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# Line 67 | Line 67 | All of these conserved quantities are important factor
67   \begin{equation}E = T + V. \label{introEquation:energyConservation}
68   \end{equation}
69   All of these conserved quantities are important factors to determine
70 < the quality of numerical integration schemes for rigid bodies
71 < \cite{Dullweber1997}.
70 > the quality of numerical integration schemes for rigid
71 > bodies.\cite{Dullweber1997}
72  
73   \subsection{\label{introSection:lagrangian}Lagrangian Mechanics}
74  
# Line 178 | Line 178 | equation of motion. Due to their symmetrical formula,
178   where Eq.~\ref{introEquation:motionHamiltonianCoordinate} and
179   Eq.~\ref{introEquation:motionHamiltonianMomentum} are Hamilton's
180   equation of motion. Due to their symmetrical formula, they are also
181 < known as the canonical equations of motions \cite{Goldstein2001}.
181 > known as the canonical equations of motions.\cite{Goldstein2001}
182  
183   An important difference between Lagrangian approach and the
184   Hamiltonian approach is that the Lagrangian is considered to be a
# Line 188 | Line 188 | coordinate and its time derivative as independent vari
188   Hamiltonian Mechanics is more appropriate for application to
189   statistical mechanics and quantum mechanics, since it treats the
190   coordinate and its time derivative as independent variables and it
191 < only works with 1st-order differential equations\cite{Marion1990}.
191 > only works with 1st-order differential equations.\cite{Marion1990}
192   In Newtonian Mechanics, a system described by conservative forces
193   conserves the total energy
194   (Eq.~\ref{introEquation:energyConservation}). It follows that
# Line 416 | Line 416 | average. It states that the time average and average o
416   many-body system in Statistical Mechanics. Fortunately, the Ergodic
417   Hypothesis makes a connection between time average and the ensemble
418   average. It states that the time average and average over the
419 < statistical ensemble are identical \cite{Frenkel1996, Leach2001}:
419 > statistical ensemble are identical:\cite{Frenkel1996, Leach2001}
420   \begin{equation}
421   \langle A(q , p) \rangle_t = \mathop {\lim }\limits_{t \to \infty }
422   \frac{1}{t}\int\limits_0^t {A(q(t),p(t))dt = \int\limits_\Gamma
# Line 434 | Line 434 | Sec.~\ref{introSection:molecularDynamics} will be the
434   utilized. Or if the system lends itself to a time averaging
435   approach, the Molecular Dynamics techniques in
436   Sec.~\ref{introSection:molecularDynamics} will be the best
437 < choice\cite{Frenkel1996}.
437 > choice.\cite{Frenkel1996}
438  
439   \section{\label{introSection:geometricIntegratos}Geometric Integrators}
440   A variety of numerical integrators have been proposed to simulate
441   the motions of atoms in MD simulation. They usually begin with
442 < initial conditions and move the objects in the direction governed
443 < by the differential equations. However, most of them ignore the
444 < hidden physical laws contained within the equations. Since 1990,
445 < geometric integrators, which preserve various phase-flow invariants
446 < such as symplectic structure, volume and time reversal symmetry,
447 < were developed to address this issue\cite{Dullweber1997,
448 < McLachlan1998, Leimkuhler1999}. The velocity Verlet method, which
449 < happens to be a simple example of symplectic integrator, continues
450 < to gain popularity in the molecular dynamics community. This fact
451 < can be partly explained by its geometric nature.
442 > initial conditions and move the objects in the direction governed by
443 > the differential equations. However, most of them ignore the hidden
444 > physical laws contained within the equations. Since 1990, geometric
445 > integrators, which preserve various phase-flow invariants such as
446 > symplectic structure, volume and time reversal symmetry, were
447 > developed to address this issue.\cite{Dullweber1997, McLachlan1998,
448 > Leimkuhler1999} The velocity Verlet method, which happens to be a
449 > simple example of symplectic integrator, continues to gain
450 > popularity in the molecular dynamics community. This fact can be
451 > partly explained by its geometric nature.
