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# Line 570 | Line 570 | The most obvious change being that matrix $J$ now depe
570   \dot x = J(x)\nabla _x H \label{introEquation:poissonHamiltonian}
571   \end{equation}
572   The most obvious change being that matrix $J$ now depends on $x$.
573 The free rigid body is an example of Poisson system (actually a
574 Lie-Poisson system) with Hamiltonian function of angular kinetic
575 energy.
576 \begin{equation}
577 J(\pi ) = \left( {\begin{array}{*{20}c}
578   0 & {\pi _3 } & { - \pi _2 }  \\
579   { - \pi _3 } & 0 & {\pi _1 }  \\
580   {\pi _2 } & { - \pi _1 } & 0  \\
581 \end{array}} \right)
582 \end{equation}
573  
584 \begin{equation}
585 H = \frac{1}{2}\left( {\frac{{\pi _1^2 }}{{I_1 }} + \frac{{\pi _2^2
586 }}{{I_2 }} + \frac{{\pi _3^2 }}{{I_3 }}} \right)
587 \end{equation}
588
574   \subsection{\label{introSection:exactFlow}Exact Flow}
575  
576   Let $x(t)$ be the exact solution of the ODE system,
# Line 837 | Line 822 | q(\Delta t)} \right]. %
822   %
823   q(\Delta t) &= q(0) + \frac{{\Delta t}}{2}\left[ {\dot q(0) + \dot
824   q(\Delta t)} \right]. %
825 < \label{introEquation:positionVerlet1}
825 > \label{introEquation:positionVerlet2}
826   \end{align}
827  
828   \subsubsection{\label{introSection:errorAnalysis}Error Analysis and Higher Order Methods}
# Line 903 | Line 888 | dynamical information.
888   biological systems, providing structural, thermodynamic and
889   dynamical information.
890  
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 + Macroscopic properties are related to microscopic behavior.
901 +
902 + Time dependent (and independent) microscopic behavior of a molecule
903 + can be calculated by molecular dynamics simulations.
904 +
905   \subsection{\label{introSec:mdInit}Initialization}
906  
907   \subsection{\label{introSec:forceEvaluation}Force Evaluation}
# Line 942 | Line 941 | symplectic structure of the flow. Introducing conjugat
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 $A$ and re-formulating Hamiltonian's equation, a
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 $A_t A = 1$. An alternative
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}.
952 > coworkers\cite{Dullweber1997} in depth.
953  
955 \subsection{\label{introSection:lieAlgebra}Lie Algebra}
956
954   \subsection{\label{introSection:constrainedHamiltonianRB}Constrained Hamiltonian for Rigid Body}
955 <
955 > The motion of the rigid body is Hamiltonian with the Hamiltonian
956 > function
957   \begin{equation}
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 ].
# Line 1027 | Line 1025 | V(q,Q) = V(Q X_0 + q).
1025   \[
1026   V(q,Q) = V(Q X_0 + q).
1027   \]
1028 < Hence,
1028 > Hence, the force and torque are given by
1029   \[
1030 < \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)}
1030 > \nabla _q V(q,Q) = F(q,Q) = \sum\limits_i {F_i (q,Q)},
1031   \]
1032 <
1032 > and
1033   \[
1034   \nabla _Q V(q,Q) = F(q,Q)X_i^t
1035   \]
1036 + respectively.
1037  
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
# Line 1080 | Line 1079 | Since $\Lambda$ is symmetric, the last term of Equatio
1079   (\Lambda  - \Lambda ^T ) . \label{introEquation:skewMatrixPI}
1080   \end{equation}
1081   Since $\Lambda$ is symmetric, the last term of Equation
1082 < \ref{introEquation:skewMatrixPI}, which implies the Lagrange
1083 < multiplier $\Lambda$ is ignored in the integration.
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 < Hence, applying hat-map isomorphism, we obtain the equation of
1088 < motion for angular momentum on body frame
1089 < \[
1090 < \dot \pi  = \pi  \times I^{ - 1} \pi  + Q^T \sum\limits_i {F_i (r,Q)
1091 < \times X_i }
1092 < \]
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   \[
1097 < \dot Q = Qskew(M^{ - 1} \pi )
1097 > \dot Q = Qskew(I^{ - 1} \pi )
1098   \]
1099  
1100 < The free rigid body equation is an example of a non-canonical
1101 < Hamiltonian system.
1100 > \subsection{\label{introSection:SymplecticFreeRB}Symplectic
1101 > Lie-Poisson Integrator for Free Rigid Body}
1102  
1103 < \subsection{\label{introSection:symplecticDiscretizationRB}Symplectic Integration of Euler Equations}
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 < \[
1127 < \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1128 < _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}
1129 < \]
1130 <
1131 < \[
1132 < \varphi _{\Delta t,T}  = \varphi _{\Delta t,R}  \circ \varphi
1133 < _{\Delta t,\pi }
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 + \[
1142 + \pi (\Delta t) = e^{\Delta tR_1 } \pi (0),Q(\Delta t) =
1143 + Q(0)e^{\Delta tR_1 }
1144 + \]
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   \[
1166 < \varphi _{\Delta t,\pi }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1166 > T^r (\pi ) = \frac{1}{2}T_1 ^r (\pi _1 ) + \frac{1}{2}T_2^r (\pi _2
1167 > ) + T_3^r (\pi _3 ) + \frac{1}{2}T_2^r (\pi _2 ) + \frac{1}{2}T_1 ^r
1168 > (\pi _1 )
1169 > \].
