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Lecture

Markov Chain: Configuration Sampling

In course

CH-351: Molecular dynamics and Monte-Carlo simulation

Introduction to molecular dynamics and Monte-Carlo simulation methods. ...

Description

This lecture explains the concept of a Markov process and Markov chain in the context of the Metropolis algorithm for generating random configurations in configurational space with correct relative Boltzmann weights. The instructor discusses the importance of starting with a suitable initial configuration, making random changes to the system, and accepting or rejecting new configurations based on their relative Boltzmann weights.

Instructor

Ursula Röthlisberger

U. Röthlisberger was born in Solothurn (Switzerland). In 1988 she made her diploma in Physical Chemistry in the group of Prof. Ernst Schumacher at the University of Berne (Switzerland). Her Ph.D. thesis was done in collaboration with Dr. Wanda Andreoni at the IBM Zurich Research Laboratory in Rüschlikon. After finishing her Ph.D in 1991 she spent some time as a postdoctoral research assistant at the IBM Research Lab. From 1992-1995 she was a postdoctoral research assistant in the group of Prof. Michael L. Klein at the University of Pennsylvania in Philadelphia (USA). In 1994 she was awarded an advanced researcher fellowship (Profil 2) from the Swiss National Science Foundation. Before starting her Profile 2-fellowship she spent another year as postdoctoral research assistant in the group of Prof. Michele Parrinello at the Max-Planck-Institute for Solid State Physics in Stuttgart, Germany. In 1996 she moved as Profile 2-fellow to the ETH in Zurich, hosted by the group of Prof. Wilfred F. van Gunsteren. In 1997 she became Assistant Professor of Computer-Aided Inorganic Chemistry at the ETH Zurich.

Official source

Ontological neighbourhood

Physics

Thermodynamics: Statistical mechanics

Related concepts (32)

Canonical ensemble

In statistical mechanics, a canonical ensemble is the statistical ensemble that represents the possible states of a mechanical system in thermal equilibrium with a heat bath at a fixed temperature. The system can exchange energy with the heat bath, so that the states of the system will differ in total energy. The principal thermodynamic variable of the canonical ensemble, determining the probability distribution of states, is the absolute temperature (symbol: T).

Grand canonical ensemble

In statistical mechanics, the grand canonical ensemble (also known as the macrocanonical ensemble) is the statistical ensemble that is used to represent the possible states of a mechanical system of particles that are in thermodynamic equilibrium (thermal and chemical) with a reservoir. The system is said to be open in the sense that the system can exchange energy and particles with a reservoir, so that various possible states of the system can differ in both their total energy and total number of particles.

Probability

Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur. A simple example is the tossing of a fair (unbiased) coin.

Markov chain

A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. Informally, this may be thought of as, "What happens next depends only on the state of affairs now." A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC). A continuous-time process is called a continuous-time Markov chain (CTMC).

Microcanonical ensemble

In statistical mechanics, the microcanonical ensemble is a statistical ensemble that represents the possible states of a mechanical system whose total energy is exactly specified. The system is assumed to be isolated in the sense that it cannot exchange energy or particles with its environment, so that (by conservation of energy) the energy of the system does not change with time. The primary macroscopic variables of the microcanonical ensemble are the total number of particles in the system (symbol: N), the system's volume (symbol: V), as well as the total energy in the system (symbol: E).

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