452  
453   \subsection{\label{introSection:symplecticManifold}Symplectic Manifolds}
454   A \emph{manifold} is an abstract mathematical space. It looks
# Line 457 | Line 457 | viewed as a whole. A \emph{differentiable manifold} (a
457   surface of Earth. It seems to be flat locally, but it is round if
458   viewed as a whole. A \emph{differentiable manifold} (also known as
459   \emph{smooth manifold}) is a manifold on which it is possible to
460 < apply calculus\cite{Hirsch1997}. A \emph{symplectic manifold} is
460 > apply calculus.\cite{Hirsch1997} A \emph{symplectic manifold} is
461   defined as a pair $(M, \omega)$ which consists of a
462   \emph{differentiable manifold} $M$ and a close, non-degenerate,
463   bilinear symplectic form, $\omega$. A symplectic form on a vector
464   space $V$ is a function $\omega(x, y)$ which satisfies
465   $\omega(\lambda_1x_1+\lambda_2x_2, y) = \lambda_1\omega(x_1, y)+
466   \lambda_2\omega(x_2, y)$, $\omega(x, y) = - \omega(y, x)$ and
467 < $\omega(x, x) = 0$\cite{McDuff1998}. The cross product operation in
467 > $\omega(x, x) = 0$.\cite{McDuff1998} The cross product operation in
468   vector field is an example of symplectic form. One of the
469   motivations to study \emph{symplectic manifolds} in Hamiltonian
470   Mechanics is that a symplectic manifold can represent all possible
471   configurations of the system and the phase space of the system can
472 < be described by it's cotangent bundle\cite{Jost2002}. Every
472 > be described by it's cotangent bundle.\cite{Jost2002} Every
473   symplectic manifold is even dimensional. For instance, in Hamilton
474   equations, coordinate and momentum always appear in pairs.
475  
# Line 496 | Line 496 | called a \emph{Hamiltonian vector field}. Another gene
496   \label{introEquation:compactHamiltonian}
497   \end{equation}In this case, $f$ is
498   called a \emph{Hamiltonian vector field}. Another generalization of
499 < Hamiltonian dynamics is Poisson Dynamics\cite{Olver1986},
499 > Hamiltonian dynamics is Poisson Dynamics,\cite{Olver1986}
500   \begin{equation}
501   \dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian}
502   \end{equation}
503 < The most obvious change being that matrix $J$ now depends on $x$.
503 > where the most obvious change being that matrix $J$ now depends on
504 > $x$.
505  
506   \subsection{\label{introSection:exactFlow}Exact Propagator}
507  
# Line 620 | Line 621 | variational methods can capture the decay of energy
621   Generating functions\cite{Channell1990} tend to lead to methods
622   which are cumbersome and difficult to use. In dissipative systems,
623   variational methods can capture the decay of energy
624 < accurately\cite{Kane2000}. Since they are geometrically unstable
624 > accurately.\cite{Kane2000} Since they are geometrically unstable
625   against non-Hamiltonian perturbations, ordinary implicit Runge-Kutta
626 < methods are not suitable for Hamiltonian system. Recently, various
627 < high-order explicit Runge-Kutta methods \cite{Owren1992,Chen2003}
628 < have been developed to overcome this instability. However, due to
629 < computational penalty involved in implementing the Runge-Kutta
630 < methods, they have not attracted much attention from the Molecular
631 < Dynamics community. Instead, splitting methods have been widely
632 < accepted since they exploit natural decompositions of the
633 < system\cite{Tuckerman1992, McLachlan1998}.
626 > methods are not suitable for Hamiltonian
627 > system.\cite{Cartwright1992} Recently, various high-order explicit
628 > Runge-Kutta methods \cite{Owren1992,Chen2003} have been developed to
629 > overcome this instability. However, due to computational penalty
630 > involved in implementing the Runge-Kutta methods, they have not
631 > attracted much attention from the Molecular Dynamics community.
