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# Line 27 | Line 27 | $F_ij$ be the force that particle $i$ exerts on partic
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 394 | Line 394 | distribution,
394   \begin{equation}
395   \rho  \propto e^{ - \beta H}
396   \label{introEquation:densityAndHamiltonian}
397 + \end{equation}
398 +
399 + \subsubsection{\label{introSection:phaseSpaceConservation}Conservation of Phase Space}
400 + Lets consider a region in the phase space,
401 + \begin{equation}
402 + \delta v = \int { \ldots \int {dq_1 } ...dq_f dp_1 } ..dp_f .
403 + \end{equation}
404 + If this region is small enough, the density $\rho$ can be regarded
405 + as uniform over the whole phase space. Thus, the number of phase
406 + points inside this region is given by,
407 + \begin{equation}
408 + \delta N = \rho \delta v = \rho \int { \ldots \int {dq_1 } ...dq_f
409 + dp_1 } ..dp_f.
410   \end{equation}
411  
412 + \begin{equation}
413 + \frac{{d(\delta N)}}{{dt}} = \frac{{d\rho }}{{dt}}\delta v + \rho
414 + \frac{d}{{dt}}(\delta v) = 0.
415 + \end{equation}
416 + With the help of stationary assumption
417 + (\ref{introEquation:stationary}), we obtain the principle of the
418 + \emph{conservation of extension in phase space},
419 + \begin{equation}
420 + \frac{d}{{dt}}(\delta v) = \frac{d}{{dt}}\int { \ldots \int {dq_1 }
421 + ...dq_f dp_1 } ..dp_f  = 0.
422 + \label{introEquation:volumePreserving}
423 + \end{equation}
424 +
425 + \subsubsection{\label{introSection:liouvilleInOtherForms}Liouville's Theorem in Other Forms}
426 +
427   Liouville's theorem can be expresses in a variety of different forms
428   which are convenient within different contexts. For any two function
429   $F$ and $G$ of the coordinates and momenta of a system, the Poisson
# Line 431 | Line 459 | expressed as
459   \label{introEquation:liouvilleTheoremInOperator}
460   \end{equation}
461  
434
462   \subsection{\label{introSection:ergodic}The Ergodic Hypothesis}
463  
464   Various thermodynamic properties can be calculated from Molecular
# Line 560 | Line 587 | H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \f
587   }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
588   \end{equation}
589  
590 < \subsection{\label{introSection:geometricProperties}Geometric Properties}
590 > \subsection{\label{introSection:exactFlow}Exact Flow}
591 >
592   Let $x(t)$ be the exact solution of the ODE system,
593   \begin{equation}
594   \frac{{dx}}{{dt}} = f(x) \label{introEquation:ODE}
# Line 570 | Line 598 | space to itself. In most cases, it is not easy to find
598   x(t+\tau) =\varphi_\tau(x(t))
599   \]
600   where $\tau$ is a fixed time step and $\varphi$ is a map from phase
601 < space to itself. In most cases, it is not easy to find the exact
602 < flow $\varphi_\tau$. Instead, we use a approximate map, $\psi_\tau$,
603 < which is usually called integrator. The order of an integrator
604 < $\psi_\tau$ is $p$, if the Taylor series of $\psi_\tau$ agree to
605 < order $p$,
601 > space to itself. The flow has the continuous group property,
602 > \begin{equation}
603 > \varphi _{\tau _1 }  \circ \varphi _{\tau _2 }  = \varphi _{\tau _1
604 > + \tau _2 } .
605 > \end{equation}
606 > In particular,
607 > \begin{equation}
608 > \varphi _\tau   \circ \varphi _{ - \tau }  = I
609 > \end{equation}
610 > Therefore, the exact flow is self-adjoint,
611   \begin{equation}
612 + \varphi _\tau   = \varphi _{ - \tau }^{ - 1}.
613 + \end{equation}
614 + The exact flow can also be written in terms of the of an operator,
615 + \begin{equation}
616 + \varphi _\tau  (x) = e^{\tau \sum\limits_i {f_i (x)\frac{\partial
617 + }{{\partial x_i }}} } (x) \equiv \exp (\tau f)(x).
