Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Lecture

Statistical Distribution in Canonical and Grand-Canonical Ensembles

In course

PHYS-105: Advanced physics II (thermodynamics)

Ce cours présente la thermodynamique en tant que théorie permettant une description d'un grand nombre de phénomènes importants en physique, chimie et ingéniere, et d'effets de transport. Une introduction à la physique statistique renforce les notions acqu ...

Description

This lecture covers the statistical distribution in canonical and grand-canonical ensembles, focusing on the bathed system and the calculation to avoid normalization. The slides present formulas and concepts related to the distribution of states and energies in different ensembles.

Instructors (2)

Jean-Philippe Ansermet

Jean-Philippe Ansermet was born March 1, 1957 in Lausanne (legal origin Vaumarcus, NE). He obtained a diploma as physics engineer of EPFL in 1980. He went on to get a PhD from the University of Illinois at Urbana-Champaign where, from 1985 to 1987, he persued as post-doc with Prof. Slichter his research on catalysis by solid state NMR studies of molecules bound to the surface of catalysts. From 1987 to 1992 he worked at the materials research center of Ciba-Geigy, on polymers for microelectronics, composites, dielectrics and organic charge transfer complexes. In March 1992, as professor of experimental physics, he developed a laboratory on the theme of nanostructured materials and turned full professor in 1995. Since 1992, he teaches classical mechanics, first to future engineering students, since 2004 to physics majors. Since 2000, he teaches thermodynamics also, to the same group of students. He offers a graduate course in spintronics, and another on spin dynamics. His research activities concern the fabrication and properties of magnetic nanostructures produced by electrodeposition. His involvement since the early days of spintronics have allowed him to gain recognition for his work on giant magnetoresistance (CPP-GMR), magnetic relaxation of single nanostructures, and was among the leading groups demonstrating magnetization reversal by spin-polarized currents. Furthermore, his group uses nuclear magnetic resonance , on the one hand as means of investigation of surfaces and electrodes, on the other hand, as a local probe of the electronic properties of complex ferromagnetic oxides.

Cécile Hébert

Cécile Hébert was born in France in 1970. She obtained her Ingeneer degree (physics) and her PhD degree (design of a new energy filter for transmission electron microscopy, under the direction of Prof. B. Jouffrey) at the Ecole Centrale in Paris. As a post doc with Prof. P. Schattschneider, she was working on the calculation of the fine structure of ionisation edges in EELS. As a research assistant at the Vienna University of Technology, she was one of the main participant to the CHIRALTEM project dealing with the measurement of magnetic circular dichroism in the electron microscope.

Official source

Ontological neighbourhood

Physics

Thermodynamics: Statistical mechanics

Related concepts (23)

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.

Boltzmann distribution

In statistical mechanics and mathematics, a Boltzmann distribution (also called Gibbs distribution) is a probability distribution or probability measure that gives the probability that a system will be in a certain state as a function of that state's energy and the temperature of the system. The distribution is expressed in the form: where pi is the probability of the system being in state i, exp is the exponential function, εi is the energy of that state, and a constant kT of the distribution is the product of the Boltzmann constant k and thermodynamic temperature T.

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).

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.

Related lectures (34)

Statistical Physics: Isolated Systems and Entropy

Covers statistical physics, isolated systems, entropy, and the Boltzmann distribution.

Canonical Ensemble: Probability Distribution

Explores the probability distribution in the canonical ensemble and the Boltzmann distribution.

Molecular Dynamics and Monte Carlo

Covers computational methods for molecular systems at finite temperature, emphasizing stochastic sampling and time evolution simulations.

Boltzmann Distribution & Thermodynamic Ensembles

Explores Boltzmann distribution, quantum particles, thermodynamic ensembles, and ensemble sampling methods.

Sampling the Canonical Ensemble