632 > Instead, splitting methods have been widely accepted since they
633 > exploit natural decompositions of the system.\cite{McLachlan1998,
634 > Tuckerman1992}
635  
636   \subsubsection{\label{introSection:splittingMethod}\textbf{Splitting Methods}}
637  
# Line 652 | Line 654 | simple first order expression is then given by the Lie
654   problem. If $H_1$ and $H_2$ can be integrated using exact
655   propagators $\varphi_1(t)$ and $\varphi_2(t)$, respectively, a
656   simple first order expression is then given by the Lie-Trotter
657 < formula
657 > formula\cite{Trotter1959}
658   \begin{equation}
659   \varphi _h  = \varphi _{1,h}  \circ \varphi _{2,h},
660   \label{introEquation:firstOrderSplitting}
# Line 673 | Line 675 | local errors proportional to $h^2$, while the Strang s
675   The Lie-Trotter
676   splitting(Eq.~\ref{introEquation:firstOrderSplitting}) introduces
677   local errors proportional to $h^2$, while the Strang splitting gives
678 < a second-order decomposition,
678 > a second-order decomposition,\cite{Strang1968}
679   \begin{equation}
680   \varphi _h  = \varphi _{1,h/2}  \circ \varphi _{2,h}  \circ \varphi
681   _{1,h/2} , \label{introEquation:secondOrderSplitting}
# Line 748 | Line 750 | q(\Delta t)} \right]. %
750  
751   \subsubsection{\label{introSection:errorAnalysis}\textbf{Error Analysis and Higher Order Methods}}
752  
753 < The Baker-Campbell-Hausdorff formula can be used to determine the
754 < local error of a splitting method in terms of the commutator of the
755 < operators(Eq.~\ref{introEquation:exponentialOperator}) associated with
756 < the sub-propagator. For operators $hX$ and $hY$ which are associated
757 < with $\varphi_1(t)$ and $\varphi_2(t)$ respectively , we have
753 > The Baker-Campbell-Hausdorff formula\cite{Gilmore1974} can be used
754 > to determine the local error of a splitting method in terms of the
755 > commutator of the operators associated with the sub-propagator. For
756 > operators $hX$ and $hY$ which are associated with $\varphi_1(t)$ and
757 > $\varphi_2(t)$ respectively , we have
758   \begin{equation}
759   \exp (hX + hY) = \exp (hZ)
760   \end{equation}
# Line 782 | Line 784 | order methods. Yoshida proposed an elegant way to comp
784   \end{equation}
785   A careful choice of coefficient $a_1 \ldots b_m$ will lead to higher
786   order methods. Yoshida proposed an elegant way to compose higher
787 < order methods based on symmetric splitting\cite{Yoshida1990}. Given
787 > order methods based on symmetric splitting.\cite{Yoshida1990} Given
788   a symmetric second order base method $ \varphi _h^{(2)} $, a
789   fourth-order symmetric method can be constructed by composing,
790   \[
# Line 943 | Line 945 | evaluation is to apply spherical cutoffs where particl
945   %cutoff and minimum image convention
946   Another important technique to improve the efficiency of force
947   evaluation is to apply spherical cutoffs where particles farther
948 < than a predetermined distance are not included in the calculation
949 < \cite{Frenkel1996}. The use of a cutoff radius will cause a
950 < discontinuity in the potential energy curve. Fortunately, one can
948 > than a predetermined distance are not included in the
949 > calculation.\cite{Frenkel1996} The use of a cutoff radius will cause
950 > a discontinuity in the potential energy curve. Fortunately, one can
951   shift a simple radial potential to ensure the potential curve go
952   smoothly to zero at the cutoff radius. The cutoff strategy works
953   well for Lennard-Jones interaction because of its short range
# Line 954 | Line 956 | with rapid and absolute convergence, has proved to min
956   in simulations. The Ewald summation, in which the slowly decaying
