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# 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 571 | Line 570 | The free rigid body is an example of Poisson system (a
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 $x$.
574 The free rigid body is an example of Poisson system (actually a
575 Lie-Poisson system) with Hamiltonian function of angular kinetic
576 energy.
577 \begin{equation}
578 J(\pi ) = \left( {\begin{array}{*{20}c}
579   0 & {\pi _3 } & { - \pi _2 }  \\
580   { - \pi _3 } & 0 & {\pi _1 }  \\
581   {\pi _2 } & { - \pi _1 } & 0  \\
582 \end{array}} \right)
583 \end{equation}
573  
585 \begin{equation}
586 H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \frac{{\pi _2^2
587 }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
588 \end{equation}
589
574   \subsection{\label{introSection:exactFlow}Exact Flow}
575  
576   Let $x(t)$ be the exact solution of the ODE system,
# Line 635 | Line 619 | a \emph{symplectic} flow if it satisfies,
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 643 | 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.
633  
# Line 661 | Line 645 | When designing any numerical methods, one should alway
645   In other words, the flow of this vector field is reversible if and
646   only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $.
647  
648 < When designing any numerical methods, one should always try to
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}
# Line 685 | Line 704 | instability \cite{}. However, due to computational pen
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 \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}.
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  
# Line 736 | Line 755 | _{1,h/2} ,
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} ,
740 < \label{introEqaution:secondOrderSplitting}
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 < \lable{introEquation:timeReversible}
765 > \label{introEquation:timeReversible}
766   \end{equation}
767  
768   \subsubsection{\label{introSection:exampleSplittingMethod}Example of Splitting Method}
# Line 802 | Line 820 | q(\Delta t) = q(0) + \frac{{\Delta t}}{2}\left[ {\dot
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
823 > q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
824   q(\Delta t)} \right]. %
825 < \label{introEquation:positionVerlet1}
825 > \label{introEquation:positionVerlet2}
826   \end{align}
827  
828   \subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods}
# Line 828 | Line 846 | can obtain
846   \]
847   Applying Baker-Campbell-Hausdorff formula to Sprang splitting, we
848   can obtain
849 < \begin{eqnarray}
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 < h^3 [Y,[Y,X]]/12 - h^3 [X,[X,Y]]/24 +  \ldots )
853 < \end{eqnarray}
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
# Line 869 | Line 888 | dynamical information.
888   biological systems, providing structural, thermodynamic and
889   dynamical information.
890  
891 < \subsection{\label{introSec:mdInit}Initialization}
891 > One of the principal tools for modeling proteins, nucleic acids and
892 > their complexes. Stability of proteins Folding of proteins.
893 > Molecular recognition by:proteins, DNA, RNA, lipids, hormones STP,
894 > etc. Enzyme reactions Rational design of biologically active
895 > molecules (drug design) Small and large-scale conformational
896 > changes. determination and construction of 3D structures (homology,
897 > Xray diffraction, NMR) Dynamic processes such as ion transport in
898 > biological systems.
899  
900 < \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
900 > Macroscopic properties are related to microscopic behavior.
901  
902 < \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
902 > Time dependent (and independent) microscopic behavior of a molecule
903 > can be calculated by molecular dynamics simulations.
904  
905 < A rigid body is a body in which the distance between any two given
879 < points of a rigid body remains constant regardless of external
880 < forces exerted on it. A rigid body therefore conserves its shape
881 < during its motion.
905 > \subsection{\label{introSec:mdInit}Initialization}
906  
907 < Applications of dynamics of rigid bodies.
907 > \subsection{\label{introSec:forceEvaluation}Force Evaluation}
908  
909 < \subsection{\label{introSection:lieAlgebra}Lie Algebra}
909 > \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
910  
911 < \subsection{\label{introSection:DLMMotionEquation}The Euler Equations of Rigid Body Motion}
911 > \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
912  
913 < \subsection{\label{introSection:otherRBMotionEquation}Other Formulations for Rigid Body Motion}
913 > Rigid bodies are frequently involved in the modeling of different
914 > areas, from engineering, physics, to chemistry. For example,
915 > missiles and vehicle are usually modeled by rigid bodies.  The
916 > movement of the objects in 3D gaming engine or other physics
917 > simulator is governed by the rigid body dynamics. In molecular
918 > simulation, rigid body is used to simplify the model in
919 > protein-protein docking study{\cite{Gray03}}.
920  
921 < \section{\label{introSection:correlationFunctions}Correlation Functions}
922 <
923 < \section{\label{introSection:langevinDynamics}Langevin Dynamics}
924 <
925 < \subsection{\label{introSection:LDIntroduction}Introduction and application of Langevin Dynamics}
926 <
927 < \subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics}
921 > It is very important to develop stable and efficient methods to
922 > integrate the equations of motion of orientational degrees of
923 > freedom. Euler angles are the nature choice to describe the
924 > rotational degrees of freedom. However, due to its singularity, the
925 > numerical integration of corresponding equations of motion is very
926 > inefficient and inaccurate. Although an alternative integrator using
927 > different sets of Euler angles can overcome this difficulty\cite{},
928 > the computational penalty and the lost of angular momentum
929 > conservation still remain. A singularity free representation
930 > utilizing quaternions was developed by Evans in 1977. Unfortunately,
931 > this approach suffer from the nonseparable Hamiltonian resulted from
932 > quaternion representation, which prevents the symplectic algorithm
933 > to be utilized. Another different approach is to apply holonomic
934 > constraints to the atoms belonging to the rigid body. Each atom
935 > moves independently under the normal forces deriving from potential
936 > energy and constraint forces which are used to guarantee the
937 > rigidness. However, due to their iterative nature, SHAKE and Rattle
938 > algorithm converge very slowly when the number of constraint
939 > increases.
940  
941 + The break through in geometric literature suggests that, in order to
942 + develop a long-term integration scheme, one should preserve the
943 + symplectic structure of the flow. Introducing conjugate momentum to
944 + rotation matrix $Q$ and re-formulating Hamiltonian's equation, a
945 + symplectic integrator, RSHAKE, was proposed to evolve the
946 + Hamiltonian system in a constraint manifold by iteratively
947 + satisfying the orthogonality constraint $Q_T Q = 1$. An alternative
948 + method using quaternion representation was developed by Omelyan.
949 + However, both of these methods are iterative and inefficient. In
950 + this section, we will present a symplectic Lie-Poisson integrator
951 + for rigid body developed by Dullweber and his
952 + coworkers\cite{Dullweber1997} in depth.
