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} |
883 |
|
|
884 |
|
\section{\label{introSection:molecularDynamics}Molecular Dynamics} |
885 |
|
|
886 |
< |
As a special discipline of molecular modeling, Molecular dynamics |
887 |
< |
has proven to be a powerful tool for studying the functions of |
888 |
< |
biological systems, providing structural, thermodynamic and |
889 |
< |
dynamical information. |
890 |
< |
|
891 |
< |
\subsection{\label{introSec:mdInit}Initialization} |
886 |
> |
As one of the principal tools of molecular modeling, Molecular |
887 |
> |
dynamics has proven to be a powerful tool for studying the functions |
888 |
> |
of biological systems, providing structural, thermodynamic and |
889 |
> |
dynamical information. The basic idea of molecular dynamics is that |
890 |
> |
macroscopic properties are related to microscopic behavior and |
891 |
> |
microscopic behavior can be calculated from the trajectories in |
892 |
> |
simulations. For instance, instantaneous temperature of an |
893 |
> |
Hamiltonian system of $N$ particle can be measured by |
894 |
> |
\[ |
895 |
> |
T = \sum\limits_{i = 1}^N {\frac{{m_i v_i^2 }}{{fk_B }}} |
896 |
> |
\] |
897 |
> |
where $m_i$ and $v_i$ are the mass and velocity of $i$th particle |
898 |
> |
respectively, $f$ is the number of degrees of freedom, and $k_B$ is |
899 |
> |
the boltzman constant. |
900 |
|
|
901 |
< |
\subsection{\label{introSec:forceEvaluation}Force Evaluation} |
901 |
> |
A typical molecular dynamics run consists of three essential steps: |
902 |
> |
\begin{enumerate} |
903 |
> |
\item Initialization |
904 |
> |
\begin{enumerate} |
905 |
> |
\item Preliminary preparation |
906 |
> |
\item Minimization |
907 |
> |
\item Heating |
908 |
> |
\item Equilibration |
909 |
> |
\end{enumerate} |
910 |
> |
\item Production |
911 |
> |
\item Analysis |
912 |
> |
\end{enumerate} |
913 |
> |
These three individual steps will be covered in the following |
914 |
> |
sections. Sec.~\ref{introSec:initialSystemSettings} deals with the |
915 |
> |
initialization of a simulation. Sec.~\ref{introSec:production} will |
916 |
> |
discusses issues in production run. Sec.~\ref{introSection:Analysis} |
917 |
> |
provides the theoretical tools for trajectory analysis. |
918 |
> |
|
919 |
> |
\subsection{\label{introSec:initialSystemSettings}Initialization} |
920 |
> |
|
921 |
> |
\subsubsection{Preliminary preparation} |
922 |
|
|
923 |
< |
\subsection{\label{introSection:mdIntegration} Integration of the Equations of Motion} |
923 |
> |
When selecting the starting structure of a molecule for molecular |
924 |
> |
simulation, one may retrieve its Cartesian coordinates from public |
925 |
> |
databases, such as RCSB Protein Data Bank \textit{etc}. Although |
926 |
> |
thousands of crystal structures of molecules are discovered every |
927 |
> |
year, many more remain unknown due to the difficulties of |
928 |
> |
purification and crystallization. Even for the molecule with known |
929 |
> |
structure, some important information is missing. For example, the |
930 |
> |
missing hydrogen atom which acts as donor in hydrogen bonding must |
931 |
> |
be added. Moreover, in order to include electrostatic interaction, |
932 |
> |
one may need to specify the partial charges for individual atoms. |
933 |
> |
Under some circumstances, we may even need to prepare the system in |
934 |
> |
a special setup. For instance, when studying transport phenomenon in |
935 |
> |
membrane system, we may prepare the lipids in bilayer structure |
936 |
> |
instead of placing lipids randomly in solvent, since we are not |
937 |
> |
interested in self-aggregation and it takes a long time to happen. |
938 |
> |
|
939 |
> |
\subsubsection{Minimization} |
940 |
> |
|
941 |
> |
It is quite possible that some of molecules in the system from |
942 |
> |
preliminary preparation may be overlapped with each other. This |
943 |
> |
close proximity leads to high potential energy which consequently |
944 |
> |
jeopardizes any molecular dynamics simulations. To remove these |
945 |
> |
steric overlaps, one typically performs energy minimization to find |
946 |
> |
a more reasonable conformation. Several energy minimization methods |
947 |
> |
have been developed to exploit the energy surface and to locate the |
948 |
> |
local minimum. While converging slowly near the minimum, steepest |
949 |
> |
descent method is extremely robust when systems are far from |
950 |
> |
harmonic. Thus, it is often used to refine structure from |
951 |
> |
crystallographic data. Relied on the gradient or hessian, advanced |
952 |
> |
methods like conjugate gradient and Newton-Raphson converge rapidly |
953 |
> |
to a local minimum, while become unstable if the energy surface is |
954 |
> |
far from quadratic. Another factor must be taken into account, when |
955 |
> |
choosing energy minimization method, is the size of the system. |
956 |
> |
Steepest descent and conjugate gradient can deal with models of any |
957 |
> |
size. Because of the limit of computation power to calculate hessian |