618 + \label{introEquation:exponentialOperator}
619 + \end{equation}
620 +
621 + In most cases, it is not easy to find the exact flow $\varphi_\tau$.
622 + Instead, we use a approximate map, $\psi_\tau$, which is usually
623 + called integrator. The order of an integrator $\psi_\tau$ is $p$, if
624 + the Taylor series of $\psi_\tau$ agree to order $p$,
625 + \begin{equation}
626   \psi_tau(x) = x + \tau f(x) + O(\tau^{p+1})
627   \end{equation}
628  
629 + \subsection{\label{introSection:geometricProperties}Geometric Properties}
630 +
631   The hidden geometric properties of ODE and its flow play important
632 < roles in numerical studies. Let $\varphi$ be the flow of Hamiltonian
633 < vector field, $\varphi$ is a \emph{symplectic} flow if it satisfies,
632 > roles in numerical studies. Many of them can be found in systems
633 > which occur naturally in applications.
634 >
635 > Let $\varphi$ be the flow of Hamiltonian vector field, $\varphi$ is
636 > a \emph{symplectic} flow if it satisfies,
637   \begin{equation}
638 < '\varphi^T J '\varphi = J.
638 > {\varphi '}^T J \varphi ' = J.
639   \end{equation}
640   According to Liouville's theorem, the symplectic volume is invariant
641   under a Hamiltonian flow, which is the basis for classical
# Line 591 | Line 643 | symplectomorphism. As to the Poisson system,
643   field on a symplectic manifold can be shown to be a
644   symplectomorphism. As to the Poisson system,
645   \begin{equation}
646 < '\varphi ^T J '\varphi  = J \circ \varphi
646 > {\varphi '}^T J \varphi ' = J \circ \varphi
647   \end{equation}
648 < is the property must be preserved by the integrator. It is possible
649 < to construct a \emph{volume-preserving} flow for a source free($
650 < \nabla \cdot f = 0 $) ODE, if the flow satisfies $ \det d\varphi  =
651 < 1$. Changing the variables $y = h(x)$ in a
652 < ODE\ref{introEquation:ODE} will result in a new system,
648 > is the property must be preserved by the integrator.
649 >
650 > It is possible to construct a \emph{volume-preserving} flow for a
651 > source free($ \nabla \cdot f = 0 $) ODE, if the flow satisfies $
652 > \det d\varphi  = 1$. One can show easily that a symplectic flow will
653 > be volume-preserving.
654 >
655 > Changing the variables $y = h(x)$ in a ODE\ref{introEquation:ODE}
656 > will result in a new system,
657   \[
658   \dot y = \tilde f(y) = ((dh \cdot f)h^{ - 1} )(y).
659   \]
660   The vector filed $f$ has reversing symmetry $h$ if $f = - \tilde f$.
661   In other words, the flow of this vector field is reversible if and
662 < only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $. When
607 < designing any numerical methods, one should always try to preserve
608 < the structural properties of the original ODE and its flow.
662 > only if $ h \circ \varphi ^{ - 1}  = \varphi  \circ h $.
663  
664 + A \emph{first integral}, or conserved quantity of a general
665 + differential function is a function $ G:R^{2d}  \to R^d $ which is
666 + constant for all solutions of the ODE $\frac{{dx}}{{dt}} = f(x)$ ,
667 + \[
668 + \frac{{dG(x(t))}}{{dt}} = 0.
669 + \]
670 + Using chain rule, one may obtain,
671 + \[
672 + \sum\limits_i {\frac{{dG}}{{dx_i }}} f_i (x) = f \bullet \nabla G,
673 + \]
674 + which is the condition for conserving \emph{first integral}. For a
675 + canonical Hamiltonian system, the time evolution of an arbitrary
676 + smooth function $G$ is given by,
677 + \begin{equation}
678 + \begin{array}{c}
679 + \frac{{dG(x(t))}}{{dt}} = [\nabla _x G(x(t))]^T \dot x(t) \\
680 +  = [\nabla _x G(x(t))]^T J\nabla _x H(x(t)). \\
681 + \end{array}
682 + \label{introEquation:firstIntegral1}
683 + \end{equation}
684 + Using poisson bracket notion, Equation
685 + \ref{introEquation:firstIntegral1} can be rewritten as
686 + \[
687 + \frac{d}{{dt}}G(x(t)) = \left\{ {G,H} \right\}(x(t)).
