Fermi-Dirac Statistics and the Number Theory
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Canonical Ensemble
ME346A Introduction to Statistical Mechanics { Wei Cai { Stanford University { Win 2011 Handout 8. Canonical Ensemble January 26, 2011 Contents Outline • In this chapter, we will establish the equilibrium statistical distribution for systems maintained at a constant temperature T , through thermal contact with a heat bath. • The resulting distribution is also called Boltzmann's distribution. • The canonical distribution also leads to definition of the partition function and an expression for Helmholtz free energy, analogous to Boltzmann's Entropy formula. • We will study energy fluctuation at constant temperature, and witness another fluctuation- dissipation theorem (FDT) and finally establish the equivalence of micro canonical ensemble and canonical ensemble in the thermodynamic limit. (We first met a mani- festation of FDT in diffusion as Einstein's relation.) Reading Assignment: Reif x6.1-6.7, x6.10 1 1 Temperature For an isolated system, with fixed N { number of particles, V { volume, E { total energy, it is most conveniently described by the microcanonical (NVE) ensemble, which is a uniform distribution between two constant energy surfaces. const E ≤ H(fq g; fp g) ≤ E + ∆E ρ (fq g; fp g) = i i (1) mc i i 0 otherwise Statistical mechanics also provides the expression for entropy S(N; V; E) = kB ln Ω. In thermodynamics, S(N; V; E) can be transformed to a more convenient form (by Legendre transform) of Helmholtz free energy A(N; V; T ), which correspond to a system with constant N; V and temperature T . Q: Does the transformation from N; V; E to N; V; T have a meaning in statistical mechanics? A: The ensemble of systems all at constant N; V; T is called the canonical NVT ensemble. -
Statistical Physics– a Second Course
Statistical Physics– a second course Finn Ravndal and Eirik Grude Flekkøy Department of Physics University of Oslo September 3, 2008 2 Contents 1 Summary of Thermodynamics 5 1.1 Equationsofstate .......................... 5 1.2 Lawsofthermodynamics. 7 1.3 Maxwell relations and thermodynamic derivatives . .... 9 1.4 Specificheatsandcompressibilities . 10 1.5 Thermodynamicpotentials . 12 1.6 Fluctuations and thermodynamic stability . .. 15 1.7 Phasetransitions ........................... 16 1.8 EntropyandGibbsParadox. 18 2 Non-Interacting Particles 23 1 2.1 Spin- 2 particlesinamagneticfield . 23 2.2 Maxwell-Boltzmannstatistics . 28 2.3 Idealgas................................ 32 2.4 Fermi-Diracstatistics. 35 2.5 Bose-Einsteinstatistics. 36 3 Statistical Ensembles 39 3.1 Ensemblesinphasespace . 39 3.2 Liouville’stheorem . .. .. .. .. .. .. .. .. .. .. 42 3.3 Microcanonicalensembles . 45 3.4 Free particles and multi-dimensional spheres . .... 48 3.5 Canonicalensembles . 50 3.6 Grandcanonicalensembles . 54 3.7 Isobaricensembles .......................... 58 3.8 Informationtheory . .. .. .. .. .. .. .. .. .. .. 62 4 Real Gases and Liquids 67 4.1 Correlationfunctions. 67 4.2 Thevirialtheorem .......................... 73 4.3 Mean field theory for the van der Waals equation . 76 4.4 Osmosis ................................ 80 3 4 CONTENTS 5 Quantum Gases and Liquids 83 5.1 Statisticsofidenticalparticles. .. 83 5.2 Blackbodyradiationandthephotongas . 88 5.3 Phonons and the Debye theory of specific heats . 96 5.4 Bosonsatnon-zerochemicalpotential . -
Microcanonical, Canonical, and Grand Canonical Ensembles Masatsugu Sei Suzuki Department of Physics, SUNY at Binghamton (Date: September 30, 2016)