953 +
954 + \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body}
955 + The motion of the rigid body is Hamiltonian with the Hamiltonian
956 + function
957   \begin{equation}
958 < H = \frac{{p^2 }}{{2m}} + U(x) + H_B  + \Delta U(x,x_1 , \ldots x_N)
959 < \label{introEquation:bathGLE}
958 > H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
959 > V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
960 > \label{introEquation:RBHamiltonian}
961   \end{equation}
962 < where $H_B$ is harmonic bath Hamiltonian,
962 > Here, $q$ and $Q$  are the position and rotation matrix for the
963 > rigid-body, $p$ and $P$  are conjugate momenta to $q$  and $Q$ , and
964 > $J$, a diagonal matrix, is defined by
965   \[
966 < H_B  =\sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
906 < }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  w_\alpha ^2 } \right\}}
966 > I_{ii}^{ - 1}  = \frac{1}{2}\sum\limits_{i \ne j} {J_{jj}^{ - 1} }
967   \]
968 < and $\Delta U$ is bilinear system-bath coupling,
968 > where $I_{ii}$ is the diagonal element of the inertia tensor. This
969 > constrained Hamiltonian equation subjects to a holonomic constraint,
970 > \begin{equation}
971 > Q^T Q = 1$, \label{introEquation:orthogonalConstraint}
972 > \end{equation}
973 > which is used to ensure rotation matrix's orthogonality.
974 > Differentiating \ref{introEquation:orthogonalConstraint} and using
975 > Equation \ref{introEquation:RBMotionMomentum}, one may obtain,
976 > \begin{equation}
977 > Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0 . \\
978 > \label{introEquation:RBFirstOrderConstraint}
979 > \end{equation}
980 >
981 > Using Equation (\ref{introEquation:motionHamiltonianCoordinate},
982 > \ref{introEquation:motionHamiltonianMomentum}), one can write down
983 > the equations of motion,
984   \[
985 < \Delta U =  - \sum\limits_{\alpha  = 1}^N {g_\alpha  x_\alpha  x}
985 > \begin{array}{c}
986 > \frac{{dq}}{{dt}} = \frac{p}{m} \label{introEquation:RBMotionPosition}\\
987 > \frac{{dp}}{{dt}} =  - \nabla _q V(q,Q) \label{introEquation:RBMotionMomentum}\\
988 > \frac{{dQ}}{{dt}} = PJ^{ - 1}  \label{introEquation:RBMotionRotation}\\
989 > \frac{{dP}}{{dt}} =  - \nabla _Q V(q,Q) - 2Q\Lambda . \label{introEquation:RBMotionP}\\
990 > \end{array}
991   \]
992 < Completing the square,
992 >
993 > In general, there are two ways to satisfy the holonomic constraints.
994 > We can use constraint force provided by lagrange multiplier on the
995 > normal manifold to keep the motion on constraint space. Or we can
996 > simply evolve the system in constraint manifold. The two method are
997 > proved to be equivalent. The holonomic constraint and equations of
998 > motions define a constraint manifold for rigid body
999   \[
1000 < H_B  + \Delta U = \sum\limits_{\alpha  = 1}^N {\left\{
1001 < {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
916 < w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
917 < w_\alpha ^2 }}x} \right)^2 } \right\}}  - \sum\limits_{\alpha  =
918 < 1}^N {\frac{{g_\alpha ^2 }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1000 > M = \left\{ {(Q,P):Q^T Q = 1,Q^T PJ^{ - 1}  + J^{ - 1} P^T Q = 0}
1001 > \right\}.
1002   \]
1003 < and putting it back into Eq.~\ref{introEquation:bathGLE},
1003 >
1004 > Unfortunately, this constraint manifold is not the cotangent bundle
1005 > $T_{\star}SO(3)$. However, it turns out that under symplectic
1006 > transformation, the cotangent space and the phase space are
1007 > diffeomorphic. Introducing
1008   \[
1009 < H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha  = 1}^N
923 < {\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
924 < w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
925 < w_\alpha ^2 }}x} \right)^2 } \right\}}
1009 > \tilde Q = Q,\tilde P = \frac{1}{2}\left( {P - QP^T Q} \right),
1010   \]
1011 < where
1011 > the mechanical system subject to a holonomic constraint manifold $M$
1012 > can be re-formulated as a Hamiltonian system on the cotangent space
1013   \[
1014 < W(x) = U(x) - \sum\limits_{\alpha  = 1}^N {\frac{{g_\alpha ^2
1015 < }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1014 > T^* SO(3) = \left\{ {(\tilde Q,\tilde P):\tilde Q^T \tilde Q =
1015 > 1,\tilde Q^T \tilde PJ^{ - 1}  + J^{ - 1} P^T \tilde Q = 0} \right\}
1016   \]
932 Since the first two terms of the new Hamiltonian depend only on the
933 system coordinates, we can get the equations of motion for
934 Generalized Langevin Dynamics by Hamilton's equations
935 \ref{introEquation:motionHamiltonianCoordinate,
936 introEquation:motionHamiltonianMomentum},
937 \begin{align}
938 \dot p &=  - \frac{{\partial H}}{{\partial x}}
939       &= m\ddot x
940       &= - \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)}
941 \label{introEquation:Lp5}
942 \end{align}
943 , and
944 \begin{align}
945 \dot p_\alpha   &=  - \frac{{\partial H}}{{\partial x_\alpha  }}
946                &= m\ddot x_\alpha
947                &= \- m_\alpha  w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha}}{{m_\alpha  w_\alpha ^2 }}x} \right)
948 \end{align}
1017  
1018 < \subsection{\label{introSection:laplaceTransform}The Laplace Transform}
1019 <
1018 > For a body fixed vector $X_i$ with respect to the center of mass of
1019 > the rigid body, its corresponding lab fixed vector $X_0^{lab}$  is
1020 > given as
1021 > \begin{equation}
1022 > X_i^{lab} = Q X_i + q.
1023 > \end{equation}
1024 > Therefore, potential energy $V(q,Q)$ is defined by
1025   \[
1026 < L(x) = \int_0^\infty  {x(t)e^{ - pt} dt}
1026 > V(q,Q) = V(Q X_0 + q).
1027   \]
1028 <
1028 > Hence, the force and torque are given by
1029   \[
1030 < L(x + y) = L(x) + L(y)
1030 > \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)},
1031   \]
1032 <
1032 > and
1033   \[
1034 < L(ax) = aL(x)
1034 > \nabla _Q V(q,Q) = F(q,Q)X_i^t
1035   \]
1036 + respectively.
