09-506 Practicum in Computational Quantum Chemistry
9 units, Carnegie Mellon University
This course introduces students to computational approaches for modeling molecular quantum systems, with an emphasis on practical implementation, numerical methods, and interpretation of results. Rather than focusing primarily on formal derivations, the course centers on using modern computational quantum chemistry tools to explore physical and chemical properties of atoms and molecules. The course uses Mathematica integrated with electronic structure packages such as Gaussian, enabling seamless integration of symbolic and numerical calculations supported by high performance algorithms and dynamic visualization. This approach allows students to develop both intuitive and quantitative understanding of quantum phenomena while working directly with computational models. By leveraging Mathematica's functional programming and symbolic algebra capabilities, the course shifts the emphasis away from traditional programming and toward computational exploration, problem solving, and discovery. In combination with emerging AI assisted tools, this framework lowers the barrier to entry and makes the course accessible to students without prior experience in technical computing. The course incorporates core computational techniques including linear algebra, Fourier analysis, and differential equation methods, alongside an introduction to machine learning approaches for analyzing and modeling complex systems. Topics include numerical solutions to the Schrödinger equation, basis set methods, variational approaches, and electronic structure techniques such as Hartree Fock and Kohn-Sham Density Functional Theory.
Prerequisites: 33-234 and 09-344
Offered: Fall 2026
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