The equivalence: microcanonical, canonical, and grand canonical ensembles Masatsugu Sei Suzuki Department of Physics, SUNY at Binghamton (Date: September 30, 2016) Here we show the equivalence of three ensembles; micro canonical ensemble, canonical ensemble, and grand canonical ensemble. The neglect for the condition of constant energy in canonical ensemble and the neglect of the condition for constant energy and constant particle number can be possible by introducing the density of states multiplied by the weight factors [Boltzmann factor (canonical ensemble) and the Gibbs factor (grand canonical ensemble)]. The introduction of such factors make it much easier for one to calculate the thermodynamic properties. ((Microcanonical ensemble)) In the micro canonical ensemble, the macroscopic system can be specified by using variables N, E, and V. These are convenient variables which are closely related to the classical mechanics. The density of states (N E,, V ) plays a significant role in deriving the thermodynamic properties such as entropy and internal energy. It depends on N, E, and V. Note that there are two constraints. The macroscopic quantity N (the number of particles) should be kept constant. The total energy E should be also kept constant. Because of these constraints, in general it is difficult to evaluate the density of states. ((Canonical ensemble)) In order to avoid such a difficulty, the concept of the canonical ensemble is introduced. The calculation become simpler than that for the micro canonical ensemble since the condition for the constant energy is neglected. In the canonical ensemble, the system is specified by three variables ( N, T, V), instead of N, E, V in the micro canonical ensemble. -
LECTURE 9 Statistical Mechanics Basic Methods We Have Talked
LECTURE 9 Statistical Mechanics Basic Methods We have talked about ensembles being large collections of copies or clones of a system with some features being identical among all the copies. There are three different types of ensembles in statistical mechanics. 1. If the system under consideration is isolated, i.e., not interacting with any other system, then the ensemble is called the microcanonical ensemble. In this case the energy of the system is a constant. 2. If the system under consideration is in thermal equilibrium with a heat reservoir at temperature T , then the ensemble is called a canonical ensemble. In this case the energy of the system is not a constant; the temperature is constant. 3. If the system under consideration is in contact with both a heat reservoir and a particle reservoir, then the ensemble is called a grand canonical ensemble. In this case the energy and particle number of the system are not constant; the temperature and the chemical potential are constant. The chemical potential is the energy required to add a particle to the system. The most common ensemble encountered in doing statistical mechanics is the canonical ensemble. We will explore many examples of the canonical ensemble. The grand canon- ical ensemble is used in dealing with quantum systems. The microcanonical ensemble is not used much because of the difficulty in identifying and evaluating the accessible microstates, but we will explore one simple system (the ideal gas) as an example of the microcanonical ensemble. Microcanonical Ensemble Consider an isolated system described by an energy in the range between E and E + δE, and similar appropriate ranges for external parameters xα. -
Statistical Physics Syllabus Lectures and Recitations
Graduate Statistical Physics Syllabus Lectures and Recitations Lectures will be held on Tuesdays and Thursdays from 9:30 am until 11:00 am via Zoom: https://nyu.zoom.us/j/99702917703. Recitations will be held on Fridays from 3:30 pm until 4:45 pm via Zoom. Recitations will begin the second week of the course. David Grier's office hours will be held on Mondays from 1:30 pm to 3:00 pm in Physics 873. Ankit Vyas' office hours will be held on Fridays from 1:00 pm to 2:00 pm in Physics 940. Instructors David G. Grier Office: 726 Broadway, room 873 Phone: (212) 998-3713 email: [email protected] Ankit Vyas Office: 726 Broadway, room 965B Email: [email protected] Text Most graduate texts in statistical physics cover the material of this course. Suitable choices include: • Mehran Kardar, Statistical Physics of Particles (Cambridge University Press, 2007) ISBN 978-0-521-87342-0 (hardcover). • Mehran Kardar, Statistical Physics of Fields (Cambridge University Press, 2007) ISBN 978- 0-521-87341-3 (hardcover). • R. K. Pathria and Paul D. Beale, Statistical Mechanics (Elsevier, 2011) ISBN 978-0-12- 382188-1 (paperback). Undergraduate texts also may provide useful background material. Typical choices include • Frederick Reif, Fundamentals of Statistical and Thermal Physics (Waveland Press, 2009) ISBN 978-1-57766-612-7. • Charles Kittel and Herbert Kroemer, Thermal Physics (W. H. Freeman, 1980) ISBN 978- 0716710882. • Daniel V. Schroeder, An Introduction to Thermal Physics (Pearson, 1999) ISBN 978- 0201380279. Outline 1. Thermodynamics 2. Probability 3. Kinetic theory of gases 4. -