1037  
1038 < \[
1039 < L(\dot x) = pL(x) - px(0)
1038 > As a common choice to describe the rotation dynamics of the rigid
1039 > body, angular momentum on body frame $\Pi  = Q^t P$ is introduced to
1040 > rewrite the equations of motion,
1041 > \begin{equation}
1042 > \begin{array}{l}
1043 > \mathop \Pi \limits^ \bullet   = J^{ - 1} \Pi ^T \Pi  + Q^T \sum\limits_i {F_i (q,Q)X_i^T }  - \Lambda  \\
1044 > \mathop Q\limits^{{\rm{   }} \bullet }  = Q\Pi {\rm{ }}J^{ - 1}  \\
1045 > \end{array}
1046 > \label{introEqaution:RBMotionPI}
1047 > \end{equation}
1048 > , as well as holonomic constraints,
1049 > \[
1050 > \begin{array}{l}
1051 > \Pi J^{ - 1}  + J^{ - 1} \Pi ^t  = 0 \\
1052 > Q^T Q = 1 \\
1053 > \end{array}
1054   \]
1055  
1056 + For a vector $v(v_1 ,v_2 ,v_3 ) \in R^3$ and a matrix $\hat v \in
1057 + so(3)^ \star$, the hat-map isomorphism,
1058 + \begin{equation}
1059 + v(v_1 ,v_2 ,v_3 ) \Leftrightarrow \hat v = \left(
1060 + {\begin{array}{*{20}c}
1061 +   0 & { - v_3 } & {v_2 }  \\
1062 +   {v_3 } & 0 & { - v_1 }  \\
1063 +   { - v_2 } & {v_1 } & 0  \\
1064 + \end{array}} \right),
1065 + \label{introEquation:hatmapIsomorphism}
1066 + \end{equation}
1067 + will let us associate the matrix products with traditional vector
1068 + operations
1069   \[
1070 < L(\ddot x) = p^2 L(x) - px(0) - \dot x(0)
1070 > \hat vu = v \times u
1071   \]
1072  
1073 + Using \ref{introEqaution:RBMotionPI}, one can construct a skew
1074 + matrix,
1075 + \begin{equation}
1076 + (\mathop \Pi \limits^ \bullet   - \mathop \Pi \limits^ \bullet  ^T
1077 + ){\rm{ }} = {\rm{ }}(\Pi  - \Pi ^T ){\rm{ }}(J^{ - 1} \Pi  + \Pi J^{
1078 + - 1} ) + \sum\limits_i {[Q^T F_i (r,Q)X_i^T  - X_i F_i (r,Q)^T Q]} -
1079 + (\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
1080 + \end{equation}
1081 + Since $\Lambda$ is symmetric, the last term of Equation
1082 + \ref{introEquation:skewMatrixPI} is zero, which implies the Lagrange
1083 + multiplier $\Lambda$ is absent from the equations of motion. This
1084 + unique property eliminate the requirement of iterations which can
1085 + not be avoided in other methods\cite{}.
1086 +
1087 + Applying hat-map isomorphism, we obtain the equation of motion for
1088 + angular momentum on body frame
1089 + \begin{equation}
1090 + \dot \pi  = \pi  \times I^{ - 1} \pi  + \sum\limits_i {\left( {Q^T
1091 + F_i (r,Q)} \right) \times X_i }.
1092 + \label{introEquation:bodyAngularMotion}
1093 + \end{equation}
1094 + In the same manner, the equation of motion for rotation matrix is
1095 + given by
1096   \[
1097 < L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p)
1097 > \dot Q = Qskew(I^{ - 1} \pi )
1098   \]
1099  
1100 < Some relatively important transformation,
1100 > \subsection{\label{introSection:SymplecticFreeRB}Symplectic
1101 > Lie-Poisson Integrator for Free Rigid Body}
1102 >
1103 > If there is not external forces exerted on the rigid body, the only
1104 > contribution to the rotational is from the kinetic potential (the
1105 > first term of \ref{ introEquation:bodyAngularMotion}). The free
1106 > rigid body is an example of Lie-Poisson system with Hamiltonian
1107 > function
1108 > \begin{equation}
1109 > T^r (\pi ) = T_1 ^r (\pi _1 ) + T_2^r (\pi _2 ) + T_3^r (\pi _3 )
1110 > \label{introEquation:rotationalKineticRB}
1111 > \end{equation}
1112 > where $T_i^r (\pi _i ) = \frac{{\pi _i ^2 }}{{2I_i }}$ and
1113 > Lie-Poisson structure matrix,
1114 > \begin{equation}
1115 > J(\pi ) = \left( {\begin{array}{*{20}c}
1116 >   0 & {\pi _3 } & { - \pi _2 }  \\
1117 >   { - \pi _3 } & 0 & {\pi _1 }  \\
1118 >   {\pi _2 } & { - \pi _1 } & 0  \\
1119 > \end{array}} \right)
1120 > \end{equation}
1121 > Thus, the dynamics of free rigid body is governed by
1122 > \begin{equation}
1123 > \frac{d}{{dt}}\pi  = J(\pi )\nabla _\pi  T^r (\pi )
1124 > \end{equation}
1125 >
1126 > One may notice that each $T_i^r$ in Equation
1127 > \ref{introEquation:rotationalKineticRB} can be solved exactly. For
1128 > instance, the equations of motion due to $T_1^r$ are given by
1129 > \begin{equation}
1130 > \frac{d}{{dt}}\pi  = R_1 \pi ,\frac{d}{{dt}}Q = QR_1
1131 > \label{introEqaution:RBMotionSingleTerm}
1132 > \end{equation}
1133 > where
1134 > \[ R_1  = \left( {\begin{array}{*{20}c}
1135 >   0 & 0 & 0  \\
1136 >   0 & 0 & {\pi _1 }  \\
1137 >   0 & { - \pi _1 } & 0  \\
1138 > \end{array}} \right).
1139 > \]
1140 > The solutions of Equation \ref{introEqaution:RBMotionSingleTerm} is
1141   \[
1142 < L(\cos at) = \frac{p}{{p^2  + a^2 }}
1142 > \pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) =
1143 > Q(0)e^{\Delta tR_1 }
1144   \]
1145 + with
1146 + \[
1147 + e^{\Delta tR_1 }  = \left( {\begin{array}{*{20}c}
1148 +   0 & 0 & 0  \\
1149 +   0 & {\cos \theta _1 } & {\sin \theta _1 }  \\
1150 +   0 & { - \sin \theta _1 } & {\cos \theta _1 }  \\
1151 + \end{array}} \right),\theta _1  = \frac{{\pi _1 }}{{I_1 }}\Delta t.
1152 + \]
1153 + To reduce the cost of computing expensive functions in $e^{\Delta
1154 + tR_1 }$, we can use Cayley transformation,
1155 + \[
1156 + e^{\Delta tR_1 }  \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1
1157 + )
1158 + \]
1159  
1160 + The flow maps for $T_2^r$ and $T_2^r$ can be found in the same
1161 + manner.
1162 +
1163 + In order to construct a second-order symplectic method, we split the
1164 + angular kinetic Hamiltonian function can into five terms
1165   \[
1166 < L(\sin at) = \frac{a}{{p^2  + a^2 }}
1166 > T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2
1167 > ) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r
1168 > (\pi _1 )
1169 > \].
1170 > Concatenating flows corresponding to these five terms, we can obtain
1171 > an symplectic integrator,
1172 > \[
1173 > \varphi _{\Delta t,T^r }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1174 > \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
1175 > \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1176 > _1 }.
1177   \]
1178  
1179 + The non-canonical Lie-Poisson bracket ${F, G}$ of two function
1180 + $F(\pi )$ and $G(\pi )$ is defined by
1181   \[
1182 < L(1) = \frac{1}{p}
1182 > \{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
1183 > )
1184   \]
1185 + If the Poisson bracket of a function $F$ with an arbitrary smooth
1186 + function $G$ is zero, $F$ is a \emph{Casimir}, which is the
1187 + conserved quantity in Poisson system. We can easily verify that the
1188 + norm of the angular momentum, $\parallel \pi
1189 + \parallel$, is a \emph{Casimir}. Let$ F(\pi ) = S(\frac{{\parallel
1190 + \pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
1191 + then by the chain rule
1192 + \[
1193 + \nabla _\pi  F(\pi ) = S'(\frac{{\parallel \pi \parallel ^2
1194 + }}{2})\pi
1195 + \]
1196 + Thus $ [\nabla _\pi  F(\pi )]^T J(\pi ) =  - S'(\frac{{\parallel \pi
1197 + \parallel ^2 }}{2})\pi  \times \pi  = 0 $. This explicit
1198 + Lie-Poisson integrator is found to be extremely efficient and stable
1199 + which can be explained by the fact the small angle approximation is
1200 + used and the norm of the angular momentum is conserved.