688 + \]
689 + Therefore, the sufficient condition for $G$ to be the \emph{first
690 + integral} of a Hamiltonian system is
691 + \[
692 + \left\{ {G,H} \right\} = 0.
693 + \]
694 + As well known, the Hamiltonian (or energy) H of a Hamiltonian system
695 + is a \emph{first integral}, which is due to the fact $\{ H,H\}  =
696 + 0$.
697 +
698 +
699 + When designing any numerical methods, one should always try to
700 + preserve the structural properties of the original ODE and its flow.
701 +
702   \subsection{\label{introSection:constructionSymplectic}Construction of Symplectic Methods}
703   A lot of well established and very effective numerical methods have
704   been successful precisely because of their symplecticities even
# Line 622 | Line 714 | and difficult to use\cite{}. In dissipative systems, v
714   \end{enumerate}
715  
716   Generating function tends to lead to methods which are cumbersome
717 < and difficult to use\cite{}. In dissipative systems, variational
718 < methods can capture the decay of energy accurately\cite{}. Since
719 < their geometrically unstable nature against non-Hamiltonian
720 < perturbations, ordinary implicit Runge-Kutta methods are not
721 < suitable for Hamiltonian system. Recently, various high-order
722 < explicit Runge--Kutta methods have been developed to overcome this
723 < instability \cite{}. However, due to computational penalty involved
724 < in implementing the Runge-Kutta methods, they do not attract too
725 < much attention from Molecular Dynamics community. Instead, splitting
726 < have been widely accepted since they exploit natural decompositions
727 < of the system\cite{Tuckerman92}. The main idea behind splitting
728 < methods is to decompose the discrete $\varphi_h$ as a composition of
729 < simpler flows,
717 > and difficult to use. In dissipative systems, variational methods
718 > can capture the decay of energy accurately. Since their
719 > geometrically unstable nature against non-Hamiltonian perturbations,
720 > ordinary implicit Runge-Kutta methods are not suitable for
721 > Hamiltonian system. Recently, various high-order explicit
722 > Runge--Kutta methods have been developed to overcome this
723 > instability. However, due to computational penalty involved in
724 > implementing the Runge-Kutta methods, they do not attract too much
725 > attention from Molecular Dynamics community. Instead, splitting have
726 > been widely accepted since they exploit natural decompositions of
727 > the system\cite{Tuckerman92}.
728 >
729 > \subsubsection{\label{introSection:splittingMethod}Splitting Method}
730 >
731 > The main idea behind splitting methods is to decompose the discrete
732 > $\varphi_h$ as a composition of simpler flows,
733   \begin{equation}
734   \varphi _h  = \varphi _{h_1 }  \circ \varphi _{h_2 }  \ldots  \circ
735   \varphi _{h_n }
736   \label{introEquation:FlowDecomposition}
737   \end{equation}
738   where each of the sub-flow is chosen such that each represent a
739 < simpler integration of the system. Let $\phi$ and $\psi$ both be
740 < symplectic maps, it is easy to show that any composition of
741 < symplectic flows yields a symplectic map,
739 > simpler integration of the system.
740 >
741 > Suppose that a Hamiltonian system takes the form,
742 > \[
743 > H = H_1 + H_2.
744 > \]
745 > Here, $H_1$ and $H_2$ may represent different physical processes of
746 > the system. For instance, they may relate to kinetic and potential
747 > energy respectively, which is a natural decomposition of the
748 > problem. If $H_1$ and $H_2$ can be integrated using exact flows
749 > $\varphi_1(t)$ and $\varphi_2(t)$, respectively, a simple first
750 > order is then given by the Lie-Trotter formula
751 > \begin{equation}
752 > \varphi _h  = \varphi _{1,h}  \circ \varphi _{2,h},
753 > \label{introEquation:firstOrderSplitting}
754 > \end{equation}
755 > where $\varphi _h$ is the result of applying the corresponding
756 > continuous $\varphi _i$ over a time $h$. By definition, as
757 > $\varphi_i(t)$ is the exact solution of a Hamiltonian system, it
758 > must follow that each operator $\varphi_i(t)$ is a symplectic map.
