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Chapter 6 Basic Methods & Results of Statistical Mechanics Historical Introduction • Statistical Mechanics developed by Maxwell, Boltzman, Clausius, Gibbs. • Question: If we have individual molecules – how can there be a pressure, enthalpy, etc? 2 Maxwell 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 Systems means that they are all in the same thermodynamic state • To do any calculations we have to first Choose an Ensemble! 3 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 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 Properties Of The Canonical and the Grand Canonical Ensemble Gibbs showed that the ensemble average was equivalent to a state average F Fn p n n (6.10) Pn=the probability that the system is in a configuration (state) n. 6 Properties of the Canonical Ensemble: U n g ne pn N Qcanon (6.11) 7 The Grand Canonical Ensemble: E n g ne pn Q grand (6.12) with: E n Un N n (6.13) 8 Partition Functions N canon Q =canonical partition function Qgrand= grand canonical partition function N Qcanon g ne U n n (6.15) Qgrand g ne n (6.16) 9 E n 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 • 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 Thermal Averages with Partition Functions pn S k B pn Ln gn n (6.40) N A k BTLn(Qcanon ) (6.59) N ( LnQcanon ) U (6.60) N LnQcanon A N S=- =k T +k LnQ B B canon T T V,N V,N 12 (6.61) N LnQcanon A P =k BT V T,N V T,N (6.62) LnQgrand PV Ν= =k BT μ μ T,V N LnQ A canon k BT N (6.63) T,V N T,V T,V (6.65) LnQgrand PV S +k B Ln(Qgrand ) k BT dT T V, V, (6.64) 13 Canonical Ensemble Partition Function Z Starting from the fundamental postulate of equal a priori probabilities, the following are obtained: i. the results of classical thermodynamics, plus their statistical underpinnings; ii. the means of calculating the thermodynamic parameters (U, H, F, G, S ) from a single statistical parameter, the partition function Z (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 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 lnΩ is carved on Boltzmann’s tombstone). 15 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)V and P = – (∂F/∂V)T . Also, G = F + PV = PV – kT lnZ. H = U + PV = PV – ∂(lnZ)/∂β. 16 Systems of N Particles of the Same Species • Z = zN for 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)V and P = (∂F/∂V)T as before. 17 Mean Energies and Heat Capacities • Equations obtained from Z = r exp (– 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 = – k2. U/, or CV = k2 (ΔU)2 = (ΔU)2/kT2; i.e. (ΔU)2 = kT2CV . Notes Since (ΔU)2 ≥ 0, (i) CV ≥ 0, (ii) U/T ≥ 0. 18 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 that Sn = k ln n – k r ln nr. • From Stirling’s theorem, ln n ≈ n ln n – n, r ln nr ≈ r nr ln nr – n. Thus Sn = k {n ln n – r nr ln nr} = k {n ln n – r nr ln n – r nr ln pr}, so that Sn = – k r nr ln pr = – kn r pr ln pr . 19 For a single system, S = Sn/n ; i.e. S = – k r pr ln pr . Ensembles 1 A microcanonical ensemble is a large number of identical isolated systems. The thermodynamic degeneracy may be written as ω(U, V, N). From the fundamental postulate, the probability of finding the system in the state r is pr = 1/ω. Thus, S = – k r pr 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 Ensembles 2 A canonical ensemble consists 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 = r exp(–Er), and = 1/kT. Now S = – k r pr ln pr = – k r [exp(–Er)/Z] ln[exp(–Er)/Z] = – (k/Z) r exp(–Er) {ln exp(–Er) – ln Z} = (k/Z) rEr exp(–Er) + (k lnZ)/Z . rexp(–Er), so that S = k U + k lnZ = k lnZ + kU. Thus, S(T, V, N) = k lnZ + U/T and F = U – TS = – kT lnZ. 21 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 ensemble is a large number of identical systems, which interact diffusively with a particle reservoir. Each system is described by a grand 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 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 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,The particles, momentum,….. only distribution consistent with a heat bath is a canonical distribution: Prob(q, p) dqdp e H ( q, p ) / Z See online notes/PDF derivation 24 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 Maxwell-Boltzmann Distribution Prob(q, p) dqdp e H ( q , p ) / N !Z • Z=partition function. Defined so that probability is Z exp( E i ) 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 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 fixed F = kBT ln (T,V,)