As part of the academic training afforded to our students, the Simons Center faculty have designed a curriculum to introduce foundational and cutting edge topics in Center’s research areas. In addition to taking some of the courses below, our students often take a graduate chemistry course in materials science or chemical biology, and may also take courses in other relevant departments such as Physics and Applied Math. A full list of Chemistry courses and syllabi is available here.
Statistical Mechanics [CHEM-GA 2600]
- This course is a modern introduction to the topic of statistical mechanics, that is, the way in which the interactions between sufficiently large sets of molecules give rise to experimentally observable properties of a system.
- We strive to make this course as directly useful for understanding research going on in the department, be it in theoretical, physical, materials, or biological chemistry. Hence special emphasis will be given to how theory and computation connects to experiments, e.g. in the areas of phase transitions, spectroscopy, self-assembly, polymers, etc.
Computational Chemistry [CHEM-GA 2627]
- This course is a full-scale introduction to computational chemistry, biomolecular modeling and related informatics tools, including special topics on computational-aided drug design.
- Over the length of this course, a hands on and practical understanding of computational methods (strengths, limitations, applicability) is developed
- By the end of this course, you will be able to apply common computational techniques to advanced problems in molecular modeling.
Quantum Mechanics [CHEM-GA 2600]
A graduate level introduction to quantum mechanics, including the time independent and time dependent Schrodinger equation, perturbation theory and variational methods, path integrals, and density matrices.
Machine Learning in Molecular Science [CHEM-GA 2672]
- This course is an introduction to machine learning and its applications to problems in chemistry.
- It systematically develops a practical understanding of machine learning methods (concepts, intuitions, algorithms, strengths, limitations, applicability)
- At the end, students will have competence in applying these machine learning methods to chemistry data (tools and strategies)
Quantum Chemistry and Advanced Statistical Mechanics [CHEM-GA2666]
This combined course builds upon earlier topics from Quantum and Statistical Mechanics course, to teach advanced topics such as non-equilibrium statistical mechanics, electronic structure methods, path-integral simulation methods, and more.