759 > It is easy to show that any composition of symplectic flows yields a
760 > symplectic map,
761   \begin{equation}
762   (\varphi '\phi ')^T J\varphi '\phi ' = \phi '^T \varphi '^T J\varphi
763 < '\phi ' = \phi '^T J\phi ' = J.
763 > '\phi ' = \phi '^T J\phi ' = J,
764   \label{introEquation:SymplecticFlowComposition}
765   \end{equation}
766 < Suppose that a Hamiltonian system has a form with $H = T + V$
766 > where $\phi$ and $\psi$ both are symplectic maps. Thus operator
767 > splitting in this context automatically generates a symplectic map.
768 >
769 > The Lie-Trotter splitting(\ref{introEquation:firstOrderSplitting})
770 > introduces local errors proportional to $h^2$, while Strang
771 > splitting gives a second-order decomposition,
772 > \begin{equation}
773 > \varphi _h  = \varphi _{1,h/2}  \circ \varphi _{2,h}  \circ \varphi
774 > _{1,h/2} ,
775 > \label{introEqaution:secondOrderSplitting}
776 > \end{equation}
777 > which has a local error proportional to $h^3$. Sprang splitting's
778 > popularity in molecular simulation community attribute to its
779 > symmetric property,
780 > \begin{equation}
781 > \varphi _h^{ - 1} = \varphi _{ - h}.
782 > \label{introEquation:timeReversible}
783 > \end{equation}
784 >
785 > \subsubsection{\label{introSection:exampleSplittingMethod}Example of Splitting Method}
786 > The classical equation for a system consisting of interacting
787 > particles can be written in Hamiltonian form,
788 > \[
789 > H = T + V
790 > \]
791 > where $T$ is the kinetic energy and $V$ is the potential energy.
792 > Setting $H_1 = T, H_2 = V$ and applying Strang splitting, one
793 > obtains the following:
794 > \begin{align}
795 > q(\Delta t) &= q(0) + \dot{q}(0)\Delta t +
796 >    \frac{F[q(0)]}{m}\frac{\Delta t^2}{2}, %
797 > \label{introEquation:Lp10a} \\%
798 > %
799 > \dot{q}(\Delta t) &= \dot{q}(0) + \frac{\Delta t}{2m}
800 >    \biggl [F[q(0)] + F[q(\Delta t)] \biggr]. %
801 > \label{introEquation:Lp10b}
802 > \end{align}
803 > where $F(t)$ is the force at time $t$. This integration scheme is
804 > known as \emph{velocity verlet} which is
805 > symplectic(\ref{introEquation:SymplecticFlowComposition}),
806 > time-reversible(\ref{introEquation:timeReversible}) and
807 > volume-preserving (\ref{introEquation:volumePreserving}). These
808 > geometric properties attribute to its long-time stability and its
809 > popularity in the community. However, the most commonly used
810 > velocity verlet integration scheme is written as below,
811 > \begin{align}
812 > \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) &=
813 >    \dot{q}(0) + \frac{\Delta t}{2m}\, F[q(0)], \label{introEquation:Lp9a}\\%
814 > %
815 > q(\Delta t) &= q(0) + \Delta t\, \dot{q}\biggl (\frac{\Delta t}{2}\biggr ),%
816 >    \label{introEquation:Lp9b}\\%
817 > %
818 > \dot{q}(\Delta t) &= \dot{q}\biggl (\frac{\Delta t}{2}\biggr ) +
819 >    \frac{\Delta t}{2m}\, F[q(0)]. \label{introEquation:Lp9c}
820 > \end{align}
821 > From the preceding splitting, one can see that the integration of
822 > the equations of motion would follow:
823 > \begin{enumerate}
824 > \item calculate the velocities at the half step, $\frac{\Delta t}{2}$, from the forces calculated at the initial position.