Lecture 7: Ensembles
Matthew Schwartz Statistical Mechanics, Spring 2019 Lecture 7: Ensembles 1 Introduction In statistical mechanics, we study the possible microstates of a system. We never know exactly which microstate the system is in. Nor do we care. We are interested only in the behavior of a system based on the possible microstates it could be, that share some macroscopic proporty (like volume V ; energy E, or number of particles N). The possible microstates a system could be in are known as the ensemble of states for a system. There are dierent kinds of ensembles. So far, we have been counting microstates with a xed number of particles N and a xed total energy E. We dened as the total number microstates for a system. That is (E; V ; N) = 1 (1) microstatesk withsaXmeN ;V ;E Then S = kBln is the entropy, and all other thermodynamic quantities follow from S. For an isolated system with N xed and E xed the ensemble is known as the microcanonical 1 @S ensemble. In the microcanonical ensemble, the temperature is a derived quantity, with T = @E . So far, we have only been using the microcanonical ensemble. 1 3 N For example, a gas of identical monatomic particles has (E; V ; N) V NE 2 . From this N! we computed the entropy S = kBln which at large N reduces to the Sackur-Tetrode formula. 1 @S 3 NkB 3 The temperature is T = @E = 2 E so that E = 2 NkBT . Also in the microcanonical ensemble we observed that the number of states for which the energy of one degree of freedom is xed to "i is (E "i) "i/k T (E " ). -
Arxiv:Cond-Mat/0411176V1
PARTITION FUNCTIONS FOR STATISTICAL MECHANICS WITH MICROPARTITIONS AND PHASE TRANSITIONS AJAY PATWARDHAN Physics department, St Xavier’s college, Mahapalika Marg , Mumbai 400001,India Visitor, Institute of Mathematical Sciences, CIT campus, Tharamani, Chennai, India [email protected] ABSTRACT The fundamentals of Statistical Mechanics require a fresh definition in the context of the developments in Classical Mechanics of integrable and chaotic systems. This is done with the introduction of Micro partitions; a union of disjoint components in phase space. Partition functions including the invariants ,Kolmogorov entropy and Euler number are introduced. The ergodic hypothesis for partial ergodicity is discussed. In the context of Quantum Mechanics the presence of symmetry groups with irreducible representations gives rise to degenerate and non degenerate spectrum for the Hamiltonian. Quantum Statistical Mechanics is formulated including these two cases ; by including the multiplicity dimension of the group representation and the Casimir invariants into the Partition function. The possibility of new kinds of phase transitions is discussed. The occurence of systems with non simply connected configuration spaces and Quantum Mechanics for them , also requires a possible generalisation of Statistical Mechanics. The developments of Quantum pure, mixed and en- tangled states has made it neccessary to understand the Statistical Mechanics of the multipartite N particle system . And to obtain in terms of the den- sity matrices , written in energy basis , the Trace of the Gibbs operator as the Partition function and use it to get statistical averages of operators. There are some issues of definition, interpretation and application that are discussed. INTRODUCTION A Statistical Mechanics of systems with properties of symmetry , chaos, topology and mixtures is required for a variety of physics ; including con- densed matter and high energy physics. -
8.044 Lecture Notes Chapter 6: Statistical Mechanics at Fixed Temperature (Canonical Ensemble)
