Lecture

Statistical Thermodynamics: Partition Function and Stirling Approximation

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In course

CH-242(b): Statistical mechanics for chemistry

Ce cours construit la base théorique universelle permettant de comprendre les systèmes à grand nombre de particules. Les méthodes introduites sont utilisées pour éclairer de nombreux phénomènes à travers la chimie. ...

Description

This lecture covers the concept of the partition function in statistical thermodynamics, focusing on the Stirling approximation for large systems. It explains how to find the most probable occupancy for a state with a specific energy and the values for a and b. The lecture also delves into the canonical partition function and the internal energy of a closed system, emphasizing the importance of recognizing the higher order terms and neglecting small values. Additionally, it discusses the heat capacity at constant volume and the calculation of Cv using the Stirling approximation.

Instructors (2)

Christopher Scott Reilly

Andreas Osterwalder

EDUCATION: 2002: Dr.sc.nat. (ETH Zurich) 1998: Dipl.Chem. (ETH Zurich) ACADEMIC POSITIONS: since 2015: Senior Scientist at EPFL 2009-2015: SNSF-funded Professor at EPFL 2005-2009: Group leader, Fritz-Haber-Institute of the Max-Planck-Society in Berlin (Department of Molecular Physics under Prof. G. Meijer) 2002-2005: Postdoctoral Fellow at UC Berkeley (Group of Prof. D.M.Neumark) 1998-2002: PhD Student at ETHZ (Group of Prof. F.Merkt)

Official source

Ontological neighbourhood

Physics

Thermodynamics: Statistical mechanics

Related concepts (32)

Partition function (statistical mechanics)

In physics, a partition function describes the statistical properties of a system in thermodynamic equilibrium. Partition functions are functions of the thermodynamic state variables, such as the temperature and volume. Most of the aggregate thermodynamic variables of the system, such as the total energy, free energy, entropy, and pressure, can be expressed in terms of the partition function or its derivatives. The partition function is dimensionless.

Lasso (statistics)

In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso or LASSO) is a regression analysis method that performs both variable selection and regularization in order to enhance the prediction accuracy and interpretability of the resulting statistical model. It was originally introduced in geophysics, and later by Robert Tibshirani, who coined the term. Lasso was originally formulated for linear regression models. This simple case reveals a substantial amount about the estimator.

Partition function (mathematics)

The partition function or configuration integral, as used in probability theory, information theory and dynamical systems, is a generalization of the definition of a partition function in statistical mechanics. It is a special case of a normalizing constant in probability theory, for the Boltzmann distribution. The partition function occurs in many problems of probability theory because, in situations where there is a natural symmetry, its associated probability measure, the Gibbs measure, has the Markov property.

Elastic net regularization

In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. The elastic net method overcomes the limitations of the LASSO (least absolute shrinkage and selection operator) method which uses a penalty function based on Use of this penalty function has several limitations. For example, in the "large p, small n" case (high-dimensional data with few examples), the LASSO selects at most n variables before it saturates.

Heat capacity

Heat capacity or thermal capacity is a physical property of matter, defined as the amount of heat to be supplied to an object to produce a unit change in its temperature. The SI unit of heat capacity is joule per kelvin (J/K). Heat capacity is an extensive property. The corresponding intensive property is the specific heat capacity, found by dividing the heat capacity of an object by its mass. Dividing the heat capacity by the amount of substance in moles yields its molar heat capacity.

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