825 >
826 > \item Use the half step velocities to move positions one whole step, $\Delta t$.
827 >
828 > \item Evaluate the forces at the new positions, $\mathbf{r}(\Delta t)$, and use the new forces to complete the velocity move.
829 >
830 > \item Repeat from step 1 with the new position, velocities, and forces assuming the roles of the initial values.
831 > \end{enumerate}
832 >
833 > Simply switching the order of splitting and composing, a new
834 > integrator, the \emph{position verlet} integrator, can be generated,
835 > \begin{align}
836 > \dot q(\Delta t) &= \dot q(0) + \Delta tF(q(0))\left[ {q(0) +
837 > \frac{{\Delta t}}{{2m}}\dot q(0)} \right], %
838 > \label{introEquation:positionVerlet1} \\%
839 > %
840 > q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
841 > q(\Delta t)} \right]. %
842 > \label{introEquation:positionVerlet1}
843 > \end{align}
844 >
845 > \subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods}
846 >
847 > Baker-Campbell-Hausdorff formula can be used to determine the local
848 > error of splitting method in terms of commutator of the
849 > operators(\ref{introEquation:exponentialOperator}) associated with
850 > the sub-flow. For operators $hX$ and $hY$ which are associate to
851 > $\varphi_1(t)$ and $\varphi_2(t$ respectively , we have
852 > \begin{equation}
853 > \exp (hX + hY) = \exp (hZ)
854 > \end{equation}
855 > where
856 > \begin{equation}
857 > hZ = hX + hY + \frac{{h^2 }}{2}[X,Y] + \frac{{h^3 }}{2}\left(
858 > {[X,[X,Y]] + [Y,[Y,X]]} \right) +  \ldots .
859 > \end{equation}
860 > Here, $[X,Y]$ is the commutators of operator $X$ and $Y$ given by
861 > \[
862 > [X,Y] = XY - YX .
863 > \]
864 > Applying Baker-Campbell-Hausdorff formula to Sprang splitting, we
865 > can obtain
866 > \begin{eqnarray*}
867 > \exp (h X/2)\exp (h Y)\exp (h X/2) & = & \exp (h X + h Y + h^2
868 > [X,Y]/4 + h^2 [Y,X]/4 \\ & & \mbox{} + h^2 [X,X]/8 + h^2 [Y,Y]/8 \\
869 > & & \mbox{} + h^3 [Y,[Y,X]]/12 - h^3 [X,[X,Y]]/24 & & \mbox{} +
870 > \ldots )
871 > \end{eqnarray*}
872 > Since \[ [X,Y] + [Y,X] = 0\] and \[ [X,X] = 0\], the dominant local
873 > error of Spring splitting is proportional to $h^3$. The same
874 > procedure can be applied to general splitting,  of the form
875 > \begin{equation}
876 > \varphi _{b_m h}^2  \circ \varphi _{a_m h}^1  \circ \varphi _{b_{m -
877 > 1} h}^2  \circ  \ldots  \circ \varphi _{a_1 h}^1 .
878 > \end{equation}
879 > Careful choice of coefficient $a_1 ,\ldot , b_m$ will lead to higher
880 > order method. Yoshida proposed an elegant way to compose higher
881 > order methods based on symmetric splitting. Given a symmetric second
882 > order base method $ \varphi _h^{(2)} $, a fourth-order symmetric
883 > method can be constructed by composing,
884 > \[
885 > \varphi _h^{(4)}  = \varphi _{\alpha h}^{(2)}  \circ \varphi _{\beta
886 > h}^{(2)}  \circ \varphi _{\alpha h}^{(2)}
887 > \]
888 > where $ \alpha  =  - \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$ and $ \beta
889 > = \frac{{2^{1/3} }}{{2 - 2^{1/3} }}$. Moreover, a symmetric
890 > integrator $ \varphi _h^{(2n + 2)}$ can be composed by
891 > \begin{equation}
892 > \varphi _h^{(2n + 2)}  = \varphi _{\alpha h}^{(2n)}  \circ \varphi
893 > _{\beta h}^{(2n)}  \circ \varphi _{\alpha h}^{(2n)}
894 > \end{equation}
895 > , if the weights are chosen as
896 > \[
897 > \alpha  =  - \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }},\beta =
898 > \frac{{2^{1/(2n + 1)} }}{{2 - 2^{1/(2n + 1)} }} .
