This 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 physics-based treatment of thermodynamics based on statistical mechanics concepts and molecular theories. The thermodynamics part to be covered in 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 :First year postgraduate and fourth year undergraduate students in Chemical Engineering, Chemistry, Materials Science, Polymer Science, Nanotechnology, Mechanical EngineeringPREREQUISITES : Basic UG course in thermodynamics or statistical mechanicsINDUSTRIES SUPPORT :Pharmaceutical, FMCG, Chemical, and Oil companies.
Advanced Thermodynamics and Molecular Simulations
Indian Institute of Technology Roorkee and NPTEL via Swayam
Overview
Syllabus
Week 1:Introduction and scope of the course. Probability and distributions. Boltzmann approximation and concept of thermodynamic Equilibrium. Molecular origin of entropy.Week 2:Laws of thermodynamics, thermodynamic functions, Legendre transformation, Maxwell relations.Week 3:Averages and fluctuations, Method of Lagrange multipliers. Introduction to thermodynamic ensembles, partition function.Week 4:Derivation of thermodynamic properties in different ensembles, definition of temperature.
Week 5:Phase equilibrium, Gibbs phase rule, mixing and phase separation, chemical potential, osmotic pressure.Week 6:Lattice model of solutions, phase space and Hamiltonian. Theoretical basis of molecular simulations.Week 7:Monte Carlo (MC) Simulations: Setting up a simulation, types of boundary conditions, detailed balance. Week 8:Monte Carlo (MC) Simulations: Numerical implementation, analysis and interpretation of results, case studies.
Week 9:Practical Introduction to Particle Simulations, Molecular Dynamics (MD) Simulations: Numerical integration of equations of motion, force-fields.Week 10:Molecular Dynamics (MD) Simulations: Temperature and Pressure Control, Analysis and interpretation of results, efficiency and parallelization, case studies.Week 11:Methods to study phase behavior and coexistence; Methods for Free Energy Calculations: Thermodynamic integration, Widom’s particle insertion method, umbrella sampling and other advanced strategies.Week 12:Non-equilibrium and Mesoscale Simulations: Langevin equations, Brownian dynamics (BD), Kinetic Monte Carlo (kMC) simulations, and other methods; simulations of chemical reactions and quantum chemistry simulations.
Week 5:Phase equilibrium, Gibbs phase rule, mixing and phase separation, chemical potential, osmotic pressure.Week 6:Lattice model of solutions, phase space and Hamiltonian. Theoretical basis of molecular simulations.Week 7:Monte Carlo (MC) Simulations: Setting up a simulation, types of boundary conditions, detailed balance. Week 8:Monte Carlo (MC) Simulations: Numerical implementation, analysis and interpretation of results, case studies.
Week 9:Practical Introduction to Particle Simulations, Molecular Dynamics (MD) Simulations: Numerical integration of equations of motion, force-fields.Week 10:Molecular Dynamics (MD) Simulations: Temperature and Pressure Control, Analysis and interpretation of results, efficiency and parallelization, case studies.Week 11:Methods to study phase behavior and coexistence; Methods for Free Energy Calculations: Thermodynamic integration, Widom’s particle insertion method, umbrella sampling and other advanced strategies.Week 12:Non-equilibrium and Mesoscale Simulations: Langevin equations, Brownian dynamics (BD), Kinetic Monte Carlo (kMC) simulations, and other methods; simulations of chemical reactions and quantum chemistry simulations.
Taught by
Prof. Prateek Kumar Jha