958 |
> |
matrix and insufficient storage capacity to store them, most |
959 |
> |
Newton-Raphson methods can not be used with very large models. |
960 |
> |
|
961 |
> |
\subsubsection{Heating} |
962 |
|
|
963 |
+ |
Typically, Heating is performed by assigning random velocities |
964 |
+ |
according to a Gaussian distribution for a temperature. Beginning at |
965 |
+ |
a lower temperature and gradually increasing the temperature by |
966 |
+ |
assigning greater random velocities, we end up with setting the |
967 |
+ |
temperature of the system to a final temperature at which the |
968 |
+ |
simulation will be conducted. In heating phase, we should also keep |
969 |
+ |
the system from drifting or rotating as a whole. Equivalently, the |
970 |
+ |
net linear momentum and angular momentum of the system should be |
971 |
+ |
shifted to zero. |
972 |
+ |
|
973 |
+ |
\subsubsection{Equilibration} |
974 |
+ |
|
975 |
+ |
The purpose of equilibration is to allow the system to evolve |
976 |
+ |
spontaneously for a period of time and reach equilibrium. The |
977 |
+ |
procedure is continued until various statistical properties, such as |
978 |
+ |
temperature, pressure, energy, volume and other structural |
979 |
+ |
properties \textit{etc}, become independent of time. Strictly |
980 |
+ |
speaking, minimization and heating are not necessary, provided the |
981 |
+ |
equilibration process is long enough. However, these steps can serve |
982 |
+ |
as a means to arrive at an equilibrated structure in an effective |
983 |
+ |
way. |
984 |
+ |
|
985 |
+ |
\subsection{\label{introSection:production}Production} |
986 |
+ |
|
987 |
+ |
Production run is the most important steps of the simulation, in |
988 |
+ |
which the equilibrated structure is used as a starting point and the |
989 |
+ |
motions of the molecules are collected for later analysis. In order |
990 |
+ |
to capture the macroscopic properties of the system, the molecular |
991 |
+ |
dynamics simulation must be performed in correct and efficient way. |
992 |
+ |
|
993 |
+ |
The most expensive part of a molecular dynamics simulation is the |
994 |
+ |
calculation of non-bonded forces, such as van der Waals force and |
995 |
+ |
Coulombic forces \textit{etc}. For a system of $N$ particles, the |
996 |
+ |
complexity of the algorithm for pair-wise interactions is $O(N^2 )$, |
997 |
+ |
which making large simulations prohibitive in the absence of any |
998 |
+ |
computation saving techniques. |
999 |
+ |
|
1000 |
+ |
A natural approach to avoid system size issue is to represent the |
1001 |
+ |
bulk behavior by a finite number of the particles. However, this |
1002 |
+ |
approach will suffer from the surface effect. To offset this, |
1003 |
+ |
\textit{Periodic boundary condition} is developed to simulate bulk |
1004 |
+ |
properties with a relatively small number of particles. In this |
1005 |
+ |
method, the simulation box is replicated throughout space to form an |
1006 |
+ |
infinite lattice. During the simulation, when a particle moves in |
1007 |
+ |
the primary cell, its image in other cells move in exactly the same |
1008 |
+ |
direction with exactly the same orientation. Thus, as a particle |
1009 |
+ |
leaves the primary cell, one of its images will enter through the |
1010 |
+ |
opposite face. |
1011 |
+ |
%\begin{figure} |
1012 |
+ |
%\centering |
1013 |
+ |
%\includegraphics[width=\linewidth]{pbcFig.eps} |
1014 |
+ |
%\caption[An illustration of periodic boundary conditions]{A 2-D |
1015 |
+ |
%illustration of periodic boundary conditions. As one particle leaves |
1016 |
+ |
%the right of the simulation box, an image of it enters the left.} |
1017 |
+ |
%\label{introFig:pbc} |
1018 |
+ |
%\end{figure} |
1019 |
+ |
|
1020 |
+ |
%cutoff and minimum image convention |
1021 |
+ |
Another important technique to improve the efficiency of force |
1022 |
+ |
evaluation is to apply cutoff where particles farther than a |
1023 |
+ |
predetermined distance, are not included in the calculation |
1024 |
+ |
\cite{Frenkel1996}. The use of a cutoff radius will cause a |
1025 |
+ |
discontinuity in the potential energy curve |
1026 |
+ |
(Fig.~\ref{introFig:shiftPot}). Fortunately, one can shift the |
1027 |
+ |
potential to ensure the potential curve go smoothly to zero at the |
1028 |
+ |
cutoff radius. Cutoff strategy works pretty well for Lennard-Jones |
1029 |
+ |
interaction because of its short range nature. However, simply |
1030 |
+ |
truncating the electrostatic interaction with the use of cutoff has |
1031 |
+ |
been shown to lead to severe artifacts in simulations. Ewald |
1032 |
+ |
summation, in which the slowly conditionally convergent Coulomb |
1033 |
+ |
potential is transformed into direct and reciprocal sums with rapid |
1034 |
+ |
and absolute convergence, has proved to minimize the periodicity |
1035 |
+ |
artifacts in liquid simulations. Taking the advantages of the fast |
1036 |
+ |
Fourier transform (FFT) for calculating discrete Fourier transforms, |
1037 |
+ |