899 > \]
900  
901   \section{\label{introSection:molecularDynamics}Molecular Dynamics}
902  
# Line 660 | Line 907 | dynamical information.
907  
908   \subsection{\label{introSec:mdInit}Initialization}
909  
910 + \subsection{\label{introSec:forceEvaluation}Force Evaluation}
911 +
912   \subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion}
913  
914   \section{\label{introSection:rigidBody}Dynamics of Rigid Bodies}
915  
916 < A rigid body is a body in which the distance between any two given
917 < points of a rigid body remains constant regardless of external
918 < forces exerted on it. A rigid body therefore conserves its shape
919 < during its motion.
916 > Rigid bodies are frequently involved in the modeling of different
917 > areas, from engineering, physics, to chemistry. For example,
918 > missiles and vehicle are usually modeled by rigid bodies.  The
919 > movement of the objects in 3D gaming engine or other physics
920 > simulator is governed by the rigid body dynamics. In molecular
921 > simulation, rigid body is used to simplify the model in
922 > protein-protein docking study{\cite{Gray03}}.
923  
924 < Applications of dynamics of rigid bodies.
924 > It is very important to develop stable and efficient methods to
925 > integrate the equations of motion of orientational degrees of
926 > freedom. Euler angles are the nature choice to describe the
927 > rotational degrees of freedom. However, due to its singularity, the
928 > numerical integration of corresponding equations of motion is very
929 > inefficient and inaccurate. Although an alternative integrator using
930 > different sets of Euler angles can overcome this difficulty\cite{},
931 > the computational penalty and the lost of angular momentum
932 > conservation still remain. A singularity free representation
933 > utilizing quaternions was developed by Evans in 1977. Unfortunately,
934 > this approach suffer from the nonseparable Hamiltonian resulted from
935 > quaternion representation, which prevents the symplectic algorithm
936 > to be utilized. Another different approach is to apply holonomic
937 > constraints to the atoms belonging to the rigid body. Each atom
938 > moves independently under the normal forces deriving from potential
939 > energy and constraint forces which are used to guarantee the
940 > rigidness. However, due to their iterative nature, SHAKE and Rattle
941 > algorithm converge very slowly when the number of constraint
942 > increases.
943  
944 + The break through in geometric literature suggests that, in order to
945 + develop a long-term integration scheme, one should preserve the
946 + symplectic structure of the flow. Introducing conjugate momentum to
947 + rotation matrix $A$ and re-formulating Hamiltonian's equation, a
948 + symplectic integrator, RSHAKE, was proposed to evolve the
949 + Hamiltonian system in a constraint manifold by iteratively
950 + satisfying the orthogonality constraint $A_t A = 1$. An alternative
951 + method using quaternion representation was developed by Omelyan.
952 + However, both of these methods are iterative and inefficient. In
953 + this section, we will present a symplectic Lie-Poisson integrator
954 + for rigid body developed by Dullweber and his coworkers\cite{}.
955 +
956   \subsection{\label{introSection:lieAlgebra}Lie Algebra}
957  
958   \subsection{\label{introSection:DLMMotionEquation}The Euler Equations of Rigid Body Motion}
959  
960   \subsection{\label{introSection:otherRBMotionEquation}Other Formulations for Rigid Body Motion}
961  
680 %\subsection{\label{introSection:poissonBrackets}Poisson Brackets}
681
962   \section{\label{introSection:correlationFunctions}Correlation Functions}
963  
964   \section{\label{introSection:langevinDynamics}Langevin Dynamics}
# Line 729 | Line 1009 | introEquation:motionHamiltonianMomentum},
1009   \dot p &=  - \frac{{\partial H}}{{\partial x}}
1010         &= m\ddot x
1011         &= - \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)}
1012 < \label{introEq:Lp5}
1012 > \label{introEquation:Lp5}
1013   \end{align}
1014   , and
1015   \begin{align}

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