8.044 Lecture Notes Chapter 6: Statistical Mechanics at Fixed Temperature (Canonical Ensemble) Lecturer: McGreevy 6.1 Derivation of the Canonical Ensemble . 6-2 6.2 Monatomic, classical, ideal gas, at fixed T ................... 6-11 6.3 Two-level systems, re-revisited . 6-16 6.4 Classical harmonic oscillators and equipartition of energy . 6-20 6.5 Quantum harmonic oscillators . 6-23 6.6 Heat capacity of a diatomic ideal gas . 6-30 6.7 Paramagnetism . 6-35 6.7.1 Cooling by adiabatic demagnetization . 6-39 6.8 The Third Law of Thermodynamics . 6-42 Reading: Greytak, Notes on the Canonical Ensemble; Baierlein, Chapters 5, 13, 14. Optional Reading for x6.6: Greytak, Notes on (Raman Spectroscopy of) Diatomic Molecules. 6-1 6.1 Derivation of the Canonical Ensemble In Chapter 4, we studied the statistical mechanics of an isolated system. This meant fixed E; V; N. From some fundamental principles (really, postulates), we developed an algorithm for cal- culating (which turns out not to be so practical, as you'll have seen e.g. if you thought about the random 2-state systems on pset 6): 1. Model the system 2. Count microstates for given E: Ω(E; V; N). 3. from Ω, derive thermodynamics by S = kB ln Ω. Ω0(X) 4. from Ω, derive microscopic information by p(X) = Ω . The fixed-energy constraint makes the counting difficult, in all but the simplest problems (the ones we've done). Fixing the temperature happens to be easier to analyze in practice. Consider a system 1 which is not isolated, but rather is in thermal contact with a heat bath 2 , and therefore held at fixed temperature T equal to that of the bath. -
A Generalized Quantum Microcanonical Ensemble
A generalized quantum microcanonical ensemble Jan Naudts and Erik Van der Straeten Departement Fysica, Universiteit Antwerpen, Groenenborgerlaan 171, 2020 Antwerpen, Belgium E-mail [email protected], [email protected] February 1, 2008 Abstract We discuss a generalized quantum microcanonical ensemble. It describes isolated systems that are not necessarily in an eigenstate of the Hamilton operator. Statistical averages are obtained by a combi- nation of a time average and a maximum entropy argument to resolve the lack of knowledge about initial conditions. As a result, statisti- cal averages of linear observables coincide with values obtained in the canonical ensemble. Non-canonical averages can be obtained by tak- ing into account conserved quantities which are non-linear functions of the microstate. arXiv:quant-ph/0602039v2 16 May 2006 1 Introduction In a recent paper, Goldstein et al [1] argue that the equilibrium state of a finite quantum system should not be described by a density operator ρ but by a probability distribution f(ψ) of wavefunctions ψ in Hilbert space . H The relation between both is then ρ = dψ f(ψ) ψ ψ . (1) | ih | ZS Integrations over normalised wavefunctions may seem weird, but have been used before in [2] and in papers quoted there. Goldstein et al develop their 1 arguments for a canonical ensemble at inverse temperature β. For the micro- canonical ensemble at energy E they follow the old works of Schr¨odinger and Bloch and define the equilibrium distribution as the uniform distribution on a subset of wavefunctions which are linear combinations of eigenfunctions of the Hamiltonian, with corresponding eigenvalues in the range [E ǫ, E + ǫ]. -
The Canonical Ensemble and Microscopic Definition of T
From Atoms to Materials: Predictive Theory and Simulations Week 4: Connecting Atomic Processes to the Macroscopic World Lecture 4.2: The Canonical Ensemble and Microscopic Definition of T Ale Strachan [email protected] School of Materials Engineering & Birck Nanotechnology Center Purdue University West Lafayette, Indiana USA Statistical Mechanics: microcanonical ensemble Equal probability postulate: N,V,E Relationship between microscopic world and thermo: S = k logΩ(E,V , N ) Statistical Mechanics: canonical ensemble E+Ebath=Etot=Constant Sys Bath Probability of system being in a microscopic ({Ri},{Pi}) state with energy E: Since E<<Etot we expand logΩbath around Etot: Maxwell – Boltzmann distribution Canonical ensemble and thermodynamics Maxwell-Boltzmann distribution: Partition