What you will learn
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Chemical Engineering
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.
First and second law of thermodynamics
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36:38
Reversible and irreversible processes; third law of thermodynamics; legendre transformation; thermodynamic functions for one component system
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35:30
Thermodynamic functions for multi-component systems; chemical potential; why do we minimize thermodynamic functions?
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37:39
Extensive and intensive variables; gibbs duhem relation; euler theorem; maxwell relations
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37:38
Discrete and continuous probabilities; stirling approximation
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37:46
Binomial distribution approaches Gaussian distribution for large n; definition of drunkard walk
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36:05
Solution of drunkard walk; Lagrange multipliers
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31:32
Energy distribution in molecular system revisited; introduction to thermodynamic ensembles
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30:13
Canonical ensemble: most probable distribution, partition function
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39:42
Definition of temperature; third law of thermodynamics
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24:41
Canonical ensemble: Helmholtz free energy, averages and fluctuations, specific heat, deriving ideal gas law
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36:20
Partition function of a dense gas; grand canonical ensemble: partition function, most probable distribution
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34:35
Computing properties in grand canonical ensemble
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30:58
Isothermal isobaric ensemble
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34:41
Summary of thermodynamic ensembles; partition function of an ideal gas
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36:34
Mixing and phase separation, phase equilibrium of a multiphase multicomponent system, Gibbs phase rule
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42:32
Pure component phase diagram; solution thermodynamics: Helmholtz free energy density
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34:45
Characterizing mixing and phase separation using Helmholtz free energy density
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29:47
Common tangent construction, definition of binodal, spinodal, and critical point
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33:23
Osmotic pressure and chemical potential
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40:44
Lattice model of liquid solutions I
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36:09
Lattice model of liquid solutions II
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38:39
Lattice model of liquid solutions III
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24:39
Critical review of Lattice model, theoretical basis of molecular dynamics simulation
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34:29
Theoretical basis of molecular dynamics simulation.
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33:28
Interaction energy and force field
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28:39
Liouiville theorem; theoretical basis of Monte Carlo simulation
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36:06
Introduction to Monte Carlo simulation method
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33:24
Markov chain algorithm, condition for equilibrium and detailed balance
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38:03
Metropolis algorithm, periodic boundary condition
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45:45
Numerical implementation of Monte Carlo simulation: python examples I
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41:45
Numerical implementation of Monte Carlo simulation: python examples II
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29:47
Numerical implementation of Monte Carlo simulation: python examples III
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38:25
Numerical implementation of Monte Carlo simulation: python examples IV
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36:11
Numerical implementation of Monte Carlo simulation: python examples V
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44:34
Particle simulations: comparison with quantum chemical and continuum simulations; bridging length and time scales
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25:33
Pair potentials
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32:36
Saving CPU time: short range and long range interactions
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36:01
Bonded and non-bonded interactions, force fields
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28:51
Practical aspects of molecular simulations
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32:10
Numerical implementation of MD; thermostat and barostat
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30:24
MD simulations - efficiency and parallelization, sampling and averaging, analysis of simulation trajectories
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43:52
MD simulations - analysis of simulation trajectories (continued), case studies I
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40:57
MD simulations - case studies II
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29:53
MD simulations - case studies III
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32:06
Free energies and phase behavior; extension of canonical ensemble Monte Carlo to other ensembles
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31:02
Extension of canonical ensemble Monte Carlo to other ensembles (continued)
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33:55
Monte Carlo in Gibbs ensemble and semi-grand canonical ensemble, thermodynamic integration
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24:37
Thermodynamic integration (continued); Widom's particle insertion; overlapping distribution method
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30:17
Multiple histogram method; umbrella sampling; thermodynamic cycle; potential of mean force; pulling simulations; metadynamics; tackling time scale issues
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24:24
Tackling time scale issues (continued); nonequilibrium molecular dynamics; mesoscale simulations: Langevin dynamics and Brownian dynamics, kinetic Monte Carlo simulations; dissipative particl
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34:57
Multiparticle collision dynamics; lattice Boltzmann method; coarse-graining
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30:27
Case studies.
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31:49
Simulations of chemical reactions using Kinetic Monte Carlo simulations
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34:14
Reactive force fields; Ab initio molecular dynamics and other advanced methods; molecular simulations in chemical engineering; concluding remarks
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28:26
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Meet Your Instructor
About Instructor
VTU is one of the largest Technological Universities in India with 24 years of Tradition of excellence in Engineering & Technical Education, Research and Innovations. It came into existence in the year 1998 to cater the needs of Indian industries for trained technical manpower with practical experience and sound theoretical knowledge.
Free
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Course Duration33 h 49 m 2 s
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Course LevelIntermediate
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Student Enrolled0
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LanguageEnglish
This Course Includes
- 33 h 49 m 2 s Video Lectures
- 0 Quizzes
- 0 Assignments
- 0 Downloadable Resources
- Full Lifetime Access
- Certificate of Completion