About Course
This Advanced Thermodynamics and Molecular Simulations course aims to impart knowledge of advanced thermodynamics concepts and molecular simulation methods.
Unlike the standard undergraduate chemical engineering thermodynamics, we will follow a rather physicsbased treatment of thermodynamics based on statistical mechanics concepts and molecular theories.
The thermodynamics part to be covered in the first half of the course would be used in the discussion of molecular simulations to be covered in the second half of the course. At the successful completion of the course, students are expected to be able to
1. Apply thermodynamic concepts in the understanding of chemical engineering problems and their research work
2. Identify the molecular simulation approach best suited for a problem, perform simulation, and analyze results.
INTENDED AUDIENCE: Firstyear postgraduate and fourthyear undergraduate students in Chemical Engineering, Chemistry, Materials Science, Polymer Science, Nanotechnology, Mechanical Engineering.
Course Content
Advanced Thermodynamics and Molecular Simulations

Advanced Thermodynamics and Molecular Simulations
02:10 
Lecture 01Introduction to the course
22:40 
Lecture 02: Molecular basis of energy and entropy
30:18 
Lecture 03: Probability and probability distributions
32:10 
Lecture 04: Probability distributions and thermodynamic equilibrium
00:00 
Lecture 05: Energy distribution in molecular systems
00:00 
Lecture 06: First and second law of thermodynamics
00:00 
Lecture 07 Reversible and irreversible processes; third law; legendre transformation…
00:00 
Lecture 08 Thermodynamic functions for multicomponent systems; chemical potential…
00:00 
Lecture 09Extensive and intensive variables; gibbs duhem relation; euler theorem; maxwell relations
00:00 
Lecture 09Extensive and intensive variables; gibbs duhem relation; euler theorem; maxwell relations
00:00 
Lecture 10 Discrete and continuous probabilities; stirling approximation
00:00 
Lecture 11 Binomial distribution approaches gaussian distribution for large n…
00:00 
Lecture 12 Solution of drunkard walk; lagrange multipliers
00:00 
Lecture 13: Energy distribution in molecular system revisited
00:00 
Lecture 14: Canonical ensemble: most probable distribution, partition function
00:00 
Lecture 15: Definition of temperature; third law of thermodynamics
00:00 
Lecture 16: Canonical ensemble: Helmholtz free energy, averages and fluctuations, specific heat
00:00 
Lecture 17: Partition function of a dense gas; grand canonical ensemble: partition function
00:00 
Lecture 18: Computing properties in grand canonical ensemble
00:00 
Lecture 19: Isothermal isobaric ensemble
00:00 
Lecture 20: Summary of thermodynamic ensembles; partition function of an ideal gas
00:00 
Lecture 21: Mixing and phase separation, phase equilibrium of a multiphase multicomponent system
00:00 
Lecture 22: Pure component phase diagram; Solution thermodynamics: Helmholtz free energy density
00:00 
Lecture 23: Characterizing mixing and phase separation using Helmholtz free energy density
00:00 
Lecture 24: Common tangent construction, definition of binodal, spinodal, and critical point
00:00 
Lecture 25: Osmotic pressure and chemical potential
00:00 
Lecture 26: Lattice model of liquid solutions I
00:00 
Lecture 27: Lattice model of liquid solutions II
00:00 
Lecture 28: Lattice model of liquid solutions III
00:00 
Lecture 29: Critical review of Lattice model, theoretical basis of molecular dynamics simulation
00:00 
Lecture 30: Theoretical basis of molecular dynamics simulation
00:00 
Lecture 31: Interaction energy and force field
00:00 
Lecture 32: Liouiville theorem; theoretical basis of Monte Carlo simulation
00:00 
Lecture 33: Introduction to Monte Carlo simulation method
00:00 
Lecture 34: Markov chain algorithm, condition for equilibrium and detailed balance
00:00 
Lecture 35: Metropolis algorithm, periodic boundary condition
00:00 
Lecture 36: Numerical implementation of Monte Carlo simulation: python examples I
00:00 
Lecture 37: Numerical implementation of Monte Carlo simulation: python examples II
00:00 
Lecture 38: Numerical implementation of Monte Carlo simulation: python examples III
00:00 
Lecture 39: Numerical implementation of Monte Carlo simulation: python examples IV
00:00 
Lecture 40: Numerical implementation of Monte Carlo simulation: python examples V
00:00 
Lecture 41: Particle simulations: comparison with quantum chemical and continuum simulations
00:00 
Lecture 42: Pair potentials
00:00 
Lecture 43: Saving CPU time: short range and long range interactions
00:00 
Lecture 44: Bonded and nonbonded interactions, force fields
00:00 
Lecture 45: Practical aspects of molecular simulations
00:00 
lecture 46: Numerical implementation of MD; thermostat and barostat
00:00 
Lecture 47: MD simulations – efficiency and parallelization, sampling and averaging
00:00 
Lecture 48: MD simulations – analysis of simulation trajectories (continued), case studies I
00:00 
Lecture 49: MD simulations – case studies II
00:00 
Lecture 50: MD simulations – case studies III
00:00 
Lecture 51: Free energies and phase behavior; extension of canonical ensemble monte carlo…
00:00 
Lecture 52: Extension of canonical ensemble monte carlo to other ensembles (continued)
00:00 
Lecture 53: Monte carlo in gibbs ensemble and semigrand canonical ensemble…
00:00 
Lecture 54: Thermodynamic integration (continued); widom’s particle insertion…
00:00 
Lecture 55: Multiple histogram method; umbrella sampling; thermodynamic cycle…
00:00 
Lecture 56:Tackling time scale issues (continued); nonequilibrium molecular dynamics…
00:00 
Lecture 57: Multiparticle collision dynamics; lattice boltzmann method; coarsegraining
00:00 
Lecture 58: Case studies
00:00 
Lecture 59: Simulations of chemical reactions using kinetic monte carlo simulations
00:00