1 / 27

Chapter 6 Basic Methods & Results of Statistical Mechanics

Chapter 6 Basic Methods & Results of Statistical Mechanics. Historical Introduction. Maxwell. Statistical Mechanics developed by Maxwell, Boltzman, Clausius, Gibbs. Question: If we have individual molecules – how can there be a pressure, enthalpy, etc?. 2.

stan
Download Presentation

Chapter 6 Basic Methods & Results of Statistical Mechanics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 6Basic Methods & Results of Statistical Mechanics

  2. Historical Introduction Maxwell Statistical Mechanics developed by Maxwell, Boltzman, Clausius, Gibbs. Question: If we have individual molecules – how can there be a pressure, enthalpy, etc? 2

  3. Key Concept In Statistical Mechanics Idea:Macroscopic properties are a thermal average of microscopic properties. • Replace the system with a set of systems "identical" to the first and average over all of the systems. We call the set of systems “The Statistical Ensemble”. • Identical Systemsmeans that they are all in the same thermodynamic state • To do any calculations we have to first Choose an Ensemble! 3

  4. Common Statistical Ensembles • Micro Canonical Ensemble: Isolated Systems. • Canonical Ensemble: Systems with a fixed number of molecules in equilibrium with a heat bath. • Grand Canonical Ensemble: Systems in equilibrium with a source a heat bath which is also a source of molecules. Their chemical potential is fixed. 4

  5. All Thermodynamic Properties Can Be Calculated With Any Ensemble We choose the one most convenient. For gases: PVT properties – canonical ensemble Vapor-liquid equilibrium – grand canonical ensemble. 5

  6. Properties Of The Canonical and the Grand Canonical Ensemble 6

  7. Properties of the Canonical Ensemble: (6.11) 7

  8. The Grand Canonical Ensemble: (6.12) with: (6.13) 8

  9. Partition Functions (6.15) (6.16) 9

  10. Partition Functions If you know the volume, temperature, and the energy levels of the system you can calculate the partition function. If you know T and the partition function you can calculate all other thermodynamic properties. Thus, stat mech provides a link between quantum and thermo. If you know the energy levels you can calculate partition functions and then calculate thermodynamic properties. 10

  11. Partition functions easily calculate from the properties of the molecules in the system (i.e. energy levels, atomic masses etc). Convenient thermodynamic variables. If you know the properties of all of the molecules, you can calculate the partition functions. Can then calculate any thermodynamic property of the system. 11

  12. Thermal Averages with Partition Functions (6.40) (6.59) (6.60) 12 (6.61)

  13. (6.62) (6.65) (6.63) (6.64) 13

  14. Canonical Ensemble Partition Function Z Starting from the fundamental postulate ofequal a priori probabilities, the following are obtained: the results of classical thermodynamics, plus their statistical underpinnings; the means of calculating the thermodynamic parameters (U, H, F, G, S ) from a single statistical parameter, the partition functionZ (or Q), which may be obtained from the energy-level scheme for a quantum system. The partition function for a quantum system in contact with a heat bath is Z = i exp(– εi/kT), where εi is the energy of the i’th state. 14

  15. The partition function for a quantum system in contact with a heat bath is Z = i exp(– εi/kT), where εi is the energy of the i’th state. The connection to the macroscopic thermodynamic function S is through the microscopic parameter Ω (or ω), which is known as thermodynamic degeneracy or statistical weight, and gives the number of microstates in a given macrostate. The connection between them, known as Boltzmann’s principle, is S = k lnω. (S = k lnΩis carved on Boltzmann’s tombstone). 15

  16. Relation of Z to Macroscopic Parameters Summary of results to be obtained in this section <U> = – ∂(lnZ)/∂β = – (1/Z)(∂Z/∂β), CV = <(ΔU)2>/kT2, where β = 1/kT, with k = Boltzmann’s constant. S = kβ<U> + k lnZ , where <U> = U for a very large system. F = U – TS = – kT lnZ, From dF = S dT – PdV, we obtain S = – (∂F/∂T)Vand P = – (∂F/∂V)T . Also, G = F + PV = PV – kT lnZ. H = U + PV = PV – ∂(lnZ)/∂β. 16

  17. Systems of N Particles of the Same Species Z = zNfor distinguishable particles (e.g. solids); Z = zN/Nfor indistinguishable particles (e.g.fluids). <u> = – ∂(lnz)/∂β = – (1/z)(∂z/∂β), U = N<u>. cV = <(Δu)2>/kT2, CV =NcV, CP =NcP. Distinguishable particles: F = Nf = – kT ln zN = – NkT lnz. Since F = U – TS, so that S = (U – F)/T or S = – (∂F/∂T)V. Indistinguishable particles: F = – kT ln(zN/N) = – kT [ln(zN)– ln N] = – NkT [ln(z/N)– 1], Since for very large N, Stirling’s theorem gives ln N! = N lnN – N. Also, S = – (∂F/∂T)Vand P = (∂F/∂V)Tas before. 17

