b^2 )S + 2a}} \\
1521 > \end{array}
1522 > \]
1523 >
1524 > \[
1525 > \begin{array}{l}
1526 > \Xi _a^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^2  - b^2 )b^2 }}{{2a - b^2 S}} \\
1527 > \Xi _b^{rr}  = \Xi _c^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^4  - b^4 )}}{{(2a^2  - b^2 )S - 2a}} \\
1528 > \end{array}
1529 > \]
1530 >
1531 >
1532 > \subsubsection{\label{introSection:approximationApproach}The Friction Tensor for Arbitrary Shape}
1533 > Unlike spherical and other regular shaped molecules, there is not
1534 > analytical solution for friction tensor of any arbitrary shaped
1535 > rigid molecules. The ellipsoid of revolution model and general
1536 > triaxial ellipsoid model have been used to approximate the
1537 > hydrodynamic properties of rigid bodies. However, since the mapping
1538 > from all possible ellipsoidal space, $r$-space, to all possible
1539 > combination of rotational diffusion coefficients, $D$-space is not
1540 > unique\cite{Wegener79} as well as the intrinsic coupling between
1541 > translational and rotational motion of rigid body\cite{}, general
1542 > ellipsoid is not always suitable for modeling arbitrarily shaped
1543 > rigid molecule. A number of studies have been devoted to determine
1544 > the friction tensor for irregularly shaped rigid bodies using more
1545 > advanced method\cite{} where the molecule of interest was modeled by
1546 > combinations of spheres(beads)\cite{} and the hydrodynamics
1547 > properties of the molecule can be calculated using the hydrodynamic
1548 > interaction tensor. Let us consider a rigid assembly of $N$ beads
1549 > immersed in a continuous medium. Due to hydrodynamics interaction,
1550 > the ``net'' velocity of $i$th bead, $v'_i$ is different than its
1551 > unperturbed velocity $v_i$,
1552 > \[
1553 > v'_i  = v_i  - \sum\limits_{j \ne i} {T_{ij} F_j }
1554 > \]
1555 > where $F_i$ is the frictional force, and $T_{ij}$ is the
1556 > hydrodynamic interaction tensor. The friction force of $i$th bead is
1557 > proportional to its ``net'' velocity
1558 > \begin{equation}
1559 > F_i  = \zeta _i v_i  - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }.
1560 > \label{introEquation:tensorExpression}
1561 > \end{equation}
1562 > This equation is the basis for deriving the hydrodynamic tensor. In
1563 > 1930, Oseen and Burgers gave a simple solution to Equation
1564 > \ref{introEquation:tensorExpression}
1565 > \begin{equation}
1566 > T_{ij}  = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij}
1567 > R_{ij}^T }}{{R_{ij}^2 }}} \right).
1568 > \label{introEquation:oseenTensor}
1569 > \end{equation}
1570 > Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$.
1571 > A second order expression for element of different size was
1572 > introduced by Rotne and Prager\cite{} and improved by Garc\'{i}a de
1573 > la Torre and Bloomfield,
1574 > \begin{equation}
1575 > T_{ij}  = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I +
1576 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma
1577 > _i^2  + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} -
1578 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right].