the particle mesh-based methods are accelerated from $O(N^{3/2})$ to |
1038 |
+ |
$O(N logN)$. An alternative approach is \emph{fast multipole |
1039 |
+ |
method}, which treats Coulombic interaction exactly at short range, |
1040 |
+ |
and approximate the potential at long range through multipolar |
1041 |
+ |
expansion. In spite of their wide acceptances at the molecular |
1042 |
+ |
simulation community, these two methods are hard to be implemented |
1043 |
+ |
correctly and efficiently. Instead, we use a damped and |
1044 |
+ |
charge-neutralized Coulomb potential method developed by Wolf and |
1045 |
+ |
his coworkers. The shifted Coulomb potential for particle $i$ and |
1046 |
+ |
particle $j$ at distance $r_{rj}$ is given by: |
1047 |
+ |
\begin{equation} |
1048 |
+ |
V(r_{ij})= \frac{q_i q_j \textrm{erfc}(\alpha |
1049 |
+ |
r_{ij})}{r_{ij}}-\lim_{r_{ij}\rightarrow |
1050 |
+ |
R_\textrm{c}}\left\{\frac{q_iq_j \textrm{erfc}(\alpha |
1051 |
+ |
r_{ij})}{r_{ij}}\right\}. \label{introEquation:shiftedCoulomb} |
1052 |
+ |
\end{equation} |
1053 |
+ |
where $\alpha$ is the convergence parameter. Due to the lack of |
1054 |
+ |
inherent periodicity and rapid convergence,this method is extremely |
1055 |
+ |
efficient and easy to implement. |
1056 |
+ |
%\begin{figure} |
1057 |
+ |
%\centering |
1058 |
+ |
%\includegraphics[width=\linewidth]{pbcFig.eps} |
1059 |
+ |
%\caption[An illustration of shifted Coulomb potential]{An illustration of shifted Coulomb potential.} |
1060 |
+ |
%\label{introFigure:shiftedCoulomb} |
1061 |
+ |
%\end{figure} |
1062 |
+ |
|
1063 |
+ |
%multiple time step |
1064 |
+ |
|
1065 |
+ |
\subsection{\label{introSection:Analysis} Analysis} |
1066 |
+ |
|
1067 |
+ |
Recently, advanced visualization technique are widely applied to |
1068 |
+ |
monitor the motions of molecules. Although the dynamics of the |
1069 |
+ |
system can be described qualitatively from animation, quantitative |
1070 |
+ |
trajectory analysis are more appreciable. According to the |
1071 |
+ |
principles of Statistical Mechanics, |
1072 |
+ |
Sec.~\ref{introSection:statisticalMechanics}, one can compute |
1073 |
+ |
thermodynamics properties, analyze fluctuations of structural |
1074 |
+ |
parameters, and investigate time-dependent processes of the molecule |
1075 |
+ |
from the trajectories. |
1076 |
+ |
|
1077 |
+ |
\subsubsection{\label{introSection:thermodynamicsProperties}Thermodynamics Properties} |
1078 |
+ |
|
1079 |
+ |
Thermodynamics properties, which can be expressed in terms of some |
1080 |
+ |
function of the coordinates and momenta of all particles in the |
1081 |
+ |
system, can be directly computed from molecular dynamics. The usual |
1082 |
+ |
way to measure the pressure is based on virial theorem of Clausius |
1083 |
+ |
which states that the virial is equal to $-3Nk_BT$. For a system |
1084 |
+ |
with forces between particles, the total virial, $W$, contains the |
1085 |
+ |
contribution from external pressure and interaction between the |
1086 |
+ |
particles: |
1087 |
+ |
\[ |
1088 |
+ |
W = - 3PV + \left\langle {\sum\limits_{i < j} {r{}_{ij} \cdot |
1089 |
+ |
f_{ij} } } \right\rangle |
1090 |
+ |
\] |
1091 |
+ |
where $f_{ij}$ is the force between particle $i$ and $j$ at a |
1092 |
+ |
distance $r_{ij}$. Thus, the expression for the pressure is given |
1093 |
+ |
by: |
1094 |
+ |
\begin{equation} |
1095 |
+ |
P = \frac{{Nk_B T}}{V} - \frac{1}{{3V}}\left\langle {\sum\limits_{i |
1096 |
+ |
< j} {r{}_{ij} \cdot f_{ij} } } \right\rangle |
1097 |
+ |
\end{equation} |
1098 |
+ |
|
1099 |
+ |
\subsubsection{\label{introSection:structuralProperties}Structural Properties} |
1100 |
+ |
|
1101 |
+ |
Structural Properties of a simple fluid can be described by a set of |
1102 |
+ |
distribution functions. Among these functions,\emph{pair |
1103 |
+ |
distribution function}, also known as \emph{radial distribution |
1104 |
+ |
function}, is of most fundamental importance to liquid-state theory. |
1105 |
+ |
Pair distribution function can be gathered by Fourier transforming |
1106 |
+ |
raw data from a series of neutron diffraction experiments and |
1107 |
+ |
integrating over the surface factor \cite{Powles73}. The experiment |
1108 |
+ |
result can serve as a criterion to justify the correctness of the |
1109 |
+ |
theory. Moreover, various equilibrium thermodynamic and structural |
1110 |
+ |
properties can also be expressed in terms of radial distribution |
1111 |
+ |
function \cite{allen87:csl}. |
1112 |
+ |
|
1113 |
+ |
A pair distribution functions $g(r)$ gives the probability that a |
1114 |
+ |
particle $i$ will be located at a distance $r$ from a another |
1115 |
+ |
particle $j$ in the system |
1116 |
+ |
\[ |
1117 |
+ |
g(r) = \frac{V}{{N^2 }}\left\langle {\sum\limits_i {\sum\limits_{j |
1118 |
+ |
\ne i} {\delta (r - r_{ij} )} } } \right\rangle. |
1119 |
+ |
\] |
1120 |
+ |
Note that the delta function can be replaced by a histogram in |
1121 |
+ |
computer simulation. Figure |
1122 |
+ |
\ref{introFigure:pairDistributionFunction} shows a typical pair |
1123 |
