PCH233: MOLECULAR MODELLING AND SIMULATION 


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Course Objective: To learn to mimic the real system and phenomena in virtual world using molecular level information and computational resources and to develop and design the novel performance chemicals and materials.
Introduction: Need of molecular modelling and simulation, Postulates of statistical mechanics, Ergodic hypothesis.
Statistical Ensembles and Partition Functions: System and particle partition function and relation to thermodynamics, Microcanonical ensemble, Canonical ensemble, Isothermalisobaric ensemble, Grandcanonical ensemble, Gibbs ensemble, Thermodynamic equivalence of ensembles, Ensemble average and time average equivalence.
Empirical Force Field Models: General features of molecular mechanic's force fields, Bond stretching, Bond bending, Dihedrals and torsion, Nonbonded interactions, Hard and soft interactions, Electrostatic interactions, Combination/mixing rules, Standard force fields.
Simulation of Ensembles Using Monte Carlo and Molecular Dynamics Methods: Introduction to MonteCarlo simulation, Importance sampling and the metropolis algorithm, Implementation of metropolis Monte Carlo algorithm, Simulation cell and periodic boundary conditions, Moves and acceptance criteria, Simulations in different ensembles, Multicanonical Monte Carlo and the transition matrix, Configurational bias Monte Carlo, Calculation of thermodynamic properties, Introduction to molecular dynamics simulation, Initialization and force calculation, Algorithms to integrate the equations of motion, Thermostats and barostats, Autocorrelation functions, Free energy calculations, Molecular dynamics packages, Design and development of novel performance chemicals and materials for applications in polymers, catalysts, pharmaceuticals and solvents.
Course Learning Outcomes (CLO):
1. Understanding of the principles of molecular mechanics applicable in molecular modeling
2. Understanding of various simulation techniques and their use for model solutions
3. Ability to use molecular modeling software
4. Acquaintance with modern analysis techniques and visualization packages
5. Capability of developing/improving simulation experiment
Recommended Books:
1. McQuarrie, D.A., Statistical Mechanics, University Science Books (2000).
2. Frenkel, D.,and Smit, B., Understanding Molecular Simulation: From Algorithms to
3. Applications, Academic Press, (2002).
4. Leach, A.R., Molecular Modeling: Principles and Applications, Pearson Education Ltd. (2001).