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Engineering Online

CHE 596 622 Introduction to Molecular Simulation

3 Credit Hours

In this course, we will cover the basics of molecular simulation methods, and provide an overview of modeling tools for different problems of interest in science and engineering. The course is geared toward graduate students with an interest in molecular modeling, with or without prior experience in the area. At the end of this course, students should have a general knowledge of the current state-of-the-art in molecular simulation, and be able to design and run simulations of systems of interest.

Prerequisite

ChE 711 and ChE 713 or equivalent. Exceptions considered on a case-by-case basis.

Course Outline

A tentative list of topics to be covered is given below:

  1. Introduction. Why molecular modeling? What systems can be studied, and what are the current challenges? Brief description of different modeling methods.
  2. Basic computing and scripting. Introduction to Unix-based systems. Scripting using bash and Python.
  3. Brief review of probability, Quantum Mechanics and Statistical Mechanics. Basics of probability. Postulates of Quantum Mechanics. Statistical Ensembles.
  4. Ab Initio methods. Overview of quantum chemical methods. Self-consistent field theory. Variational and perturbation methods. Density Functional Theory.
  5. Classical potentials and force fields. Simple fluids: continuous and discontinuous potentials. Complex molecules and force fields. Multibody effects. Electrostatics. Polarization. Reactive force fields. Bulk systems and periodic boundary conditions.
  6. Optimization and molecular mechanics. Gradient methods. Quasi-Newton methods. Finding transition states and transition paths.
  7. Molecular dynamics. Integration of classical equations of motion. Calculating properties. Sampling and stability. Improving performance. Thermostats and barostats. Constraints and restraints. Ab Initio molecular dynamics.
  8. Stochastic dynamics. Brownian motion. The Langevin equation. The Fokker-Planck equation. The fluctuation-dissipation theorem. Dissipative particle dynamics.
  9. Monte Carlo simulation. Markov processes. Detailed balance. Monte Carlo moves for different ensembles. MC versus MD. Monte Carlo simulation of complex molecules.
  10. Calculating interfacial properties. Anisotropic systems. Pressure tensor and surface tension. The test-area method.
  11. Calculating transport properties. Green-Kubo equations. Nonequilibrium molecular dynamics.
  12. Free energy calculations. Widom particle insertion. Biased sampling. Metadynamics. Temperature Accelerated Dynamics. Histogram methods. Perturbation methods.
  13. Coarse-graining. Coarse-grained force field fitting. Iterative Boltzmann inversion. Force matching. Relative entropy minimization. Top-down coarse graining.
  14. Rare events. Bennett-Chandler method. Transition path sampling. Transition interface sampling. Forward flux sampling. The String Method. Milestoning. Calculating reaction rates.
  15. Chemoinformatics and the Materials Genome. Overview of machine learning applications in physical chemistry. Metaheuristics. Computer-aided molecular design. Data driven property prediction.

Requirements

Basic knowledge of classical thermodynamics and differential and integral calculus are required. Prior knowledge of statistical mechanics, quantum mechanics, prior exposure to programming or using mathematical software, and experience with Unix-based operating systems are desirable but not required.

Textbook

None.

Created :11/3/2023