+ |
distribution function for the liquid argon system. The occurrence of |
1124 |
+ |
several peaks in the plot of $g(r)$ suggests that it is more likely |
1125 |
+ |
to find particles at certain radial values than at others. This is a |
1126 |
+ |
result of the attractive interaction at such distances. Because of |
1127 |
+ |
the strong repulsive forces at short distance, the probability of |
1128 |
+ |
locating particles at distances less than about 2.5{\AA} from each |
1129 |
+ |
other is essentially zero. |
1130 |
+ |
|
1131 |
+ |
%\begin{figure} |
1132 |
+ |
%\centering |
1133 |
+ |
%\includegraphics[width=\linewidth]{pdf.eps} |
1134 |
+ |
%\caption[Pair distribution function for the liquid argon |
1135 |
+ |
%]{Pair distribution function for the liquid argon} |
1136 |
+ |
%\label{introFigure:pairDistributionFunction} |
1137 |
+ |
%\end{figure} |
1138 |
+ |
|
1139 |
+ |
\subsubsection{\label{introSection:timeDependentProperties}Time-dependent |
1140 |
+ |
Properties} |
1141 |
+ |
|
1142 |
+ |
Time-dependent properties are usually calculated using \emph{time |
1143 |
+ |
correlation function}, which correlates random variables $A$ and $B$ |
1144 |
+ |
at two different time |
1145 |
+ |
\begin{equation} |
1146 |
+ |
C_{AB} (t) = \left\langle {A(t)B(0)} \right\rangle. |
1147 |
+ |
\label{introEquation:timeCorrelationFunction} |
1148 |
+ |
\end{equation} |
1149 |
+ |
If $A$ and $B$ refer to same variable, this kind of correlation |
1150 |
+ |
function is called \emph{auto correlation function}. One example of |
1151 |
+ |
auto correlation function is velocity auto-correlation function |
1152 |
+ |
which is directly related to transport properties of molecular |
1153 |
+ |
liquids: |
1154 |
+ |
\[ |
1155 |
+ |
D = \frac{1}{3}\int\limits_0^\infty {\left\langle {v(t) \cdot v(0)} |
1156 |
+ |
\right\rangle } dt |
1157 |
+ |
\] |
1158 |
+ |
where $D$ is diffusion constant. Unlike velocity autocorrelation |
1159 |
+ |
function which is averaging over time origins and over all the |
1160 |
+ |
atoms, dipole autocorrelation are calculated for the entire system. |
1161 |
+ |
The dipole autocorrelation function is given by: |
1162 |
+ |
\[ |
1163 |
+ |
c_{dipole} = \left\langle {u_{tot} (t) \cdot u_{tot} (t)} |
1164 |
+ |
\right\rangle |
1165 |
+ |
\] |
1166 |
+ |
Here $u_{tot}$ is the net dipole of the entire system and is given |
1167 |
+ |
by |
1168 |
+ |
\[ |
1169 |
+ |
u_{tot} (t) = \sum\limits_i {u_i (t)} |
1170 |
+ |
\] |
1171 |
+ |
In principle, many time correlation functions can be related with |
1172 |
+ |
Fourier transforms of the infrared, Raman, and inelastic neutron |
1173 |
+ |
scattering spectra of molecular liquids. In practice, one can |
1174 |
+ |
extract the IR spectrum from the intensity of dipole fluctuation at |
1175 |
+ |
each frequency using the following relationship: |
1176 |
+ |
\[ |
1177 |
+ |
\hat c_{dipole} (v) = \int_{ - \infty }^\infty {c_{dipole} (t)e^{ - |
1178 |
+ |
i2\pi vt} dt} |
1179 |
+ |
\] |
1180 |
+ |
|
1181 |
|
\section{\label{introSection:rigidBody}Dynamics of Rigid Bodies} |
1182 |
|
|
1183 |
|
Rigid bodies are frequently involved in the modeling of different |
1211 |
|
The break through in geometric literature suggests that, in order to |
1212 |
|
develop a long-term integration scheme, one should preserve the |
1213 |
|
symplectic structure of the flow. Introducing conjugate momentum to |
1214 |
< |
rotation matrix $A$ and re-formulating Hamiltonian's equation, a |
1214 |
> |
rotation matrix $Q$ and re-formulating Hamiltonian's equation, a |
1215 |
|
symplectic integrator, RSHAKE, was proposed to evolve the |
1216 |
|
Hamiltonian system in a constraint manifold by iteratively |
1217 |
< |
satisfying the orthogonality constraint $A_t A = 1$. An alternative |
1217 |
> |
satisfying the orthogonality constraint $Q_T Q = 1$. An alternative |
1218 |
|
method using quaternion representation was developed by Omelyan. |
1219 |
|
However, both of these methods are iterative and inefficient. In |
1220 |
|
this section, we will present a symplectic Lie-Poisson integrator |
1420 |
|
0 & { - \sin \theta _1 } & {\cos \theta _1 } \\ |
1421 |
|
\end{array}} \right),\theta _1 = \frac{{\pi _1 }}{{I_1 }}\Delta t. |
1422 |
|
\] |
1423 |
< |
To reduce the cost of computing expensive functions in e^{\Delta |
1424 |
< |
tR_1 }, we can use Cayley transformation, |
1423 |
> |
To reduce the cost of computing expensive functions in $e^{\Delta |
1424 |
> |
tR_1 }$, we can use Cayley transformation, |
1425 |
|
\[ |
1426 |
|
e^{\Delta tR_1 } \approx (1 - \Delta tR_1 )^{ - 1} (1 + \Delta tR_1 |
1427 |
|
) |
1428 |
|
\] |
1429 |
< |
|
1146 |
< |
The flow maps for $T_2^r$ and $T_2^r$ can be found in the same |
1429 |
> |
The flow maps for $T_2^r$ and $T_3^r$ can be found in the same |
1430 |
|
manner. |
1431 |
|
|
1432 |
|
In order to construct a second-order symplectic method, we split the |
1496 |
|
\varphi _{\Delta t} = \varphi _{\Delta t/2,V} \circ \varphi |
1497 |
|
_{\Delta t,T} \circ \varphi _{\Delta t/2,V}. |
1498 |
|
\] |
1499 |
< |