1201  
1202 < First, the bath coordinates,
1202 > \subsection{\label{introSection:RBHamiltonianSplitting} Hamiltonian
1203 > Splitting for Rigid Body}
1204 >
1205 > The Hamiltonian of rigid body can be separated in terms of kinetic
1206 > energy and potential energy,
1207   \[
1208 < p^2 L(x_\alpha  ) - px_\alpha  (0) - \dot x_\alpha  (0) =  - \omega
992 < _\alpha ^2 L(x_\alpha  ) + \frac{{g_\alpha  }}{{\omega _\alpha
993 < }}L(x)
1208 > H = T(p,\pi ) + V(q,Q)
1209   \]
1210 + The equations of motion corresponding to potential energy and
1211 + kinetic energy are listed in the below table,
1212 + \begin{center}
1213 + \begin{tabular}{|l|l|}
1214 +  \hline
1215 +  % after \\: \hline or \cline{col1-col2} \cline{col3-col4} ...
1216 +  Potential & Kinetic \\
1217 +  $\frac{{dq}}{{dt}} = \frac{p}{m}$ & $\frac{d}{{dt}}q = p$ \\
1218 +  $\frac{d}{{dt}}p =  - \frac{{\partial V}}{{\partial q}}$ & $ \frac{d}{{dt}}p = 0$ \\
1219 +  $\frac{d}{{dt}}Q = 0$ & $ \frac{d}{{dt}}Q = Qskew(I^{ - 1} j)$ \\
1220 +  $ \frac{d}{{dt}}\pi  = \sum\limits_i {\left( {Q^T F_i (r,Q)} \right) \times X_i }$ & $\frac{d}{{dt}}\pi  = \pi  \times I^{ - 1} \pi$\\
1221 +  \hline
1222 + \end{tabular}
1223 + \end{center}
1224 + A second-order symplectic method is now obtained by the composition
1225 + of the flow maps,
1226   \[
1227 < L(x_\alpha  ) = \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) +
1228 < px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }}
1227 > \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1228 > _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}.
1229   \]
1230 < Then, the system coordinates,
1231 < \begin{align}
1232 < mL(\ddot x) &=  - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} -
1233 < \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{\frac{{g_\alpha
1234 < }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha
1235 < (0)}}{{p^2  + \omega _\alpha ^2 }} - \frac{{g_\alpha ^2 }}{{m_\alpha
1236 < }}\omega _\alpha ^2 L(x)} \right\}}
1237 < %
1238 < &= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} -
1008 < \sum\limits_{\alpha  = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}\frac{p}{{p^2  + \omega _\alpha ^2 }}pL(x)
1009 < - \frac{p}{{p^2  + \omega _\alpha ^2 }}g_\alpha  x_\alpha  (0)
1010 < - \frac{1}{{p^2  + \omega _\alpha ^2 }}g_\alpha  \dot x_\alpha  (0)} \right\}}
1011 < \end{align}
1012 < Then, the inverse transform,
1230 > Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two
1231 > sub-flows which corresponding to force and torque respectively,
1232 > \[
1233 > \varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
1234 > _{\Delta t/2,\tau }.
1235 > \]
1236 > Since the associated operators of $\varphi _{\Delta t/2,F} $ and
1237 > $\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition
1238 > order inside $\varphi _{\Delta t/2,V}$ does not matter.
1239  
1240 + Furthermore, kinetic potential can be separated to translational
1241 + kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$,
1242 + \begin{equation}
1243 + T(p,\pi ) =T^t (p) + T^r (\pi ).
1244 + \end{equation}
1245 + where $ T^t (p) = \frac{1}{2}p^T m^{ - 1} p $ and $T^r (\pi )$ is
1246 + defined by \ref{introEquation:rotationalKineticRB}. Therefore, the
1247 + corresponding flow maps are given by
1248 + \[
1249 + \varphi _{\Delta t,T}  = \varphi _{\Delta t,T^t }  \circ \varphi
1250 + _{\Delta t,T^r }.
1251 + \]
1252 + Finally, we obtain the overall symplectic flow maps for free moving
1253 + rigid body
1254 + \begin{equation}
1255 + \begin{array}{c}
1256 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,F}  \circ \varphi _{\Delta t/2,\tau }  \\
1257 +  \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 }  \\
1258 +  \circ \varphi _{\Delta t/2,\tau }  \circ \varphi _{\Delta t/2,F}  .\\
1259 + \end{array}
1260 + \label{introEquation:overallRBFlowMaps}
1261 + \end{equation}
1262 +
1263 + \section{\label{introSection:langevinDynamics}Langevin Dynamics}
1264 + As an alternative to newtonian dynamics, Langevin dynamics, which
1265 + mimics a simple heat bath with stochastic and dissipative forces,
1266 + has been applied in a variety of studies. This section will review
1267 + the theory of Langevin dynamics simulation. A brief derivation of
1268 + generalized Langevin equation will be given first. Follow that, we
1269 + will discuss the physical meaning of the terms appearing in the
1270 + equation as well as the calculation of friction tensor from
1271 + hydrodynamics theory.
1272 +
1273 + \subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation}
1274 +
1275 + Harmonic bath model, in which an effective set of harmonic
1276 + oscillators are used to mimic the effect of a linearly responding
1277 + environment, has been widely used in quantum chemistry and
1278 + statistical mechanics. One of the successful applications of
1279 + Harmonic bath model is the derivation of Deriving Generalized
1280 + Langevin Dynamics. Lets consider a system, in which the degree of
1281 + freedom $x$ is assumed to couple to the bath linearly, giving a
1282 + Hamiltonian of the form
1283 + \begin{equation}
1284 + H = \frac{{p^2 }}{{2m}} + U(x) + H_B  + \Delta U(x,x_1 , \ldots x_N)
1285 + \label{introEquation:bathGLE}.