1170 > Concatenating flows corresponding to these five terms, we can obtain
1171 > an symplectic integrator,
1172 > \[
1173 > \varphi _{\Delta t,T^r }  = \varphi _{\Delta t/2,\pi _1 }  \circ
1174   \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t,\pi _3 }
1175   \circ \varphi _{\Delta t/2,\pi _2 }  \circ \varphi _{\Delta t/2,\pi
1176 < _1 }
1176 > _1 }.
1177   \]
1178  
1179 + The non-canonical Lie-Poisson bracket ${F, G}$ of two function
1180 + $F(\pi )$ and $G(\pi )$ is defined by
1181   \[
1182 + \{ F,G\} (\pi ) = [\nabla _\pi  F(\pi )]^T J(\pi )\nabla _\pi  G(\pi
1183 + )
1184 + \]
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 + \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 + \[
1208 + H = T(p,\pi ) + V(q,Q)
1209 + \]
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 + \[
1227 + \varphi _{\Delta t}  = \varphi _{\Delta t/2,V}  \circ \varphi
1228 + _{\Delta t,T}  \circ \varphi _{\Delta t/2,V}.
1229 + \]
1230 + Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two
1231 + sub-flows which corresponding to force and torque respectively,
1232 + \[
1233   \varphi _{\Delta t/2,V}  = \varphi _{\Delta t/2,F}  \circ \varphi
1234 < _{\Delta t/2,\tau }
1234 > _{\Delta t/2,\tau }.
1235   \]
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:LDIntroduction}Introduction and application of Langevin Dynamics}
1273 > \subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation}
1274  
1275 < \subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics}
1276 <
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}
1285 > \label{introEquation:bathGLE}.
1286   \end{equation}
1287 < where $H_B$ is harmonic bath Hamiltonian,
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   \[
1290 < H_B  =\sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
1291 < }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  w_\alpha ^2 } \right\}}
1290 > H_B  = \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{p_\alpha ^2
1291 > }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha  \omega _\alpha ^2 }
1292 > \right\}}
1293   \]
1294 < and $\Delta U$ is bilinear system-bath coupling,
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 < Completing the square,
1301 > where $g_\alpha$ are the coupling constants between the bath and the
1302 > coordinate $x$. Introducing
1303   \[
1304 < H_B  + \Delta U = \sum\limits_{\alpha  = 1}^N {\left\{
1305 < {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
1306 < w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
1307 < w_\alpha ^2 }}x} \right)^2 } \right\}}  - \sum\limits_{\alpha  =
1308 < 1}^N {\frac{{g_\alpha ^2 }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1152 < \]
1153 < and putting it back into Eq.~\ref{introEquation:bathGLE},
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   \[
1310   H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha  = 1}^N
1311   {\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha  }} + \frac{1}{2}m_\alpha
1312   w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha  }}{{m_\alpha
1313   w_\alpha ^2 }}x} \right)^2 } \right\}}
1314   \]
1160 where
1161 \[
1162 W(x) = U(x) - \sum\limits_{\alpha  = 1}^N {\frac{{g_\alpha ^2
1163 }}{{2m_\alpha  w_\alpha ^2 }}} x^2
1164 \]
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{align}
1321 < \dot p &=  - \frac{{\partial H}}{{\partial x}}
1322 <       &= m\ddot x
1323 <       &= - \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)}
1324 < \label{introEquation:Lp5}
1325 < \end{align}
1326 < , and
1327 < \begin{align}
1328 < \dot p_\alpha   &=  - \frac{{\partial H}}{{\partial x_\alpha  }}
1329 <                &= m\ddot x_\alpha
1330 <                &= \- m_\alpha  w_\alpha ^2 \left( {x_\alpha   - \frac{{g_\alpha}}{{m_\alpha  w_\alpha ^2 }}x} \right)
1331 < \end{align}
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 < \subsection{\label{introSection:laplaceTransform}The Laplace Transform}
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   \[
1346 < L(x) = \int_0^\infty  {x(t)e^{ - pt} dt}
1346 > L(f(t)) \equiv F(p) = \int_0^\infty  {f(t)e^{ - pt} dt}
1347   \]
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   \[
1362 < L(x + y) = L(x) + L(y)
1362 > \begin{array}{c}
1363 > p^2 L(x_\alpha  ) - px_\alpha  (0) - \dot x_\alpha  (0) =  - \omega _\alpha ^2 L(x_\alpha  ) + \frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) \\
