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 Vesely MolSim Tutorial

# Statistical Physics and Simulation / Molecular Simulation Course material Academic years 2001-..

Franz J. Vesely
University of Vienna

franz.vesely@univie.ac.at

Chapter 6 in my web tutorial on Computational Physics refers to simulation. Here follows a more extensive treatment which I also use in a more advanced course on "Statistical Mechanical Simulation".

Ludwig Boltzmann would have loved simulation!

Subsections

## Molecular simulation in the U of Vienna Physics curriculum

In 1975 I was a postdoc guest of Konrad Singer at Royal Holloway College. David Adams, then a postdoc himself, showed me how to do MD, and how to do it elegantly. I am still striving to learn that second part.

Back in Vienna I started to offer courses on the basics and the current applications of molecular simulation. In 1978 my textbook "Computerexperimente an Fluessigkeitsmodellen" (Physik Weinheim, ISBN 3-87664-041-5) appeared. Other colleagues at the Institute of Experimental Physics, notably Harald Posch and Martin Neumann, joined me in applying and teaching various aspects of molecular simulation.

The growing interest of students forced Martin and me to broaden the content of the courses we offered - the market for molecular simulators being rather limited. Thus we installed a regular study curriculum in "Computational Physics", covering topics that range from the basics of finite difference calculus to hydrodynamic and quantum calculations. Every other year a special course on molecular simulation is offered.

In 1993 I published the German version of a textbook on CompPhys, and the first English edition appeared in 1994; The current version of "Computational Physics - An Introduction", Kluwer-Plenum, ISBN 0-306-46631-7) is from 2001.

Martin Neumann and I alternate in teaching the CompPhys course which comprises 3 weekly lectures and 2 hours of workshop, spanning one full academic year. In the subsequent year students may attend an Advanced Workshop preparing them for their Master's thesis in CompPhys.

## A little history

- 1952, Metropolis, Rosenbluth, Teller: Monte Carlo on 32 Hard disks

- 1957, Alder: Molecular Dynamics of hard spheres; microscopic vortices!

- 1964, Rahman, Verlet: Lennard Jones fluid

- 1968-70, Hoover, Ree: Melting transition for hard disks and hard spheres, then for soft (LJ) spheres!

- 1970: Harvest time for Liquid State Physics; more complex fluids, too (water, polymers, ...)

- 1987: Holian, Hoover, Posch: Irreversibility explained

- 1990+ : Diversification, Simulation as standard method in StatMech

## 1.1 Model Systems of Statistical Mechanics

Simple models comprise (a) particles moving in space; (b) spins flipping at fixed positions.

Subsections

## Fluids

Made up of classical mass points or rigid bodies, interacting by pair forces (and possibly torques).

Table 1.1: Isotropic model potentials in statistical-mechanical simulation: $u=u(r)$
 Hard spheres $\begin{eqnarray} u(r)&=& \infty \;\;{\rm if}\;\; r < r_{0} \\ &=& 0 \;\;\; {\rm if} \;\; r \geq r_{0} \end{eqnarray}$ First approximation in many applications Lennard-Jones $u(r)= 4 \epsilon \left[ \left(\frac{r}{\sigma}\right)^{-12} - \left(\frac{r}{\sigma}\right)^{-6} \right]$ Noble gas atoms; nearly spherical molecules Isotropic Kihara $u(r)=$ $4 \epsilon \left[ \left( \frac{\textstyle{r-a}}{\textstyle{\sigma-a}} \right)^{-12} - \left(\frac{\textstyle{r-a}}{\textstyle{\sigma-a}}\right)^{-6} \right]$ Noble gas atoms; nearly spherical molecules, $\;\;\;(a=0.05-0.1\sigma)$ Harmonic $u(r)= A \left( r-r_{0}\right)^{2}$ Intramolecular bonds, if $kT$ is small compared to the bond energy Morse $u(r)= A\left[ e^{\textstyle{-2b(r\!-\!r_{0})}}- 2 e^{\textstyle{-b(r\!-\!r_{0})}} \right]$ Intramolecular bonds, if $kT$ is comparable to the bond energy Born-Huggins-Mayer $u(r)= \frac{\textstyle{q_{1}q_{2}}}{\textstyle{4\pi \epsilon_{0}r}} + B e^{\textstyle{-\alpha r}} - \frac{\textstyle{C}}{\textstyle{r^{6}}}- \frac{\textstyle{D}}{\textstyle{r^{8}}}$ Ionic melts; $q_{i}$ are the ion charges