1286 + \end{equation}
1287 + Here $p$ is a momentum conjugate to $q$, $m$ is the mass associated
1288 + with this degree of freedom, $H_B$ is harmonic bath Hamiltonian,
1289 + \[
1290 + H_B  = \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
1291 + }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  \omega _\alpha ^2 }
1292 + \right\}}
1293 + \]
1294 + where the index $\alpha$ runs over all the bath degrees of freedom,
1295 + $\omega _\alpha$ are the harmonic bath frequencies, $m_\alpha$ are
1296 + the harmonic bath masses, and $\Delta U$ is bilinear system-bath
1297 + coupling,
1298 + \[
1299 + \Delta U =  - \sum\limits_{\alpha  = 1}^N {g_\alpha  x_\alpha  x}
1300 + \]
1301 + where $g_\alpha$ are the coupling constants between the bath and the
1302 + coordinate $x$. Introducing
1303 + \[
1304 + W(x) = U(x) - \sum\limits_{\alpha  = 1}^N {\frac{{g_\alpha ^2
1305 + }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1306 + \] and combining the last two terms in Equation
1307 + \ref{introEquation:bathGLE}, we may rewrite the Harmonic bath
1308 + Hamiltonian as
1309 + \[
1310 + H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha  = 1}^N
1311 + {\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
1312 + w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
1313 + w_\alpha ^2 }}x} \right)^2 } \right\}}
1314 + \]
1315 + Since the first two terms of the new Hamiltonian depend only on the
1316 + system coordinates, we can get the equations of motion for
1317 + Generalized Langevin Dynamics by Hamilton's equations
1318 + \ref{introEquation:motionHamiltonianCoordinate,
1319 + introEquation:motionHamiltonianMomentum},
1320 + \begin{equation}
1321 + m\ddot x =  - \frac{{\partial W(x)}}{{\partial x}} -
1322 + \sum\limits_{\alpha  = 1}^N {g_\alpha  \left( {x_\alpha   -
1323 + \frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right)},
1324 + \label{introEquation:coorMotionGLE}
1325 + \end{equation}
1326 + and
1327 + \begin{equation}
1328 + m\ddot x_\alpha   =  - m_\alpha  w_\alpha ^2 \left( {x_\alpha   -
1329 + \frac{{g_\alpha  }}{{m_\alpha  w_\alpha ^2 }}x} \right).
1330 + \label{introEquation:bathMotionGLE}
1331 + \end{equation}
1332 +
1333 + In order to derive an equation for $x$, the dynamics of the bath
1334 + variables $x_\alpha$ must be solved exactly first. As an integral
1335 + transform which is particularly useful in solving linear ordinary
1336 + differential equations, Laplace transform is the appropriate tool to
1337 + solve this problem. The basic idea is to transform the difficult
1338 + differential equations into simple algebra problems which can be
1339 + solved easily. Then applying inverse Laplace transform, also known
1340 + as the Bromwich integral, we can retrieve the solutions of the
1341 + original problems.
1342 +
1343 + Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace
1344 + transform of f(t) is a new function defined as
1345 + \[
1346 + L(f(t)) \equiv F(p) = \int_0^\infty  {f(t)e^{ - pt} dt}
1347 + \]
1348 + where  $p$ is real and  $L$ is called the Laplace Transform
1349 + Operator. Below are some important properties of Laplace transform
1350 + \begin{equation}
1351 + \begin{array}{c}
1352 + L(x + y) = L(x) + L(y) \\
1353 + L(ax) = aL(x) \\
1354 + L(\dot x) = pL(x) - px(0) \\
1355 + L(\ddot x) = p^2 L(x) - px(0) - \dot x(0) \\
1356 + L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p) \\
1357 + \end{array}
1358 + \end{equation}
1359 +
1360 + Applying Laplace transform to the bath coordinates, we obtain
1361 + \[
1362 + \begin{array}{c}
1363 + p^2 L(x_\alpha  ) - px_\alpha  (0) - \dot x_\alpha  (0) =  - \omega _\alpha ^2 L(x_\alpha  ) + \frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) \\
1364 + L(x_\alpha  ) = \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }} \\
1365 + \end{array}
1366 + \]
1367 + By the same way, the system coordinates become
1368 + \[
1369 + \begin{array}{c}
1370 + mL(\ddot x) =  - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\
1371 +  - \sum\limits_{\alpha  = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}\frac{p}{{p^2  + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2  + \omega _\alpha ^2 }}g_\alpha  x_\alpha  (0) - \frac{1}{{p^2  + \omega _\alpha ^2 }}g_\alpha  \dot x_\alpha  (0)} \right\}}  \\
1372 + \end{array}
1373 + \]
1374 +
1375 + With the help of some relatively important inverse Laplace
1376 + transformations:
1377 + \[
1378 + \begin{array}{c}
1379 + L(\cos at) = \frac{p}{{p^2  + a^2 }} \\
1380 + L(\sin at) = \frac{a}{{p^2  + a^2 }} \\
1381 + L(1) = \frac{1}{p} \\
1382 + \end{array}
1383 + \]
1384 + , we obtain
1385   \begin{align}
1386   m\ddot x &=  - \frac{{\partial W(x)}}{{\partial x}} -
1387   \sum\limits_{\alpha  = 1}^N {\left\{ {\left( { - \frac{{g_\alpha ^2
# Line 1030 | Line 1401 | t)\dot x(t - \tau )d} \tau }  + \sum\limits_{\alpha  =
1401   (\omega _\alpha  t)} \right\}}
1402   \end{align}
1403  
1404 + Introducing a \emph{dynamic friction kernel}
1405   \begin{equation}
1406 + \xi (t) = \sum\limits_{\alpha  = 1}^N {\left( { - \frac{{g_\alpha ^2
1407 + }}{{m_\alpha  \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha  t)}
1408 + \label{introEquation:dynamicFrictionKernelDefinition}
1409 + \end{equation}
1410 + and \emph{a random force}
1411 + \begin{equation}
1412 + R(t) = \sum\limits_{\alpha  = 1}^N {\left( {g_\alpha  x_\alpha  (0)
1413 + - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}x(0)}
1414 + \right)\cos (\omega _\alpha  t)}  + \frac{{\dot x_\alpha
1415 + (0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t),
1416 + \label{introEquation:randomForceDefinition}
1417 + \end{equation}
1418 + the equation of motion can be rewritten as
1419 + \begin{equation}
1420   m\ddot x =  - \frac{{\partial W}}{{\partial x}} - \int_0^t {\xi
1421   (t)\dot x(t - \tau )d\tau }  + R(t)
1422   \label{introEuqation:GeneralizedLangevinDynamics}
1423   \end{equation}
1424 < %where $ {\xi (t)}$ is friction kernel, $R(t)$ is random force and
1425 < %$W$ is the potential of mean force. $W(x) =  - kT\ln p(x)$
1424 > which is known as the \emph{generalized Langevin equation}.
1425 >
1426 > \subsubsection{\label{introSection:randomForceDynamicFrictionKernel}Random Force and Dynamic Friction Kernel}
1427 >
1428 > One may notice that $R(t)$ depends only on initial conditions, which
1429 > implies it is completely deterministic within the context of a
1430 > harmonic bath. However, it is easy to verify that $R(t)$ is totally
1431 > uncorrelated to $x$ and $\dot x$,
1432   \[
1433 < \xi (t) = \sum\limits_{\alpha  = 1}^N {\left( { - \frac{{g_\alpha ^2
1434 < }}{{m_\alpha  \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha  t)}
1433 > \begin{array}{l}
1434 > \left\langle {x(t)R(t)} \right\rangle  = 0, \\
1435 > \left\langle {\dot x(t)R(t)} \right\rangle  = 0. \\
1436 > \end{array}
1437   \]
1438 < For an infinite harmonic bath, we can use the spectral density and
1439 < an integral over frequencies.