1579 > \label{introEquation:RPTensorNonOverlapped}
1580 > \end{equation}
1581 > Both of the Equation \ref{introEquation:oseenTensor} and Equation
1582 > \ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij}
1583 > \ge \sigma _i  + \sigma _j$. An alternative expression for
1584 > overlapping beads with the same radius, $\sigma$, is given by
1585 > \begin{equation}
1586 > T_{ij}  = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 -
1587 > \frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I +
1588 > \frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right]
1589 > \label{introEquation:RPTensorOverlapped}
1590 > \end{equation}
1591 >
1592 > %Bead Modeling
1593 >
1594 > \[
1595 > B = \left( {\begin{array}{*{20}c}
1596 >   {T_{11} } &  \ldots  & {T_{1N} }  \\
1597 >    \vdots  &  \ddots  &  \vdots   \\
1598 >   {T_{N1} } &  \cdots  & {T_{NN} }  \\
1599 > \end{array}} \right)
1600 > \]
1601 >
1602 > \[
1603 > C = B^{ - 1}  = \left( {\begin{array}{*{20}c}
1604 >   {C_{11} } &  \ldots  & {C_{1N} }  \\
1605 >    \vdots  &  \ddots  &  \vdots   \\
1606 >   {C_{N1} } &  \cdots  & {C_{NN} }  \\
1607 > \end{array}} \right)
1608 > \]
1609 >
1610 > \begin{equation}
1611 > \begin{array}{l}
1612 > \Xi _{}^{tt}  = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\
1613 > \Xi _{}^{tr}  = \Xi _{}^{rt}  = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\
1614 > \Xi _{}^{rr}  =  - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j  \\
1615 > \end{array}
1616 > \end{equation}
1617 > where
1618 > \[
1619 > U_i  = \left( {\begin{array}{*{20}c}
1620 >   0 & { - z_i } & {y_i }  \\
1621 >   {z_i } & 0 & { - x_i }  \\
1622 >   { - y_i } & {x_i } & 0  \\
1623 > \end{array}} \right)
1624 > \]
1625 >
1626 > \[
1627 > r_{OR}  = \left( \begin{array}{l}
1628 > x_{OR}  \\
1629 > y_{OR}  \\
1630 > z_{OR}  \\
1631 > \end{array} \right) = \left( {\begin{array}{*{20}c}
1632 >   {\Xi _{yy}^{rr}  + \Xi _{zz}^{rr} } & { - \Xi _{xy}^{rr} } & { - \Xi _{xz}^{rr} }  \\
1633 >   { - \Xi _{yx}^{rr} } & {\Xi _{zz}^{rr}  + \Xi _{xx}^{rr} } & { - \Xi _{yz}^{rr} }  \\
1634 >   { - \Xi _{zx}^{rr} } & { - \Xi _{yz}^{rr} } & {\Xi _{xx}^{rr}  + \Xi _{yy}^{rr} }  \\
1635 > \end{array}} \right)^{ - 1} \left( \begin{array}{l}
1636 > \Xi _{yz}^{tr}  - \Xi _{zy}^{tr}  \\
1637 > \Xi _{zx}^{tr}  - \Xi _{xz}^{tr}  \\
1638 > \Xi _{xy}^{tr}  - \Xi _{yx}^{tr}  \\
1639 > \end{array} \right)
1640 > \]
1641 >
1642 > \[
1643 > U_{OR}  = \left( {\begin{array}{*{20}c}
1644 >   0 & { - z_{OR} } & {y_{OR} }  \\
1645 >   {z_i } & 0 & { - x_{OR} }  \\
1646 >   { - y_{OR} } & {x_{OR} } & 0  \\
1647 > \end{array}} \right)
1648 > \]
1649 >
1650 > \[
1651 > \begin{array}{l}
1652 > \Xi _R^{tt}  = \Xi _{}^{tt}  \\
1653 > \Xi _R^{tr}  = \Xi _R^{rt}  = \Xi _{}^{tr}  - U_{OR} \Xi _{}^{tt}  \\
1654 > \Xi _R^{rr}  = \Xi _{}^{rr}  - U_{OR} \Xi _{}^{tt} U_{OR}  + \Xi _{}^{tr} U_{OR}  - U_{OR} \Xi _{}^{tr} ^{^T }  \\
1655 > \end{array}
1656 > \]
1657 >
1658 > \[
1659 > D_R  = \left( {\begin{array}{*{20}c}
1660 >   {D_R^{tt} } & {D_R^{rt} }  \\
1661 >   {D_R^{tr} } & {D_R^{rr} }  \\
1662 > \end{array}} \right) = k_b T\left( {\begin{array}{*{20}c}
1663 >   {\Xi _R^{tt} } & {\Xi _R^{rt} }  \\
1664 >   {\Xi _R^{tr} } & {\Xi _R^{rr} }  \\
1665 > \end{array}} \right)^{ - 1}
1666 > \]
1667 >
1668 >
1669 > %Approximation Methods
1670 >
1671 > %\section{\label{introSection:correlationFunctions}Correlation Functions}

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