  18. Mean Energies and Heat Capacities Equations obtained from Z = rexp (– Er), where  = 1/kT. U = rprEr/rpr= – (ln Z)/ = – (1/Z) Z/ . U2 = rprEr2/rpr = (1/Z) 2Z/2. Un = rprErn/rpr= (–1)n(1/Z) nZ/n. (ΔU)2 = U2 – (U)2 = 2lnZ/2 or –  U/ . CV =  U/T =  U/ . d/dT = – k2.  U/, or CV = k2 (ΔU)2 = (ΔU)2/kT2; i.e. (ΔU)2 = kT2CV. Notes Since (ΔU)2 ≥ 0, (i) CV≥ 0, (ii)  U/T ≥ 0. 18

  19. Entropy and Probability Consider an ensemble of n replicas of a system. If the probability of finding a member in the state r is pr, the number of systems that would be found in the r’th state is nr= n pr, if n is large. The statistical weight of the ensemble Ωn(n1 systems are in state 1, etc.), is Ωn= n/(n1 n2…nr..), so thatSn= k ln n – k r ln nr.‍ From Stirling’s theorem, ln n ≈ n ln n – n, r ln nr ≈ rnr ln nr – n. Thus Sn= k {n ln n – rnr ln nr} =‍ k {n ln n – rnr ln n – r nr ln pr}, so thatSn= – k r nr ln pr= – kn rpr ln pr. For a single system, S = Sn/n ; i.e. S = – k r pr ln pr . 19

  20. Ensembles 1 A microcanonical ensembleis a large number of identical isolated systems. The thermodynamic degeneracy may be written asω(U, V, N). From the fundamental postulate, the probability of finding the system in the state r ispr = 1/ω. Thus, S = – k rpr ln pr = k r(1/ω) ln ω = (k/ω) ln ωr1 = k ln ω. Statistical parameter: ω(U, V, N). Thermodynamic parameter: S(U, V, N) [T dS = dU – PdV + μdN]. Connection: S = k ln ω. Equilibrium condition: S Smax. 20

  21. Ensembles 2 Acanonical ensembleconsists of a large number of identically prepared systems, which are in thermal contact with a heat reservoir at temperature T. The probability pr of finding the system in the state r is given by the Boltzmann distribution: pr = exp(– Er)/Z, where Z = rexp(–Er), and  = 1/kT. Now S = – k rpr ln pr =– k r [exp(–Er)/Z] ln[exp(–Er)/Z] = – (k/Z) rexp(–Er) {ln exp(–Er) – ln Z} = (k/Z) rErexp(–Er) + (k lnZ)/Z . rexp(–Er), so that S = k U + k lnZ = k lnZ + kU. Thus, S(T, V, N) = k lnZ + U/T and F = U – TS = – kT lnZ. 21

  22. Ensembles 3 S(T, V, N) = k lnZ + U/T , F = U – TS = – kT lnZ. Statistical parameter: Z(T, V, N). Thermodynamic parameter: F(T, V, N). Connection: F = – kT ln Z. Equilibrium condition: F Fmin. A grand canonical ensembleis a large number of identical systems, which interact diffusively with a particle reservoir. Each system is described by agrand partition function, G(T, V, μ) = N{r(μN – EN,r)}, where N refers to the number of particles and r to the set of states associated with a given value of N. 22

  23. Statistical Ensembles Classical phase space is 6N variables (pi, qi) with a Hamiltonian function H(q,p,t). We may know a few constants of motion such as energy, number of particles, volume, ... The most fundamental way to understand the foundation of statistical mechanics is by using quantum mechanics: In a finite system, there are a countable number of states with various properties, e.g. energy Ei. For each energy interval we can define the density of states. g(E)dE = exp(S(E)/kB) dE, where S(E) is the entropy. If all we know is the energy, we have to assume that each state in the interval is equally likely.(Maybe we know the p or another property) 23

  24. Environment Perhaps the system is isolated. No contact with outside world. This is appropriate to describe a cluster in vacuum. Or we have a heat bath: replace surrounding system with heat bath. All the heat bath does is occasionally shuffle the system by exchanging energy, particles, momentum,….. The only distribution consistent with a heat bath is a canonical distribution: See online notes/PDF derivation 24

  25. Statistical ensembles (E, V, N) microcanonical, constant volume (T, V, N) canonical, constant volume (T, P N) canonical, constant pressure (T, V , μ) grand canonical (variable particle number) Which is best? It depends on: the question you are asking the simulation method: MC or MD (MC better for phase transitions) your code. Lots of work in recent years on various ensembles (later). 25

  26. Maxwell-Boltzmann Distribution Z=partition function. Defined so that probability is normalized. Quantum expression Also Z= exp(-β F), F=free energy (more convenient since F is extensive) Classically: H(q,p) = V(q)+ Σi p2i /2mi Then the momentum integrals can be performed. One has simply an uncorrelated Gaussian (Maxwell) distribution of momentum. 26

  27. Microcanonical ensemble E, V and N fixed S = kB lnW(E,V,N) Canonical ensemble T, V and N fixed F = -kBT lnZ(T,V,N) Grand canonical ensemble T, V and m fixed F = -kBT ln (T,V,m)

More Related