function: E P(E) − kT E e Ω(N,V , E) log Z(N,V ,T ) = logΩ(N,V , E)− kT Helmholtz free energy: E Canonical ensemble: averages Consider a quantity that depends on the atomic positions and momenta: In equilibrium the average values of A is: Ensemble average When you measure the quantity A in an experiment or MD simulation: Time average Under equilibrium conditions temporal and ensemble averages are equal Various important ensembles Microcanonical (NVE) Canonical (NVT) Isobaric/isothermal (NPT) Probability distributions E E−PV − − kT kT Z(T,V , N ) = ∑e Z P (T, P, N ) = ∑ ∑e micro V micro Free energies (atomistic ↔ macroscopic thermodynamics) S = k logΩ(E,V , N ) F(T,V , N ) = −kT log Z G(T, P, N ) = −kT log Z p Canonical ensemble: equipartition of energy Average value of a quantity that appears squared in the Hamiltonian Change of variable: Equipartition of energy: Any degree of freedom that appears squared in the Hamiltonian contributes 1/2kT of energy Equipartition of energy: MD temperature 3N K = kT 2 In most cases c.m. -
LECTURE 13 Maxwell–Boltzmann, Fermi, and Bose Statistics Suppose We Have a Gas of N Identical Point Particles in a Box of Volume V
LECTURE 13 Maxwell–Boltzmann, Fermi, and Bose Statistics Suppose we have a gas of N identical point particles in a box of volume V. When we say “gas”, we mean that the particles are not interacting with one another. Suppose we know the single particle states in this gas. We would like to know what are the possible states of the system as a whole. There are 3 possible cases. Which one is appropriate depends on whether we use Maxwell–Boltzmann, Fermi or Bose statistics. Let’s consider a very simple case in which we have 2 particles in the box and the box has 2 single particle states. How many distinct ways can we put the particles into the 2 states? Maxwell–Boltzmann Statistics: This is sometimes called the classical case. In this case the particles are distinguishable so let’s label them A and B. Let’s call the 2 single particle states 1 and 2. For Maxwell–Boltzmann statistics any number of particles can be in any state. So let’s enumerate the states of the system: SingleParticleState 1 2 ----------------------------------------------------------------------- AB AB A B B A We get a total of 4 states of the system as a whole. Half of the states have the particles bunched in the same state and half have them in separate states. Bose–Einstein Statistics: This is a quantum mechanical case. This means that the particles are indistinguishable. Both particles are labelled A. Recall that bosons have integer spin: 0, 1, 2, etc. For Bose statistics any number of particles can be in one state. -
4. Classical Phase Space 4.1. Phase Space and Probability Density
4. Classical phase space quantum states in the part of the space under consideration. 4.1. Phase space and probability density The ensemble or statistical set consists, at a given We consider a system of N particles in a d-dimensional moment, of all those phase space points which correspond space. Canonical coordinates and momenta to a given macroscopic system. Corresponding to a macro(scopic) state of the system q = (q1,...,qdN ) there are thus a set of micro(scopic) states which belong p = (p1,...,pdN ) to the ensemble with the probability ρ(P ) dΓ. ρ(P ) is the probability density which is normalized to unity: determine exactly the microscopic state of the system. The phase space is the 2dN-dimensional space (p, q) , dΓ ρ(P )=1, whose every point P = (p, q) corresponds to a possible{ } Z state of the system. A trajectory is such a curve in the phase space along and it gives the local density of points in (q,p)-space at which the point P (t) as a function of time moves. time t. Trajectories are determined by the classical equations of The statistical average, or the ensemble expectation motion value, of a measurable quantity f = f(P ) is dq ∂H i = f = dΓ f(P )ρ(P ). dt ∂p h i i Z dp ∂H i = , We associate every phase space point with the velocity dt ∂q − i field ∂H ∂H where V = (q, ˙ p˙)= , . ∂p − ∂q H = H(q ,...,q ,p ,...,p ,t) 1 dN 1 dN The probability current is then Vρ.