957   Coulomb potential is transformed into direct and reciprocal sums
958   with rapid and absolute convergence, has proved to minimize the
959 < periodicity artifacts in liquid simulations. Taking advantage
960 < of fast Fourier transform (FFT) techniques for calculating discrete Fourier
961 < transforms, the particle mesh-based
959 > periodicity artifacts in liquid simulations. Taking advantage of
960 > fast Fourier transform (FFT) techniques for calculating discrete
961 > Fourier transforms, the particle mesh-based
962   methods\cite{Hockney1981,Shimada1993, Luty1994} are accelerated from
963   $O(N^{3/2})$ to $O(N logN)$. An alternative approach is the
964   \emph{fast multipole method}\cite{Greengard1987, Greengard1994},
# Line 966 | Line 968 | charge-neutralized Coulomb potential method developed
968   simulation community, these two methods are difficult to implement
969   correctly and efficiently. Instead, we use a damped and
970   charge-neutralized Coulomb potential method developed by Wolf and
971 < his coworkers\cite{Wolf1999}. The shifted Coulomb potential for
971 > his coworkers.\cite{Wolf1999} The shifted Coulomb potential for
972   particle $i$ and particle $j$ at distance $r_{rj}$ is given by:
973   \begin{equation}
974   V(r_{ij})= \frac{q_i q_j \textrm{erfc}(\alpha
# Line 1029 | Line 1031 | Fourier transforming raw data from a series of neutron
1031   function}, is of most fundamental importance to liquid theory.
1032   Experimentally, pair distribution functions can be gathered by
1033   Fourier transforming raw data from a series of neutron diffraction
1034 < experiments and integrating over the surface factor
1035 < \cite{Powles1973}. The experimental results can serve as a criterion
1036 < to justify the correctness of a liquid model. Moreover, various
1037 < equilibrium thermodynamic and structural properties can also be
1038 < expressed in terms of the radial distribution function
1039 < \cite{Allen1987}. The pair distribution functions $g(r)$ gives the
1040 < probability that a particle $i$ will be located at a distance $r$
1041 < from a another particle $j$ in the system
1034 > experiments and integrating over the surface
1035 > factor.\cite{Powles1973} The experimental results can serve as a
1036 > criterion to justify the correctness of a liquid model. Moreover,
1037 > various equilibrium thermodynamic and structural properties can also
1038 > be expressed in terms of the radial distribution
1039 > function.\cite{Allen1987} The pair distribution functions $g(r)$
1040 > gives the probability that a particle $i$ will be located at a
1041 > distance $r$ from a another particle $j$ in the system
1042   \begin{equation}
1043   g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j
1044   \ne i} {\delta (r - r_{ij} )} } } \right\rangle = \frac{\rho
# Line 1097 | Line 1099 | simulations, rigid bodies are used to simplify protein
1099   movement of the objects in 3D gaming engines or other physics
1100   simulators is governed by rigid body dynamics. In molecular
1101   simulations, rigid bodies are used to simplify protein-protein
1102 < docking studies\cite{Gray2003}.
1102 > docking studies.\cite{Gray2003}
1103  
1104   It is very important to develop stable and efficient methods to
1105   integrate the equations of motion for orientational degrees of
# Line 1109 | Line 1111 | still remain. A singularity-free representation utiliz
1111   angles can overcome this difficulty\cite{Barojas1973}, the
1112   computational penalty and the loss of angular momentum conservation
1113   still remain. A singularity-free representation utilizing
1114 < quaternions was developed by Evans in 1977\cite{Evans1977}.