Moreover, \varphi _{\Delta t/2,V} can be divided into two sub-flows |
1500 |
< |
which corresponding to force and torque respectively, |
1499 |
> |
Moreover, $\varphi _{\Delta t/2,V}$ can be divided into two |
1500 |
> |
sub-flows which corresponding to force and torque respectively, |
1501 |
|
\[ |
1502 |
|
\varphi _{\Delta t/2,V} = \varphi _{\Delta t/2,F} \circ \varphi |
1503 |
|
_{\Delta t/2,\tau }. |
1504 |
|
\] |
1505 |
|
Since the associated operators of $\varphi _{\Delta t/2,F} $ and |
1506 |
|
$\circ \varphi _{\Delta t/2,\tau }$ are commuted, the composition |
1507 |
< |
order inside \varphi _{\Delta t/2,V} does not matter. |
1507 |
> |
order inside $\varphi _{\Delta t/2,V}$ does not matter. |
1508 |
|
|
1509 |
|
Furthermore, kinetic potential can be separated to translational |
1510 |
|
kinetic term, $T^t (p)$, and rotational kinetic term, $T^r (\pi )$, |
1534 |
|
mimics a simple heat bath with stochastic and dissipative forces, |
1535 |
|
has been applied in a variety of studies. This section will review |
1536 |
|
the theory of Langevin dynamics simulation. A brief derivation of |
1537 |
< |
generalized Langevin Dynamics will be given first. Follow that, we |
1537 |
> |
generalized Langevin equation will be given first. Follow that, we |
1538 |
|
will discuss the physical meaning of the terms appearing in the |
1539 |
|
equation as well as the calculation of friction tensor from |
1540 |
|
hydrodynamics theory. |
1541 |
|
|
1542 |
< |
\subsection{\label{introSection:generalizedLangevinDynamics}Generalized Langevin Dynamics} |
1542 |
> |
\subsection{\label{introSection:generalizedLangevinDynamics}Derivation of Generalized Langevin Equation} |
1543 |
|
|
1544 |
+ |
Harmonic bath model, in which an effective set of harmonic |
1545 |
+ |
oscillators are used to mimic the effect of a linearly responding |
1546 |
+ |
environment, has been widely used in quantum chemistry and |
1547 |
+ |
statistical mechanics. One of the successful applications of |
1548 |
+ |
Harmonic bath model is the derivation of Deriving Generalized |
1549 |
+ |
Langevin Dynamics. Lets consider a system, in which the degree of |
1550 |
+ |
freedom $x$ is assumed to couple to the bath linearly, giving a |
1551 |
+ |
Hamiltonian of the form |
1552 |
|
\begin{equation} |
1553 |
|
H = \frac{{p^2 }}{{2m}} + U(x) + H_B + \Delta U(x,x_1 , \ldots x_N) |
1554 |
< |
\label{introEquation:bathGLE} |
1554 |
> |
\label{introEquation:bathGLE}. |
1555 |
|
\end{equation} |
1556 |
< |
where $H_B$ is harmonic bath Hamiltonian, |
1556 |
> |
Here $p$ is a momentum conjugate to $q$, $m$ is the mass associated |
1557 |
> |
with this degree of freedom, $H_B$ is harmonic bath Hamiltonian, |
1558 |
|
\[ |
1559 |
< |
H_B =\sum\limits_{\alpha = 1}^N {\left\{ {\frac{{p_\alpha ^2 |
1560 |
< |
}}{{2m_\alpha }} + \frac{1}{2}m_\alpha w_\alpha ^2 } \right\}} |
1559 |
> |
H_B = \sum\limits_{\alpha = 1}^N {\left\{ {\frac{{p_\alpha ^2 |
1560 |
> |
}}{{2m_\alpha }} + \frac{1}{2}m_\alpha \omega _\alpha ^2 } |
1561 |
> |
\right\}} |
1562 |
|
\] |
1563 |
< |
and $\Delta U$ is bilinear system-bath coupling, |
1563 |
> |
where the index $\alpha$ runs over all the bath degrees of freedom, |
1564 |
> |
$\omega _\alpha$ are the harmonic bath frequencies, $m_\alpha$ are |
1565 |
> |
the harmonic bath masses, and $\Delta U$ is bilinear system-bath |
1566 |
> |
coupling, |
1567 |
|
\[ |
1568 |
|
\Delta U = - \sum\limits_{\alpha = 1}^N {g_\alpha x_\alpha x} |
1569 |
|
\] |
1570 |
< |
Completing the square, |
1570 |
> |
where $g_\alpha$ are the coupling constants between the bath and the |
1571 |
> |
coordinate $x$. Introducing |
1572 |
|
\[ |
1573 |
< |
H_B + \Delta U = \sum\limits_{\alpha = 1}^N {\left\{ |
1574 |
< |
{\frac{{p_\alpha ^2 }}{{2m_\alpha }} + \frac{1}{2}m_\alpha |
1575 |
< |
w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha }}{{m_\alpha |
1576 |
< |
w_\alpha ^2 }}x} \right)^2 } \right\}} - \sum\limits_{\alpha = |
1577 |
< |
1}^N {\frac{{g_\alpha ^2 }}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1281 |
< |
\] |
1282 |
< |
and putting it back into Eq.~\ref{introEquation:bathGLE}, |
1573 |
> |
W(x) = U(x) - \sum\limits_{\alpha = 1}^N {\frac{{g_\alpha ^2 |
1574 |
> |
}}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1575 |
> |
\] and combining the last two terms in Equation |
1576 |
> |
\ref{introEquation:bathGLE}, we may rewrite the Harmonic bath |
1577 |
> |
Hamiltonian as |
1578 |
|
\[ |
1579 |
|
H = \frac{{p^2 }}{{2m}} + W(x) + \sum\limits_{\alpha = 1}^N |
1580 |
|
{\left\{ {\frac{{p_\alpha ^2 }}{{2m_\alpha }} + \frac{1}{2}m_\alpha |
1581 |
|
w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha }}{{m_\alpha |
1582 |
|
w_\alpha ^2 }}x} \right)^2 } \right\}} |
1583 |
|
\] |
1289 |
– |
where |
1290 |
– |
\[ |
1291 |
– |
W(x) = U(x) - \sum\limits_{\alpha = 1}^N {\frac{{g_\alpha ^2 |
1292 |
– |
}}{{2m_\alpha w_\alpha ^2 }}} x^2 |
1293 |
– |
\] |
1584 |
|
Since the first two terms of the new Hamiltonian depend only on the |
1585 |
|
system coordinates, we can get the equations of motion for |
1586 |
|
