Teaching

Odd Semester (July-Nov)

CH2010: Chemical Engineering Thermodynamics

I teach the second half of this course that focuses on equilibrium thermodynamics. Topics covered include: Thermodynamics of mixtures, Partial Molar Properties, Gibbs Duhem Equation, Property Changes of Mixing, Multicomponent Phase Equilibria, Chemical Potential, Fugacity, Chemical Equilibria and Fugacity, Fugacity in the Liquid Phase, Activity Coefficient, Excess Properties, Models for Excess Gibbs Energy, Vapor Liquid Equilibrium, Phase Diagrams, Non-ideality, Azeotropes, Chemical Reaction Equilibria

Books: Engineering and Chemical Thermodynamics, Milo D. Koretsky, 2nd Ed. Wiley. 

Even Semester (Jan-May)

CH5003: Machine Learning for Molecular Simulations (ML4MS)

This is a curated elective course that I teach in the even semester, which brings together Statistical Mechanics, Electronic Structure Theory, and Deep Learning within a single course. Topics covered include: Math and Probability recap, Ensembles, Partition functions, Stat Mech of Simple Gases, Classical Stat Mech, Hamiltonian, Phase Space, Classical Partition functions, Equipartition, Ergodic hypothesis, Molecular Dynamics Simulations, MD simulations in practice, Potential Energy Surface and the many-body problem, Density Functional Theory, Hohenberg and Kohn Theorem, Kohn-Sham equations, DFT in practice, Born-Oppenheimer and Car-Parrinello Molecular Dynamics, ML recap, Machine-learned interatomic potentials (MLIPs), Representing Matter, Neural Network Potentials, Active learning, Uncertainty Quantification, Validation, Challenges with Neural Network Potentials, Foundational Models.

Books: 

1. Statistical Mechanics, Donald A. McQuarrie, University Science Books, 2000.

2. Density Functional Theory: A Practical Introduction, David S. Sholl and Janice A. Steckel, John Wiley & Sons, 2009.

3. Understanding Molecular Simulations, Daan Frenkel and Berend Smit, Academic Press, 2001.

4. Deep Learning for Molecules and Materials, Andrew D. White, Living Journal of Computational Molecular Science, Volume 3, Page 1499, 2021. URL: https://dmol.pub