1438 > This property is what we expect from a truly random process. As long
1439 > as the model, which is gaussian distribution in general, chosen for
1440 > $R(t)$ is a truly random process, the stochastic nature of the GLE
1441 > still remains.
1442  
1443 + %dynamic friction kernel
1444 + The convolution integral
1445   \[
1446 < R(t) = \sum\limits_{\alpha  = 1}^N {\left( {g_\alpha  x_\alpha  (0)
1049 < - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}x(0)}
1050 < \right)\cos (\omega _\alpha  t)}  + \frac{{\dot x_\alpha
1051 < (0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t)
1446 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }
1447   \]
1448 < The random forces depend only on initial conditions.
1448 > depends on the entire history of the evolution of $x$, which implies
1449 > that the bath retains memory of previous motions. In other words,
1450 > the bath requires a finite time to respond to change in the motion
1451 > of the system. For a sluggish bath which responds slowly to changes
1452 > in the system coordinate, we may regard $\xi(t)$ as a constant
1453 > $\xi(t) = \Xi_0$. Hence, the convolution integral becomes
1454 > \[
1455 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }  = \xi _0 (x(t) - x(0))
1456 > \]
1457 > and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes
1458 > \[
1459 > m\ddot x =  - \frac{\partial }{{\partial x}}\left( {W(x) +
1460 > \frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t),
1461 > \]
1462 > which can be used to describe dynamic caging effect. The other
1463 > extreme is the bath that responds infinitely quickly to motions in
1464 > the system. Thus, $\xi (t)$ can be taken as a $delta$ function in
1465 > time:
1466 > \[
1467 > \xi (t) = 2\xi _0 \delta (t)
1468 > \]
1469 > Hence, the convolution integral becomes
1470 > \[
1471 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }  = 2\xi _0 \int_0^t
1472 > {\delta (t)\dot x(t - \tau )d\tau }  = \xi _0 \dot x(t),
1473 > \]
1474 > and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes
1475 > \begin{equation}
1476 > m\ddot x =  - \frac{{\partial W(x)}}{{\partial x}} - \xi _0 \dot
1477 > x(t) + R(t) \label{introEquation:LangevinEquation}
1478 > \end{equation}
1479 > which is known as the Langevin equation. The static friction
1480 > coefficient $\xi _0$ can either be calculated from spectral density
1481 > or be determined by Stokes' law for regular shaped particles.A
1482 > briefly review on calculating friction tensor for arbitrary shaped
1483 > particles is given in section \ref{introSection:frictionTensor}.
1484  
1485   \subsubsection{\label{introSection:secondFluctuationDissipation}The Second Fluctuation Dissipation Theorem}
1486 < So we can define a new set of coordinates,
1486 >
1487 > Defining a new set of coordinates,
1488   \[
1489   q_\alpha  (t) = x_\alpha  (t) - \frac{1}{{m_\alpha  \omega _\alpha
1490   ^2 }}x(0)
1491 < \]
1492 < This makes
1491 > \],
1492 > we can rewrite $R(T)$ as
1493   \[
1494 < R(t) = \sum\limits_{\alpha  = 1}^N {g_\alpha  q_\alpha  (t)}
1494 > R(t) = \sum\limits_{\alpha  = 1}^N {g_\alpha  q_\alpha  (t)}.
1495   \]
1496   And since the $q$ coordinates are harmonic oscillators,
1497   \[
1498 < \begin{array}{l}
1498 > \begin{array}{c}
1499 > \left\langle {q_\alpha ^2 } \right\rangle  = \frac{{kT}}{{m_\alpha  \omega _\alpha ^2 }} \\
1500   \left\langle {q_\alpha  (t)q_\alpha  (0)} \right\rangle  = \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha  t) \\
1501   \left\langle {q_\alpha  (t)q_\beta  (0)} \right\rangle  = \delta _{\alpha \beta } \left\langle {q_\alpha  (t)q_\alpha  (0)} \right\rangle  \\
1502 + \left\langle {R(t)R(0)} \right\rangle  = \sum\limits_\alpha  {\sum\limits_\beta  {g_\alpha  g_\beta  \left\langle {q_\alpha  (t)q_\beta  (0)} \right\rangle } }  \\
1503 +  = \sum\limits_\alpha  {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha  t)}  \\
1504 +  = kT\xi (t) \\
1505   \end{array}
1506   \]
1507 <
1073 < \begin{align}
1074 < \left\langle {R(t)R(0)} \right\rangle  &= \sum\limits_\alpha
1075 < {\sum\limits_\beta  {g_\alpha  g_\beta  \left\langle {q_\alpha
1076 < (t)q_\beta  (0)} \right\rangle } }
1077 < %
1078 < &= \sum\limits_\alpha  {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)}
1079 < \right\rangle \cos (\omega _\alpha  t)}
1080 < %
1081 < &= kT\xi (t)
1082 < \end{align}
1083 <
1507 > Thus, we recover the \emph{second fluctuation dissipation theorem}
1508   \begin{equation}
1509   \xi (t) = \left\langle {R(t)R(0)} \right\rangle
1510 < \label{introEquation:secondFluctuationDissipation}
1510 > \label{introEquation:secondFluctuationDissipation}.
1511   \end{equation}
1512 + In effect, it acts as a constraint on the possible ways in which one
1513 + can model the random force and friction kernel.
1514  
1089 \section{\label{introSection:hydroynamics}Hydrodynamics}
1090
1515   \subsection{\label{introSection:frictionTensor} Friction Tensor}
1516 < \subsection{\label{introSection:analyticalApproach}Analytical
1517 < Approach}
1518 <
1519 < \subsection{\label{introSection:approximationApproach}Approximation
1520 < Approach}
1516 > Theoretically, the friction kernel can be determined using velocity
1517 > autocorrelation function. However, this approach become impractical
1518 > when the system become more and more complicate. Instead, various
1519 > approaches based on hydrodynamics have been developed to calculate
1520 > the friction coefficients. The friction effect is isotropic in
1521 > Equation, \zeta can be taken as a scalar. In general, friction
1522 > tensor \Xi is a $6\times 6$ matrix given by
1523 > \[
1524 > \Xi  = \left( {\begin{array}{*{20}c}
1525 >   {\Xi _{}^{tt} } & {\Xi _{}^{rt} }  \\
1526 >   {\Xi _{}^{tr} } & {\Xi _{}^{rr} }  \\
1527 > \end{array}} \right).
1528 > \]
1529 > Here, $ {\Xi^{tt} }$ and $ {\Xi^{rr} }$ are translational friction
1530 > tensor and rotational resistance (friction) tensor respectively,
1531 > while ${\Xi^{tr} }$ is translation-rotation coupling tensor and $
1532 > {\Xi^{rt} }$ is rotation-translation coupling tensor. When a
1533 > particle moves in a fluid, it may experience friction force or
1534 > torque along the opposite direction of the velocity or angular
1535 > velocity,
1536 > \[
1537 > \left( \begin{array}{l}
1538 > F_R  \\
1539 > \tau _R  \\
1540 > \end{array} \right) =  - \left( {\begin{array}{*{20}c}
1541 >   {\Xi ^{tt} } & {\Xi ^{rt} }  \\
1542 >   {\Xi ^{tr} } & {\Xi ^{rr} }  \\
1543 > \end{array}} \right)\left( \begin{array}{l}
1544 > v \\
1545 > w \\
1546 > \end{array} \right)
1547 > \]
1548 > where $F_r$ is the friction force and $\tau _R$ is the friction
1549 > toque.