Generalized Langevin Dynamics by Hamilton's equations |
1587 |
|
\ref{introEquation:motionHamiltonianCoordinate, |
1588 |
|
introEquation:motionHamiltonianMomentum}, |
1589 |
< |
\begin{align} |
1590 |
< |
\dot p &= - \frac{{\partial H}}{{\partial x}} |
1591 |
< |
&= m\ddot x |
1592 |
< |
&= - \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)} |
1593 |
< |
\label{introEquation:Lp5} |
1594 |
< |
\end{align} |
1595 |
< |
, and |
1596 |
< |
\begin{align} |
1597 |
< |
\dot p_\alpha &= - \frac{{\partial H}}{{\partial x_\alpha }} |
1598 |
< |
&= m\ddot x_\alpha |
1599 |
< |
&= \- m_\alpha w_\alpha ^2 \left( {x_\alpha - \frac{{g_\alpha}}{{m_\alpha w_\alpha ^2 }}x} \right) |
1600 |
< |
\end{align} |
1589 |
> |
\begin{equation} |
1590 |
> |
m\ddot x = - \frac{{\partial W(x)}}{{\partial x}} - |
1591 |
> |
\sum\limits_{\alpha = 1}^N {g_\alpha \left( {x_\alpha - |
1592 |
> |
\frac{{g_\alpha }}{{m_\alpha w_\alpha ^2 }}x} \right)}, |
1593 |
> |
\label{introEquation:coorMotionGLE} |
1594 |
> |
\end{equation} |
1595 |
> |
and |
1596 |
> |
\begin{equation} |
1597 |
> |
m\ddot x_\alpha = - m_\alpha w_\alpha ^2 \left( {x_\alpha - |
1598 |
> |
\frac{{g_\alpha }}{{m_\alpha w_\alpha ^2 }}x} \right). |
1599 |
> |
\label{introEquation:bathMotionGLE} |
1600 |
> |
\end{equation} |
1601 |
|
|
1602 |
< |
\subsection{\label{introSection:laplaceTransform}The Laplace Transform} |
1602 |
> |
In order to derive an equation for $x$, the dynamics of the bath |
1603 |
> |
variables $x_\alpha$ must be solved exactly first. As an integral |
1604 |
> |
transform which is particularly useful in solving linear ordinary |
1605 |
> |
differential equations, Laplace transform is the appropriate tool to |
1606 |
> |
solve this problem. The basic idea is to transform the difficult |
1607 |
> |
differential equations into simple algebra problems which can be |
1608 |
> |
solved easily. Then applying inverse Laplace transform, also known |
1609 |
> |
as the Bromwich integral, we can retrieve the solutions of the |
1610 |
> |
original problems. |
1611 |
|
|
1612 |
+ |
Let $f(t)$ be a function defined on $ [0,\infty ) $. The Laplace |
1613 |
+ |
transform of f(t) is a new function defined as |
1614 |
|
\[ |
1615 |
< |
L(x) = \int_0^\infty {x(t)e^{ - pt} dt} |
1615 |
> |
L(f(t)) \equiv F(p) = \int_0^\infty {f(t)e^{ - pt} dt} |
1616 |
|
\] |
1617 |
+ |
where $p$ is real and $L$ is called the Laplace Transform |
1618 |
+ |
Operator. Below are some important properties of Laplace transform |
1619 |
+ |
\begin{equation} |
1620 |
+ |
\begin{array}{c} |
1621 |
+ |
L(x + y) = L(x) + L(y) \\ |
1622 |
+ |
L(ax) = aL(x) \\ |
1623 |
+ |
L(\dot x) = pL(x) - px(0) \\ |
1624 |
+ |
L(\ddot x) = p^2 L(x) - px(0) - \dot x(0) \\ |
1625 |
+ |
L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p) \\ |
1626 |
+ |
\end{array} |
1627 |
+ |
\end{equation} |
1628 |
|
|
1629 |
+ |
Applying Laplace transform to the bath coordinates, we obtain |
1630 |
|
\[ |
1631 |
< |
L(x + y) = L(x) + L(y) |
1631 |
> |
\begin{array}{c} |
1632 |
> |
p^2 L(x_\alpha ) - px_\alpha (0) - \dot x_\alpha (0) = - \omega _\alpha ^2 L(x_\alpha ) + \frac{{g_\alpha }}{{\omega _\alpha }}L(x) \\ |
1633 |
> |
L(x_\alpha ) = \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }} \\ |
1634 |
> |
\end{array} |
1635 |
|
\] |
1636 |
< |
|
1636 |
> |
By the same way, the system coordinates become |
1637 |
|
\[ |
1638 |
< |
L(ax) = aL(x) |
1638 |
> |
\begin{array}{c} |
1639 |
> |
mL(\ddot x) = - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} \\ |
1640 |
> |
- \sum\limits_{\alpha = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}\frac{p}{{p^2 + \omega _\alpha ^2 }}pL(x) - \frac{p}{{p^2 + \omega _\alpha ^2 }}g_\alpha x_\alpha (0) - \frac{1}{{p^2 + \omega _\alpha ^2 }}g_\alpha \dot x_\alpha (0)} \right\}} \\ |
1641 |
> |
\end{array} |
1642 |
|
\] |
1643 |
|
|
1644 |
+ |
With the help of some relatively important inverse Laplace |
1645 |
+ |
transformations: |
1646 |
|
\[ |
1647 |
< |
L(\dot x) = pL(x) - px(0) |
1647 |
> |
\begin{array}{c} |
1648 |
> |
L(\cos at) = \frac{p}{{p^2 + a^2 }} \\ |
1649 |
> |
L(\sin at) = \frac{a}{{p^2 + a^2 }} \\ |
1650 |
> |
L(1) = \frac{1}{p} \\ |
1651 |
> |
\end{array} |
1652 |
|
\] |
1653 |
< |
|
1330 |
< |
\[ |
1331 |
< |
L(\ddot x) = p^2 L(x) - px(0) - \dot x(0) |
1332 |
< |
\] |
1333 |
< |
|
1334 |
< |
\[ |
1335 |
< |
L\left( {\int_0^t {g(t - \tau )h(\tau )d\tau } } \right) = G(p)H(p) |
1336 |
< |
\] |
1337 |
< |
|
1338 |
< |
Some relatively important transformation, |
1339 |
< |
\[ |
1340 |
< |
L(\cos at) = \frac{p}{{p^2 + a^2 }} |
1341 |
< |
\] |
1342 |
< |
|
1343 |
< |
\[ |
1344 |
< |
L(\sin at) = \frac{a}{{p^2 + a^2 }} |
1345 |
< |
\] |
1346 |
< |
|
1347 |
< |
\[ |
1348 |
< |
L(1) = \frac{1}{p} |
1349 |
< |
\] |
1350 |
< |
|
1351 |
< |
First, the bath coordinates, |
1352 |
< |
\[ |
1353 |
< |
p^2 L(x_\alpha ) - px_\alpha (0) - \dot x_\alpha (0) = - \omega |
1354 |
< |
_\alpha ^2 L(x_\alpha ) + \frac{{g_\alpha }}{{\omega _\alpha |
1355 |
< |
}}L(x) |
1356 |
< |
\] |
1357 |
< |
\[ |
1358 |
< |
L(x_\alpha ) = \frac{{\frac{{g_\alpha }}{{\omega _\alpha }}L(x) + |