1364 > L(x_\alpha  ) = \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }} \\
1365 > \end{array}
1366   \]
1367 <
1367 > By the same way, the system coordinates become
1368   \[
1369 < L(ax) = aL(x)
1369 > \begin{array}{c}
1370 > mL(\ddot x) =  - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\
1371 >  - \sum\limits_{\alpha  = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}\frac{p}{{p^2  + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2  + \omega _\alpha ^2 }}g_\alpha  x_\alpha  (0) - \frac{1}{{p^2  + \omega _\alpha ^2 }}g_\alpha  \dot x_\alpha  (0)} \right\}}  \\
1372 > \end{array}
1373   \]
1374  
1375 + With the help of some relatively important inverse Laplace
1376 + transformations:
1377   \[
1378 < L(\dot x) = pL(x) - px(0)
1378 > \begin{array}{c}
1379 > L(\cos at) = \frac{p}{{p^2  + a^2 }} \\
1380 > L(\sin at) = \frac{a}{{p^2  + a^2 }} \\
1381 > L(1) = \frac{1}{p} \\
1382 > \end{array}
1383   \]
1384 <
1201 < \[
1202 < L(\ddot x) = p^2 L(x) - px(0) - \dot x(0)
1203 < \]
1204 <
1205 < \[
1206 < L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p)
1207 < \]
1208 <
1209 < Some relatively important transformation,
1210 < \[
1211 < L(\cos at) = \frac{p}{{p^2  + a^2 }}
1212 < \]
1213 <
1214 < \[
1215 < L(\sin at) = \frac{a}{{p^2  + a^2 }}
1216 < \]
1217 <
1218 < \[
1219 < L(1) = \frac{1}{p}
1220 < \]
1221 <
1222 < First, the bath coordinates,
1223 < \[
1224 < p^2 L(x_\alpha  ) - px_\alpha  (0) - \dot x_\alpha  (0) =  - \omega
1225 < _\alpha ^2 L(x_\alpha  ) + \frac{{g_\alpha  }}{{\omega _\alpha
1226 < }}L(x)
1227 < \]
1228 < \[
1229 < L(x_\alpha  ) = \frac{{\frac{{g_\alpha  }}{{\omega _\alpha  }}L(x) +
1230 < px_\alpha  (0) + \dot x_\alpha  (0)}}{{p^2  + \omega _\alpha ^2 }}
1231 < \]
1232 < Then, the system coordinates,
1233 < \begin{align}
1234 < mL(\ddot x) &=  - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} -
1235 < \sum\limits_{\alpha  = 1}^N {\left\{ {\frac{{\frac{{g_\alpha
1236 < }}{{\omega _\alpha  }}L(x) + px_\alpha  (0) + \dot x_\alpha
1237 < (0)}}{{p^2  + \omega _\alpha ^2 }} - \frac{{g_\alpha ^2 }}{{m_\alpha
1238 < }}\omega _\alpha ^2 L(x)} \right\}}
1239 < %
1240 < &= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} -
1241 < \sum\limits_{\alpha  = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}\frac{p}{{p^2  + \omega _\alpha ^2 }}pL(x)
1242 < - \frac{p}{{p^2  + \omega _\alpha ^2 }}g_\alpha  x_\alpha  (0)
1243 < - \frac{1}{{p^2  + \omega _\alpha ^2 }}g_\alpha  \dot x_\alpha  (0)} \right\}}
1244 < \end{align}
1245 < Then, the inverse transform,
1246 <
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 1263 | 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   \[
1446 < R(t) = \sum\limits_{\alpha  = 1}^N {\left( {g_\alpha  x_\alpha  (0)
1282 < - \frac{{g_\alpha ^2 }}{{m_\alpha  \omega _\alpha ^2 }}x(0)}
1283 < \right)\cos (\omega _\alpha  t)}  + \frac{{\dot x_\alpha
1284 < (0)}}{{\omega _\alpha  }}\sin (\omega _\alpha  t)
1446 > \int_0^t {\xi (t)\dot x(t - \tau )d\tau }
1447   \]
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 <
1306 < \begin{align}
1307 < \left\langle {R(t)R(0)} \right\rangle  &= \sum\limits_\alpha
1308 < {\sum\limits_\beta  {g_\alpha  g_\beta  \left\langle {q_\alpha
1309 < (t)q_\beta  (0)} \right\rangle } }
1310 < %
1311 < &= \sum\limits_\alpha  {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)}
1312 < \right\rangle \cos (\omega _\alpha  t)}
1313 < %
1314 < &= kT\xi (t)
1315 < \end{align}
1316 <
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  
1322 \section{\label{introSection:hydroynamics}Hydrodynamics}
1323
1515   \subsection{\label{introSection:frictionTensor} Friction Tensor}
1516 < \subsection{\label{introSection:analyticalApproach}Analytical
1517 < 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:approximationApproach}Approximation
1329 < Approach}
1551 > \subsubsection{\label{introSection:resistanceTensorRegular}The Resistance Tensor for Regular Shape}
1552  
1553 < \subsection{\label{introSection:centersRigidBody}Centers of Rigid
1554 < Body}
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 < \section{\label{introSection:correlationFunctions}Correlation Functions}
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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