1550  
1551 < \subsection{\label{introSection:centersRigidBody}Centers of Rigid
1552 < Body}
1551 > \subsubsection{\label{introSection:resistanceTensorRegular}The Resistance Tensor for Regular Shape}
1552 >
1553 > For a spherical particle, the translational and rotational friction
1554 > constant can be calculated from Stoke's law,
1555 > \[
1556 > \Xi ^{tt}  = \left( {\begin{array}{*{20}c}
1557 >   {6\pi \eta R} & 0 & 0  \\
1558 >   0 & {6\pi \eta R} & 0  \\
1559 >   0 & 0 & {6\pi \eta R}  \\
1560 > \end{array}} \right)
1561 > \]
1562 > and
1563 > \[
1564 > \Xi ^{rr}  = \left( {\begin{array}{*{20}c}
1565 >   {8\pi \eta R^3 } & 0 & 0  \\
1566 >   0 & {8\pi \eta R^3 } & 0  \\
1567 >   0 & 0 & {8\pi \eta R^3 }  \\
1568 > \end{array}} \right)
1569 > \]
1570 > where $\eta$ is the viscosity of the solvent and $R$ is the
1571 > hydrodynamics radius.
1572 >
1573 > Other non-spherical shape, such as cylinder and ellipsoid
1574 > \textit{etc}, are widely used as reference for developing new
1575 > hydrodynamics theory, because their properties can be calculated
1576 > exactly. In 1936, Perrin extended Stokes's law to general ellipsoid,
1577 > also called a triaxial ellipsoid, which is given in Cartesian
1578 > coordinates by
1579 > \[
1580 > \frac{{x^2 }}{{a^2 }} + \frac{{y^2 }}{{b^2 }} + \frac{{z^2 }}{{c^2
1581 > }} = 1
1582 > \]
1583 > where the semi-axes are of lengths $a$, $b$, and $c$. Unfortunately,
1584 > due to the complexity of the elliptic integral, only the ellipsoid
1585 > with the restriction of two axes having to be equal, \textit{i.e.}
1586 > prolate($ a \ge b = c$) and oblate ($ a < b = c $), can be solved
1587 > exactly. Introducing an elliptic integral parameter $S$ for prolate,
1588 > \[
1589 > S = \frac{2}{{\sqrt {a^2  - b^2 } }}\ln \frac{{a + \sqrt {a^2  - b^2
1590 > } }}{b},
1591 > \]
1592 > and oblate,
1593 > \[
1594 > S = \frac{2}{{\sqrt {b^2  - a^2 } }}arctg\frac{{\sqrt {b^2  - a^2 }
1595 > }}{a}
1596 > \],
1597 > one can write down the translational and rotational resistance
1598 > tensors
1599 > \[
1600 > \begin{array}{l}
1601 > \Xi _a^{tt}  = 16\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - b^2 )S - 2a}} \\
1602 > \Xi _b^{tt}  = \Xi _c^{tt}  = 32\pi \eta \frac{{a^2  - b^2 }}{{(2a^2  - 3b^2 )S + 2a}} \\
1603 > \end{array},
1604 > \]
1605 > and
1606 > \[
1607 > \begin{array}{l}
1608 > \Xi _a^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^2  - b^2 )b^2 }}{{2a - b^2 S}} \\
1609 > \Xi _b^{rr}  = \Xi _c^{rr}  = \frac{{32\pi }}{3}\eta \frac{{(a^4  - b^4 )}}{{(2a^2  - b^2 )S - 2a}} \\
1610 > \end{array}.
1611 > \]
1612 >
1613 > \subsubsection{\label{introSection:resistanceTensorRegularArbitrary}The Resistance Tensor for Arbitrary Shape}
1614 >
1615 > Unlike spherical and other regular shaped molecules, there is not
1616 > analytical solution for friction tensor of any arbitrary shaped
1617 > rigid molecules. The ellipsoid of revolution model and general
1618 > triaxial ellipsoid model have been used to approximate the
1619 > hydrodynamic properties of rigid bodies. However, since the mapping
1620 > from all possible ellipsoidal space, $r$-space, to all possible
1621 > combination of rotational diffusion coefficients, $D$-space is not
1622 > unique\cite{Wegener79} as well as the intrinsic coupling between
1623 > translational and rotational motion of rigid body\cite{}, general
1624 > ellipsoid is not always suitable for modeling arbitrarily shaped
1625 > rigid molecule. A number of studies have been devoted to determine
1626 > the friction tensor for irregularly shaped rigid bodies using more
1627 > advanced method\cite{} where the molecule of interest was modeled by
1628 > combinations of spheres(beads)\cite{} and the hydrodynamics
1629 > properties of the molecule can be calculated using the hydrodynamic
1630 > interaction tensor. Let us consider a rigid assembly of $N$ beads
1631 > immersed in a continuous medium. Due to hydrodynamics interaction,
1632 > the ``net'' velocity of $i$th bead, $v'_i$ is different than its
1633 > unperturbed velocity $v_i$,
1634 > \[
1635 > v'_i  = v_i  - \sum\limits_{j \ne i} {T_{ij} F_j }
1636 > \]
1637 > where $F_i$ is the frictional force, and $T_{ij}$ is the
1638 > hydrodynamic interaction tensor. The friction force of $i$th bead is
1639 > proportional to its ``net'' velocity
1640 > \begin{equation}
1641 > F_i  = \zeta _i v_i  - \zeta _i \sum\limits_{j \ne i} {T_{ij} F_j }.
1642 > \label{introEquation:tensorExpression}
1643 > \end{equation}
1644 > This equation is the basis for deriving the hydrodynamic tensor. In
1645 > 1930, Oseen and Burgers gave a simple solution to Equation
1646 > \ref{introEquation:tensorExpression}
1647 > \begin{equation}
1648 > T_{ij}  = \frac{1}{{8\pi \eta r_{ij} }}\left( {I + \frac{{R_{ij}
1649 > R_{ij}^T }}{{R_{ij}^2 }}} \right).
1650 > \label{introEquation:oseenTensor}
1651 > \end{equation}
1652 > Here $R_{ij}$ is the distance vector between bead $i$ and bead $j$.
1653 > A second order expression for element of different size was
1654 > introduced by Rotne and Prager\cite{} and improved by Garc\'{i}a de
1655 > la Torre and Bloomfield,
1656 > \begin{equation}
1657 > T_{ij}  = \frac{1}{{8\pi \eta R_{ij} }}\left[ {\left( {I +
1658 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right) + R\frac{{\sigma
1659 > _i^2  + \sigma _j^2 }}{{r_{ij}^2 }}\left( {\frac{I}{3} -
1660 > \frac{{R_{ij} R_{ij}^T }}{{R_{ij}^2 }}} \right)} \right].