1359 |
< |
px_\alpha (0) + \dot x_\alpha (0)}}{{p^2 + \omega _\alpha ^2 }} |
1360 |
< |
\] |
1361 |
< |
Then, the system coordinates, |
1362 |
< |
\begin{align} |
1363 |
< |
mL(\ddot x) &= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} - |
1364 |
< |
\sum\limits_{\alpha = 1}^N {\left\{ {\frac{{\frac{{g_\alpha |
1365 |
< |
}}{{\omega _\alpha }}L(x) + px_\alpha (0) + \dot x_\alpha |
1366 |
< |
(0)}}{{p^2 + \omega _\alpha ^2 }} - \frac{{g_\alpha ^2 }}{{m_\alpha |
1367 |
< |
}}\omega _\alpha ^2 L(x)} \right\}} |
1368 |
< |
% |
1369 |
< |
&= - \frac{1}{p}\frac{{\partial W(x)}}{{\partial x}} - |
1370 |
< |
\sum\limits_{\alpha = 1}^N {\left\{ { - \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}\frac{p}{{p^2 + \omega _\alpha ^2 }}pL(x) |
1371 |
< |
- \frac{p}{{p^2 + \omega _\alpha ^2 }}g_\alpha x_\alpha (0) |
1372 |
< |
- \frac{1}{{p^2 + \omega _\alpha ^2 }}g_\alpha \dot x_\alpha (0)} \right\}} |
1373 |
< |
\end{align} |
1374 |
< |
Then, the inverse transform, |
1375 |
< |
|
1653 |
> |
, we obtain |
1654 |
|
\begin{align} |
1655 |
|
m\ddot x &= - \frac{{\partial W(x)}}{{\partial x}} - |
1656 |
|
\sum\limits_{\alpha = 1}^N {\left\{ {\left( { - \frac{{g_\alpha ^2 |
1670 |
|
(\omega _\alpha t)} \right\}} |
1671 |
|
\end{align} |
1672 |
|
|
1673 |
+ |
Introducing a \emph{dynamic friction kernel} |
1674 |
|
\begin{equation} |
1675 |
+ |
\xi (t) = \sum\limits_{\alpha = 1}^N {\left( { - \frac{{g_\alpha ^2 |
1676 |
+ |
}}{{m_\alpha \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha t)} |
1677 |
+ |
\label{introEquation:dynamicFrictionKernelDefinition} |
1678 |
+ |
\end{equation} |
1679 |
+ |
and \emph{a random force} |
1680 |
+ |
\begin{equation} |
1681 |
+ |
R(t) = \sum\limits_{\alpha = 1}^N {\left( {g_\alpha x_\alpha (0) |
1682 |
+ |
- \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}x(0)} |
1683 |
+ |
\right)\cos (\omega _\alpha t)} + \frac{{\dot x_\alpha |
1684 |
+ |
(0)}}{{\omega _\alpha }}\sin (\omega _\alpha t), |
1685 |
+ |
\label{introEquation:randomForceDefinition} |
1686 |
+ |
\end{equation} |
1687 |
+ |
the equation of motion can be rewritten as |
1688 |
+ |
\begin{equation} |
1689 |
|
m\ddot x = - \frac{{\partial W}}{{\partial x}} - \int_0^t {\xi |
1690 |
|
(t)\dot x(t - \tau )d\tau } + R(t) |
1691 |
|
\label{introEuqation:GeneralizedLangevinDynamics} |
1692 |
|
\end{equation} |
1693 |
< |
%where $ {\xi (t)}$ is friction kernel, $R(t)$ is random force and |
1694 |
< |
%$W$ is the potential of mean force. $W(x) = - kT\ln p(x)$ |
1693 |
> |
which is known as the \emph{generalized Langevin equation}. |
1694 |
> |
|
1695 |
> |
\subsubsection{\label{introSection:randomForceDynamicFrictionKernel}Random Force and Dynamic Friction Kernel} |
1696 |
> |
|
1697 |
> |
One may notice that $R(t)$ depends only on initial conditions, which |
1698 |
> |
implies it is completely deterministic within the context of a |
1699 |
> |
harmonic bath. However, it is easy to verify that $R(t)$ is totally |
1700 |
> |
uncorrelated to $x$ and $\dot x$, |
1701 |
|
\[ |
1702 |
< |
\xi (t) = \sum\limits_{\alpha = 1}^N {\left( { - \frac{{g_\alpha ^2 |
1703 |
< |
}}{{m_\alpha \omega _\alpha ^2 }}} \right)\cos (\omega _\alpha t)} |
1702 |
> |
\begin{array}{l} |
1703 |
> |
\left\langle {x(t)R(t)} \right\rangle = 0, \\ |
1704 |
> |
\left\langle {\dot x(t)R(t)} \right\rangle = 0. \\ |
1705 |
> |
\end{array} |
1706 |
|
\] |
1707 |
< |
For an infinite harmonic bath, we can use the spectral density and |
1708 |
< |
an integral over frequencies. |
1707 |
> |
This property is what we expect from a truly random process. As long |
1708 |
> |
as the model, which is gaussian distribution in general, chosen for |
1709 |
> |
$R(t)$ is a truly random process, the stochastic nature of the GLE |
1710 |
> |
still remains. |
1711 |
|
|
1712 |
+ |
%dynamic friction kernel |
1713 |
+ |
The convolution integral |
1714 |
|
\[ |
1715 |
< |
R(t) = \sum\limits_{\alpha = 1}^N {\left( {g_\alpha x_\alpha (0) |
1411 |
< |
- \frac{{g_\alpha ^2 }}{{m_\alpha \omega _\alpha ^2 }}x(0)} |
1412 |
< |
\right)\cos (\omega _\alpha t)} + \frac{{\dot x_\alpha |
1413 |
< |
(0)}}{{\omega _\alpha }}\sin (\omega _\alpha t) |
1715 |
> |
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } |
1716 |
|
\] |
1717 |
< |
The random forces depend only on initial conditions. |
1717 |
> |
depends on the entire history of the evolution of $x$, which implies |
1718 |
> |
that the bath retains memory of previous motions. In other words, |
1719 |
> |
the bath requires a finite time to respond to change in the motion |
1720 |
> |
of the system. For a sluggish bath which responds slowly to changes |
1721 |
> |
in the system coordinate, we may regard $\xi(t)$ as a constant |
1722 |
> |
$\xi(t) = \Xi_0$. Hence, the convolution integral becomes |
1723 |
> |
\[ |
1724 |
> |
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } = \xi _0 (x(t) - x(0)) |
1725 |
> |
\] |
1726 |
> |
and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1727 |
> |
\[ |
1728 |
> |
m\ddot x = - \frac{\partial }{{\partial x}}\left( {W(x) + |
