Lecture

Monte Carlo Moves in Simulation

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In course

CH-351: Molecular dynamics and Monte-Carlo simulation

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

Description

This lecture covers the concept of Monte Carlo moves in simulation, focusing on trial moves, translational moves, and more sophisticated moves. It explains how to enhance efficiency through biased moves and configurational bias, and compares Monte Carlo with Molecular Dynamics. The instructor discusses ensemble properties via Molecular Dynamics using the example of Ru-tris(bipy) in water, emphasizing the intrinsic dynamics to generate thermally relevant configurations.

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

Related concepts (32)

Ergodicity

In mathematics, ergodicity expresses the idea that a point of a moving system, either a dynamical system or a stochastic process, will eventually visit all parts of the space that the system moves in, in a uniform and random sense. This implies that the average behavior of the system can be deduced from the trajectory of a "typical" point. Equivalently, a sufficiently large collection of random samples from a process can represent the average statistical properties of the entire process.

Simulation

A simulation is the imitation of the operation of a real-world process or system over time. Simulations require the use of models; the model represents the key characteristics or behaviors of the selected system or process, whereas the simulation represents the evolution of the model over time. Often, computers are used to execute the simulation. Simulation is used in many contexts, such as simulation of technology for performance tuning or optimizing, safety engineering, testing, training, education, and video games.

Ergodic theory

Ergodic theory is a branch of mathematics that studies statistical properties of deterministic dynamical systems; it is the study of ergodicity. In this context, "statistical properties" refers to properties which are expressed through the behavior of time averages of various functions along trajectories of dynamical systems. The notion of deterministic dynamical systems assumes that the equations determining the dynamics do not contain any random perturbations, noise, etc.

Ensemble (mathematical physics)

In physics, specifically statistical mechanics, an ensemble (also statistical ensemble) is an idealization consisting of a large number of virtual copies (sometimes infinitely many) of a system, considered all at once, each of which represents a possible state that the real system might be in. In other words, a statistical ensemble is a set of systems of particles used in statistical mechanics to describe a single system. The concept of an ensemble was introduced by J. Willard Gibbs in 1902.

Ergodic hypothesis

In physics and thermodynamics, the ergodic hypothesis says that, over long periods of time, the time spent by a system in some region of the phase space of microstates with the same energy is proportional to the volume of this region, i.e., that all accessible microstates are equiprobable over a long period of time. Liouville's theorem states that, for a Hamiltonian system, the local density of microstates following a particle path through phase space is constant as viewed by an observer moving with the ensemble (i.

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