1661 > \label{introEquation:RPTensorNonOverlapped}
1662 > \end{equation}
1663 > Both of the Equation \ref{introEquation:oseenTensor} and Equation
1664 > \ref{introEquation:RPTensorNonOverlapped} have an assumption $R_{ij}
1665 > \ge \sigma _i  + \sigma _j$. An alternative expression for
1666 > overlapping beads with the same radius, $\sigma$, is given by
1667 > \begin{equation}
1668 > T_{ij}  = \frac{1}{{6\pi \eta R_{ij} }}\left[ {\left( {1 -
1669 > \frac{2}{{32}}\frac{{R_{ij} }}{\sigma }} \right)I +
1670 > \frac{2}{{32}}\frac{{R_{ij} R_{ij}^T }}{{R_{ij} \sigma }}} \right]
1671 > \label{introEquation:RPTensorOverlapped}
1672 > \end{equation}
1673 >
1674 > To calculate the resistance tensor at an arbitrary origin $O$, we
1675 > construct a $3N \times 3N$ matrix consisting of $N \times N$
1676 > $B_{ij}$ blocks
1677 > \begin{equation}
1678 > B = \left( {\begin{array}{*{20}c}
1679 >   {B_{11} } &  \ldots  & {B_{1N} }  \\
1680 >    \vdots  &  \ddots  &  \vdots   \\
1681 >   {B_{N1} } &  \cdots  & {B_{NN} }  \\
1682 > \end{array}} \right),
1683 > \end{equation}
1684 > where $B_{ij}$ is given by
1685 > \[
1686 > B_{ij}  = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij}
1687 > )T_{ij}
1688 > \]
1689 > where $\delta _{ij}$ is Kronecker delta function. Inverting matrix
1690 > $B$, we obtain
1691 >
1692 > \[
1693 > C = B^{ - 1}  = \left( {\begin{array}{*{20}c}
1694 >   {C_{11} } &  \ldots  & {C_{1N} }  \\
1695 >    \vdots  &  \ddots  &  \vdots   \\
1696 >   {C_{N1} } &  \cdots  & {C_{NN} }  \\
1697 > \end{array}} \right)
1698 > \]
1699 > , which can be partitioned into $N \times N$ $3 \times 3$ block
1700 > $C_{ij}$. With the help of $C_{ij}$ and skew matrix $U_i$
1701 > \[
1702 > U_i  = \left( {\begin{array}{*{20}c}
1703 >   0 & { - z_i } & {y_i }  \\
1704 >   {z_i } & 0 & { - x_i }  \\
1705 >   { - y_i } & {x_i } & 0  \\
1706 > \end{array}} \right)
1707 > \]
1708 > where $x_i$, $y_i$, $z_i$ are the components of the vector joining
1709 > bead $i$ and origin $O$. Hence, the elements of resistance tensor at
1710 > arbitrary origin $O$ can be written as
1711 > \begin{equation}
1712 > \begin{array}{l}
1713 > \Xi _{}^{tt}  = \sum\limits_i {\sum\limits_j {C_{ij} } } , \\
1714 > \Xi _{}^{tr}  = \Xi _{}^{rt}  = \sum\limits_i {\sum\limits_j {U_i C_{ij} } } , \\
1715 > \Xi _{}^{rr}  =  - \sum\limits_i {\sum\limits_j {U_i C_{ij} } } U_j  \\
1716 > \end{array}
1717 > \label{introEquation:ResistanceTensorArbitraryOrigin}
1718 > \end{equation}
1719 >
1720 > The resistance tensor depends on the origin to which they refer. The
1721 > proper location for applying friction force is the center of
1722 > resistance (reaction), at which the trace of rotational resistance
1723 > tensor, $ \Xi ^{rr}$ reaches minimum. Mathematically, the center of
1724 > resistance is defined as an unique point of the rigid body at which
1725 > the translation-rotation coupling tensor are symmetric,
1726 > \begin{equation}
1727 > \Xi^{tr}  = \left( {\Xi^{tr} } \right)^T
1728 > \label{introEquation:definitionCR}
1729 > \end{equation}
1730 > Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin},
1731 > we can easily find out that the translational resistance tensor is
1732 > origin independent, while the rotational resistance tensor and
1733 > translation-rotation coupling resistance tensor depend on the
1734 > origin. Given resistance tensor at an arbitrary origin $O$, and a
1735 > vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can
1736 > obtain the resistance tensor at $P$ by
1737 > \begin{equation}
1738 > \begin{array}{l}
1739 > \Xi _P^{tt}  = \Xi _O^{tt}  \\
1740 > \Xi _P^{tr}  = \Xi _P^{rt}  = \Xi _O^{tr}  - U_{OP} \Xi _O^{tt}  \\
1741 > \Xi _P^{rr}  = \Xi _O^{rr}  - U_{OP} \Xi _O^{tt} U_{OP}  + \Xi _O^{tr} U_{OP}  - U_{OP} \Xi _O^{tr} ^{^T }  \\
1742 > \end{array}
1743 > \label{introEquation:resistanceTensorTransformation}
1744 > \end{equation}
1745 > where
1746 > \[
1747 > U_{OP}  = \left( {\begin{array}{*{20}c}
1748 >   0 & { - z_{OP} } & {y_{OP} }  \\
1749 >   {z_i } & 0 & { - x_{OP} }  \\
1750 >   { - y_{OP} } & {x_{OP} } & 0  \\
1751 > \end{array}} \right)
1752 > \]
1753 > Using Equations \ref{introEquation:definitionCR} and
1754 > \ref{introEquation:resistanceTensorTransformation}, one can locate
1755 > the position of center of resistance,
1756 > \[
1757 > \left( \begin{array}{l}
1758 > x_{OR}  \\
1759 > y_{OR}  \\
1760 > z_{OR}  \\
1761 > \end{array} \right) = \left( {\begin{array}{*{20}c}
1762 >   {(\Xi _O^{rr} )_{yy}  + (\Xi _O^{rr} )_{zz} } & { - (\Xi _O^{rr} )_{xy} } & { - (\Xi _O^{rr} )_{xz} }  \\
1763 >   { - (\Xi _O^{rr} )_{xy} } & {(\Xi _O^{rr} )_{zz}  + (\Xi _O^{rr} )_{xx} } & { - (\Xi _O^{rr} )_{yz} }  \\
1764 >   { - (\Xi _O^{rr} )_{xz} } & { - (\Xi _O^{rr} )_{yz} } & {(\Xi _O^{rr} )_{xx}  + (\Xi _O^{rr} )_{yy} }  \\
1765 > \end{array}} \right)^{ - 1} \left( \begin{array}{l}
1766 > (\Xi _O^{tr} )_{yz}  - (\Xi _O^{tr} )_{zy}  \\
1767 > (\Xi _O^{tr} )_{zx}  - (\Xi _O^{tr} )_{xz}  \\
1768 > (\Xi _O^{tr} )_{xy}  - (\Xi _O^{tr} )_{yx}  \\
1769 > \end{array} \right).
1770 > \]
1771 > where $x_OR$, $y_OR$, $z_OR$ are the components of the vector
1772 > joining center of resistance $R$ and origin $O$.
1773 >
1774 > %\section{\label{introSection:correlationFunctions}Correlation Functions}

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