CHEM 567
Download as PDF
Statistical Mechanics
Chemistry and Biochemistry
College of Computational, Mathematical, & Physical Sciences
Course Description
Introduction to classical and quantum statistical mechanics, including Boltzmann, Fermi-Dirac, and Bose-Einstein statistics. Applications of statistical thermodynamics to gases, liquids, and solids.
When Taught
Contact Department
Min
3
Fixed
3
Fixed
3
Fixed
0
Recommended
Chem 565
Title
Outcome 13
Learning Outcome
Obtain the radial distribution from the probability function for a system, from Monte Carlo calculations or molecular dynamics trajectories and evaluate system properties from the radial distribution function.
Title
Outcome 2
Learning Outcome
Use combinatorics to define probabilities in simple systems.
Title
Outcome 14
Learning Outcome
Use Monte Carlo and transfer matrix methods to study a system of interacting spins as a model of phase transitions.
Title
Outcome 11
Learning Outcome
Use the Metropolis Monte Carlo method to calculate statistical averages for a system. Know how to choose an efficient sampling method.
Title
Outcome 8
Learning Outcome
Explain the meaning, significance and range of applicability of these fundamentals: ergodic hypothesis, equipartition theorem, and Bose-Einstein condensation.
Title
Outcome 10
Learning Outcome
Apply statistical mechanical principles to simple crystals, blackbody radiation and imperfect gases.
Title
Outcome 9
Learning Outcome
Use statistical mechanical principles to describe chemical equilibrium constants.
Title
Outcome 6
Learning Outcome
Use proper statistics for classical particles and for quantum particles (bosons or fermions).
Title
Outcome 1
Learning Outcome
Use probability density functions to describe a system, and recognize simple probability functions and their properties including Gaussian (normal), binomial (or multinomial), and Poisson distributions.
Title
Outcome 5
Learning Outcome
Calculate the size of fluctuations of system properties and relate them to the number of particles in a system.
Title
Outcome 3
Learning Outcome
Describe how to choose a Gibbsian ensemble for calculation of system properties. Be able to recognize when the following ensembles are appropriate: microcanonical (N,V,E), canonical (N,V,T), grand canonical (V,T,m), isobaric-isothermal (N,T,P). Know how to construct the partition function in each case.
Title
Outcome 4
Learning Outcome
Calculate average system quantities and thermodynamic variables using the appropriate partition function.
Title
Outcome 12
Learning Outcome
Show how to use molecular dynamics to calculate statistical averages for a molecular system. Know when molecular dynamics is more appropriate than Monte Carlo methods. Know the advantages and disadvantages of common trajectory propagation algorithms.
Title
Outcome 7
Learning Outcome
Calculate canonical partition functions for translation, rotation, and harmonic vibration. Use them for applications and know when they don't apply.