1729 |
> |
\frac{1}{2}\xi _0 (x - x_0 )^2 } \right) + R(t), |
1730 |
> |
\] |
1731 |
> |
which can be used to describe dynamic caging effect. The other |
1732 |
> |
extreme is the bath that responds infinitely quickly to motions in |
1733 |
> |
the system. Thus, $\xi (t)$ can be taken as a $delta$ function in |
1734 |
> |
time: |
1735 |
> |
\[ |
1736 |
> |
\xi (t) = 2\xi _0 \delta (t) |
1737 |
> |
\] |
1738 |
> |
Hence, the convolution integral becomes |
1739 |
> |
\[ |
1740 |
> |
\int_0^t {\xi (t)\dot x(t - \tau )d\tau } = 2\xi _0 \int_0^t |
1741 |
> |
{\delta (t)\dot x(t - \tau )d\tau } = \xi _0 \dot x(t), |
1742 |
> |
\] |
1743 |
> |
and Equation \ref{introEuqation:GeneralizedLangevinDynamics} becomes |
1744 |
> |
\begin{equation} |
1745 |
> |
m\ddot x = - \frac{{\partial W(x)}}{{\partial x}} - \xi _0 \dot |
1746 |
> |
x(t) + R(t) \label{introEquation:LangevinEquation} |
1747 |
> |
\end{equation} |
1748 |
> |
which is known as the Langevin equation. The static friction |
1749 |
> |
coefficient $\xi _0$ can either be calculated from spectral density |
1750 |
> |
or be determined by Stokes' law for regular shaped particles.A |
1751 |
> |
briefly review on calculating friction tensor for arbitrary shaped |
1752 |
> |
particles is given in Sec.~\ref{introSection:frictionTensor}. |
1753 |
|
|
1754 |
|
\subsubsection{\label{introSection:secondFluctuationDissipation}The Second Fluctuation Dissipation Theorem} |
1755 |
< |
So we can define a new set of coordinates, |
1755 |
> |
|
1756 |
> |
Defining a new set of coordinates, |
1757 |
|
\[ |
1758 |
|
q_\alpha (t) = x_\alpha (t) - \frac{1}{{m_\alpha \omega _\alpha |
1759 |
|
^2 }}x(0) |
1760 |
< |
\] |
1761 |
< |
This makes |
1760 |
> |
\], |
1761 |
> |
we can rewrite $R(T)$ as |
1762 |
|
\[ |
1763 |
< |
R(t) = \sum\limits_{\alpha = 1}^N {g_\alpha q_\alpha (t)} |
1763 |
> |
R(t) = \sum\limits_{\alpha = 1}^N {g_\alpha q_\alpha (t)}. |
1764 |
|
\] |
1765 |
|
And since the $q$ coordinates are harmonic oscillators, |
1766 |
|
\[ |
1767 |
< |
\begin{array}{l} |
1767 |
> |
\begin{array}{c} |
1768 |
> |
\left\langle {q_\alpha ^2 } \right\rangle = \frac{{kT}}{{m_\alpha \omega _\alpha ^2 }} \\ |
1769 |
|
\left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle = \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t) \\ |
1770 |
|
\left\langle {q_\alpha (t)q_\beta (0)} \right\rangle = \delta _{\alpha \beta } \left\langle {q_\alpha (t)q_\alpha (0)} \right\rangle \\ |
1771 |
+ |
\left\langle {R(t)R(0)} \right\rangle = \sum\limits_\alpha {\sum\limits_\beta {g_\alpha g_\beta \left\langle {q_\alpha (t)q_\beta (0)} \right\rangle } } \\ |
1772 |
+ |
= \sum\limits_\alpha {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} \right\rangle \cos (\omega _\alpha t)} \\ |
1773 |
+ |
= kT\xi (t) \\ |
1774 |
|
\end{array} |
1775 |
|
\] |
1776 |
< |
|
1435 |
< |
\begin{align} |
1436 |
< |
\left\langle {R(t)R(0)} \right\rangle &= \sum\limits_\alpha |
1437 |
< |
{\sum\limits_\beta {g_\alpha g_\beta \left\langle {q_\alpha |
1438 |
< |
(t)q_\beta (0)} \right\rangle } } |
1439 |
< |
% |
1440 |
< |
&= \sum\limits_\alpha {g_\alpha ^2 \left\langle {q_\alpha ^2 (0)} |
1441 |
< |
\right\rangle \cos (\omega _\alpha t)} |
1442 |
< |
% |
1443 |
< |
&= kT\xi (t) |
1444 |
< |
\end{align} |
1445 |
< |
|
1776 |
> |
Thus, we recover the \emph{second fluctuation dissipation theorem} |
1777 |
|
\begin{equation} |
1778 |
|
\xi (t) = \left\langle {R(t)R(0)} \right\rangle |
1779 |
< |
\label{introEquation:secondFluctuationDissipation} |
1779 |
> |
\label{introEquation:secondFluctuationDissipation}. |
1780 |
|
\end{equation} |
1781 |
+ |
In effect, it acts as a constraint on the possible ways in which one |
1782 |
+ |
can model the random force and friction kernel. |
1783 |
|
|
1784 |
|
\subsection{\label{introSection:frictionTensor} Friction Tensor} |
1785 |
|
Theoretically, the friction kernel can be determined using velocity |
1955 |
|
B_{ij} = \delta _{ij} \frac{I}{{6\pi \eta R}} + (1 - \delta _{ij} |
1956 |
|
)T_{ij} |
1957 |
|
\] |
1958 |
< |
where \delta _{ij} is Kronecker delta function. Inverting matrix |
1958 |
> |
where $\delta _{ij}$ is Kronecker delta function. Inverting matrix |
1959 |
|
$B$, we obtain |
1960 |
|
|
1961 |
|
\[ |
1999 |
|
Form Equation \ref{introEquation:ResistanceTensorArbitraryOrigin}, |
2000 |
|
we can easily find out that the translational resistance tensor is |
2001 |
|
origin independent, while the rotational resistance tensor and |
2002 |
< |
translation-rotation coupling resistance tensor do depend on the |
2002 |
> |
translation-rotation coupling resistance tensor depend on the |
2003 |
|
origin. Given resistance tensor at an arbitrary origin $O$, and a |
2004 |
|
vector ,$r_{OP}(x_{OP}, y_{OP}, z_{OP})$, from $O$ to $P$, we can |
2005 |
|
obtain the resistance tensor at $P$ by |
2039 |
|
\] |
2040 |
|
where $x_OR$, $y_OR$, $z_OR$ are the components of the vector |
2041 |
|
joining center of resistance $R$ and origin $O$. |
1709 |
– |
|
1710 |
– |
%\section{\label{introSection:correlationFunctions}Correlation Functions} |