Table 1.2: Anisotropic model potentials in statistical-mechanical simulation: $u(12)=u(r_{12}, e_{1}, e_{2})$
 Hard dumbbells, hard spherocylinders, etc. $u(12)= \infty \;\;$ if overlap $= 0 \;\;\;$ otherwise First approximation to rigid molecules Interaction site models, rigid $u(12)=$ sum of isotropic pair energies $\textstyle{u(r_{i(1),j(2)})}$, where several interaction sites $i$ and $j$ are in fixed positions on molecules $1$ and $2$, respectively Rigid molecules Interaction site models with non-rigid bonds $u(12)=$ sum of isotropic pair energies, both intra- and intermolecular Non-rigid molecules Kramers-type $u(12)=$ sum of isotropic pair energies, exclusively between sites on different molecules; certain intramolecular distances (bonds) and/or angles are fixed Flexible molecules, from ethane to biopolymers Stockmayer $u(12)=$ Lennard-Jones + point dipoles First approximation to small polar molecules Anisotropic Kihara $u(12)= 4 \epsilon \left[ \left( \frac{\textstyle{\rho_{12}}}{\textstyle{\sigma}}\right)^{-12} - \left( \frac{\textstyle{\rho_{12}}}{\textstyle{\sigma}}\right)^{-6} \right]$ where $\rho_{12}$ is the shortest distance between two linear rods Rigid linear molecules with distributed Lennard-Jones interaction Gay-Berne $u(12)=$ $4 \epsilon(12) \left[ \left( \frac{\textstyle{r_{12}-\sigma(12)+\sigma_{0}}}{\textstyle{\sigma_{0}}}\right)^{-12} \right.$ $- \left. \left( \frac{\textstyle{r_{12}-\sigma(12)+\sigma_{0}}}{\textstyle{\sigma_{0}}}\right)^{-6} \right]$ where $\sigma(12)$ and $\epsilon(12)$ depend on $r_{12}, e_{1}, e_{2}$ and certain substance-specific shape parameters Liquid crystal molecules of ellipsoidal shape, with smoothly distributed Lennard-Jones sites

At time $t$, combine the position vectors of the $N$ atoms into a vector $\Gamma_{c}\equiv$ $\{ r_{1} \dots$ $r_{N}\}$. The set of all possible such vectors spans the $3N$-dimensional "configuration space" $\Gamma_{c}$.

Let $a(\Gamma_{c})$ be some property of the $N$-body system, depending on the positions of all particles (i.e. of the microstate $\Gamma_{c}$.) The thermodynamic average of $a$ is given by
$\langle a \rangle = \int_{\textstyle \Gamma_{c}} a(\Gamma_{c}) p(\Gamma_{c}) d\Gamma_{c}$

where $p(\Gamma_{c})$ is the probability density at the configuration space point $\Gamma_{c}$.

Examples:
• Internal energy:
$U=NkT+\frac{1}{2} \langle \sum_{i} \sum_{j \neq i} u(r_{ij}) \rangle$

• Pressure:
$p=\frac{\textstyle NkT}{\textstyle V} - \frac{\textstyle 1}{\textstyle 6V} \langle \sum_{i} \sum_{j \neq i} r_{ij} \left. \frac{\textstyle du}{\textstyle dr} \right|_{r_{ij}} \rangle$

Problem: the probability density $p(\Gamma_{c})$ is known only up to an indetermined normalizing factor. Thus, $p_{can}(\Gamma_{c})$ $\propto exp[-E(\Gamma_{c})/kT]$, but the normalizing denominator $Q$ (the Configurational Partition Function) in

$1 = \int p(\Gamma_{c}) d\Gamma_{c} \equiv \frac{\textstyle 1}{\textstyle Q} \int e^{\textstyle -E(\Gamma_{c})/kT} d\Gamma_{c}$
is not known.

## Spin systems

Basic model of ferromagnetic solids: atoms are fixed on the vertices of a lattice. Their dipole vectors (spins) may have varying directions: either up/down (Ising model) or any direction (Heisenberg model).

Microscopic configuration $\Gamma_{c}$: given by the $N$ spins on the lattice.

Example: Two-dimensional square Ising lattice; only the four nearest spins contribute to the energy of spin $\sigma_{i}$ ($= \pm 1$). (Three dimensions: six nearest neighbors). The total energy is
$E=-\frac{\textstyle A}{\textstyle 2} \sum_{\textstyle i=1}^{\textstyle N} \sum_{\textstyle j(i)=1}^{\textstyle 4\; or \;6} \sigma_{i} \sigma_{j(i)}$

($A$ ... coupling constant).

$\Longrightarrow$ Magnetic polarization
$M \equiv \sum_{\textstyle i=1}^{\textstyle N} \sigma_{i}$

may be determined as a function of temperature. If a magnetic field $H$ is applied, the additional potential energy is $E_{H}=-H \sum_{i} \sigma_{i}$.

vesely may-06

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