1114 > quaternions was developed by Evans in 1977.\cite{Evans1977}
1115   Unfortunately, this approach used a nonseparable Hamiltonian
1116   resulting from the quaternion representation, which prevented the
1117   symplectic algorithm from being utilized. Another different approach
# Line 1118 | Line 1120 | the SHAKE and Rattle algorithms also converge very slo
1120   deriving from potential energy and constraint forces which are used
1121   to guarantee the rigidness. However, due to their iterative nature,
1122   the SHAKE and Rattle algorithms also converge very slowly when the
1123 < number of constraints increases\cite{Ryckaert1977, Andersen1983}.
1123 > number of constraints increases.\cite{Ryckaert1977, Andersen1983}
1124  
1125   A break-through in geometric literature suggests that, in order to
1126   develop a long-term integration scheme, one should preserve the
# Line 1128 | Line 1130 | An alternative method using the quaternion representat
1130   proposed to evolve the Hamiltonian system in a constraint manifold
1131   by iteratively satisfying the orthogonality constraint $Q^T Q = 1$.
1132   An alternative method using the quaternion representation was
1133 < developed by Omelyan\cite{Omelyan1998}. However, both of these
1133 > developed by Omelyan.\cite{Omelyan1998} However, both of these
1134   methods are iterative and inefficient. In this section, we descibe a
1135   symplectic Lie-Poisson integrator for rigid bodies developed by
1136   Dullweber and his coworkers\cite{Dullweber1997} in depth.
1137  
1138   \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Bodies}
1139 < The Hamiltonian of a rigid body is given by
1139 > The Hamiltonian of a rigid body is given by
1140   \begin{equation}
1141   H = \frac{1}{2}(p^T m^{ - 1} p) + \frac{1}{2}tr(PJ^{ - 1} P) +
1142   V(q,Q) + \frac{1}{2}tr[(QQ^T  - 1)\Lambda ].
# Line 1248 | Line 1250 | motion. This unique property eliminates the requiremen
1250   Eq.~\ref{introEquation:skewMatrixPI} is zero, which implies the
1251   Lagrange multiplier $\Lambda$ is absent from the equations of
1252   motion. This unique property eliminates the requirement of
1253 < iterations which can not be avoided in other methods\cite{Kol1997,
1254 < Omelyan1998}. Applying the hat-map isomorphism, we obtain the
1253 > iterations which can not be avoided in other methods.\cite{Kol1997,
1254 > Omelyan1998} Applying the hat-map isomorphism, we obtain the
1255   equation of motion for angular momentum in the body frame
1256   \begin{equation}
1257   \dot \pi  = \pi  \times I^{ - 1} \pi  + \sum\limits_i {\left( {Q^T
# Line 1355 | Line 1357 | norm of the angular momentum, $\parallel \pi
1357   function $G$ is zero, $F$ is a \emph{Casimir}, which is the
1358   conserved quantity in Poisson system. We can easily verify that the
1359   norm of the angular momentum, $\parallel \pi
1360 < \parallel$, is a \emph{Casimir}\cite{McLachlan1993}. Let $F(\pi ) = S(\frac{{\parallel
1360 > \parallel$, is a \emph{Casimir}.\cite{McLachlan1993} Let $F(\pi ) = S(\frac{{\parallel
1361   \pi \parallel ^2 }}{2})$ for an arbitrary function $ S:R \to R$ ,
1362   then by the chain rule
1363   \[
# Line 1376 | Line 1378 | of motion corresponding to potential energy and kineti
1378   The Hamiltonian of rigid body can be separated in terms of kinetic
1379   energy and potential energy, $H = T(p,\pi ) + V(q,Q)$. The equations
1380   of motion corresponding to potential energy and kinetic energy are
1381 < listed in Table~\ref{introTable:rbEquations}.
1381 > listed in Table~\ref{introTable:rbEquations}.
1382   \begin{table}
1383   \caption{EQUATIONS OF MOTION DUE TO POTENTIAL AND KINETIC ENERGIES}
1384   \label{introTable:rbEquations}

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