Module Notes
Faculty Member (Members):
Postgraduate, Spring Semester
Module Type: Specialization Courses
Teaching Language: English/Greek
Course Code: GCHM_C731
ECTS Credits: 8
Module Availability on Erasmus Students: No
Module Details

The scope of the course is:

  1. to teach graduate students the key principles of equilibrium statistical mechanics (theory of statistical ensembles), and
  2. to introduce them to the techniques of the computer simulation of matter (molecular dynamics, Brownian dynamics, Monte Carlo, dissipative particle dynamics)

At the end of the course, the student will be able to:

  1. design molecular simulation algorithms (e.g., Molecular Dyanimcs and Monte Carlo) for simple fluids
  2. given a classical trajectory from such a simulation, to compute values for the thermodynamic and structural properties of the system
  3. use fundamental theorems of classical statistical mechanics to compute time autocrrelation and cross-correlation functions and extract from them important transport properties (e.g., viscosity and diffusivity)

Good knowledge of classical mechanics and of statistical thermodynamics (one under-graduate semester course is enough)

  1. Introduction to Statistical Mechanics. Statistical Mechanics and axiomatic foundation of Thernodynamics.
  2. Equilibrium Statistical Ensembles.
  3. Dynamical trajectory in phase space. Probability density and the time-reversible Liouville equation. Ensemble averages and time averages. Ergodic hypothesis.
  4. Microscopic and macroscopic stress tensors, the virial theorem
  5. From Newton's to Hamilton's equations of motion. Derivation of the microscopic dynamical equations in the NVE ensemble
  6. Derivation of the microscopic dynamical equations in the NVT ensemble
  7. Derivation of the microscopic dynamical equations in the NPT ensemble
  8. Statistical ensembles for simulating systems under uniaxial extension or under flow. Derivation of the relevant microscopic dynamical equations
  9. The chemical potential as an ensemble average. Widom test particle insertion. Simulations of phase equilibria
  10. Statistical mechanics of sorption. The BET and the Langmuir isotherms
  11. Fluctuations and their role in determining thermodynamic properties
  12. Statistical mechanics of thermoealsticity. Generalized ensembles for simulating the elastic properties of materials
  13. Introduction to molecualr simulations. Molecular models, potential energy function, periodic boundary conditions, minimum image convention 
  14. Molecular dynamics. Algorithms for integrating the microscopic equations of motion in the absence and presence of holonomic constraints
  15. The trajectory file and its post-processing. Radial pair correlation functions, time auto-correlation and cross-correlation functions
  16. From the trajectotry file to the macrospically observed physical properties of a system. How to get the XRD pattern. Connection with neutron spin echo experiments
  17. Monte Carlo sampling. Monte Carlo simulations in the canonical, isothermal-isobaric, grand canonical and semigrand canonical ensembles
  18. Applications. Getting experienced with the molecular simulation of physical systems and predicting their thermodynamic, dynamic, structural, conformational, transport, interfacial, and other properties
  1. D.N. Theodorou and V.G. Mavrantzas, Multiscale modelling of Polymers (Oxford Univ. Press, Oxford, 2019).
  2. D.A. McQuarrie, Statistical Mechanics (Harper and Row: New York, 1976).
  3. M.P. Allen and D.J. Tildesley, Computer Simulation of liquids (Oxford Science Publications, Oxford, 1997).

The instructor prefers teaching by writing a lot on the blackboard but also by using power-point transparencies showing how one can set up a simulation and how he/she can visualize its progress

Weekly or bi-weekly homework sets, semester project