Statistical Physics (2024)
Statistical mechanics enables us to model the behavior of macroscopic objects, which are made up of large numbers of constituents for which we only have incomplete descriptions, using probability theory and a microscopic theory of the constituents. It bridges the disconnect between mechanics which require complete knowledge of initial conditions, and the real world where such information is not available. These ideas therefore find widespread application.
Target Audience
This is course SP-1 in the TIFR graduate school.
This page will be updated regularly with course-related information. Please check frequently.
Administrivia
Time: Tu, Th at 930 hrs
Venue: AG 69
First lecture: 6 Feb
Credit policy: Tentatively Midterm (35%) + Endterm (35%) + Assignment (30%)
Instructor: Basudeb Dasgupta
Tutors: Sandeep Jangid and Soumya Pal
Course Webpage: Moodle [TIFR only]
Lecture Notes: Dropbox Link [Public]
Course Contents
1. Preliminaries: Motivation and review of thermodynamics
2. Probability and statistics: Counting, distributions, large numbers
3. Kinetic theory and approach to equilibrium; Brownian walks etc.
4. Classical statistical mechanics: Formalism and simple systems
5. Classical statistical mechanics: Interactions, approximations, phase transitions
6. Quantum statistical mechanics: Formalism, ideal Bose/Fermi gases, phase transitions
7. What else is there? and Review of the course
References
1. Statistical Mechanics, Huang (I personally find it most readable)
2. Statistical Physics (Berkeley Physics Course Vol.5), Reif
3. Statistical Mechanics of Particles, Kardar (Main text, but very terse. Videos are awesome.)
4. Statistical Mechanics Part-I (Course of Theoretical Physics Vol.5), Landau and Lifshitz
5. Lecture notes by David Tong, Cambridge Univ. (Kinetic Theory, Statistical Mechanics)
Problem Sets
1. PS1 (thermodynamics and probability)
2. PS2 (kinetic theory)
3. PS3 (classical stat. mech + interactions)
4. PS4 (quantum stat. mech)
Exams
1. Droptest on 18 Feb
2. Midterm (4 April, AG69)
3. Endterm (30 May, 9am-1pm, AG69)
Lecture Summaries (tentative)
Lecture 1 (Feb 6)
Why Statistical Mechanics?
Introduction to Thermodynamics
Basic notions / terms used in Thermodynamics
Lecture 2 (Feb 8)
Zeroth Law
First Law
Lecture 3 (Feb 13)
Second Law
Potentials
Third Law
Lecture 4 (Feb 15)
Probability
Bayesian and Frequentist
Generating functions
Lecture 5 (20 Feb)
Moments, Cumulants, and MGF, CGF,
Examples: Gaussian, Binomial
Many Random Variables
Lecture 6 (Feb 22)
Central Limit Theorem for iids; and extensions
Summing large exponentials, Saddle point, Stirling's approximation
Lecture 7 (Feb 27)
Information and Entropy
Lecture 8 (Feb 29)
Phase space and Liouville eq.
Equilibrium and its features
Lecture 9 (Mar 5)
Liouville to BBGKY for dilute gas
N particle density
Time-scales
Lecture 10 (Mar 7)
BBGKY to Boltzmann
Coarse-graining and molecular chaos
Boltzmann eq. and local equilibrium
H Theorem
Lecture 11 (Mar 12)
Boltzmann eq. to Hydrodynamics via moments
Hydrodynamics at zeroth order: Waves
Lecture 12 (Mar 14)
First order: Diffusion
Approach to global equilibrium and relevant timescales
Lecture 13 (Mar 19)
Equilibrium
Time average vs Phase average
Main goal of Stat Mech : Assign probability to microstates
Lecture 14 (Mar 21)
Density/Number of states
Lecture 15 (Mar 26)
Microcanonical ensemble
Microcanonical Example: Ideal gas
Derivations of 0th, 1st Law
Lecture x (Mar 28)
Lecture cancelled
Midterm Week (1-5 April)
Midterm on 4th April
Lecture x (April 9)
No class [Gudi Padwa]
Lecture x (April 11)
No class [Eid ul Fitr]
Lecture 16, 17, 18, 19, 20 (April 16, 18, 23, 30, May 2)
2nd Law of Thermodynamics
Canonical and Examples
Grand Canonical and Examples
Classical Stat Mech. of Interacting Systems
Mayer cluster expansion technique
Mean Field Theory of Condensation
1st order Phase Transition
Gibbs Canonical Ensemble and Multiple Saddle Points
Meaning of Maxwell Construction
Curie-Weiss Mean Field Theory
2nd Order Phase Transition
Important Results on Phase Transitions (without proof)
Lectures 21, 22 (April 22 at 4pm, 25 at 10am)
Stochastic Processes [Guest Lectures by Prof. Shamik Gupta]
Lectures 23 onwards (May 7, 9, 14, 16, 21, 23)
Discreteness of energy
C_V of diatomic gas vs T
C_V of a crystal and Debye's T^3 Law
Black body radiation
Symmetrization and Antisymmetrization
Density matrix in energy and position bases
Virial expansion and "effective potential" for ideal quantum gas
Ideal quantum gas at high T (mu vs T, virial expansion again)
Ideal Fermi gas (Fermi energy, mu, C_V, degeneracy pressure)
Ideal Bose gas (BEC, pressure, C_V, mu)