CM⁴ courses

Here you can find computaional courses offered on campus by different departments. The content and teaching modality may differ depending on the instructor. Plese contact the instructor of each semester for more accurate information.

Department of Chemistry and Biochemistry

CHEM 5200/6200, Python Programming in Chemistry: This new graduate/undergraduate level course introduces the basics of the Python programming language and commonly used Python libraries for numerical methods, plotting, database manipulation, quantum chemical calculations, and machine learning. Student activities focus on specific applications in different areas of chemistry.

CHEM 5280/6280, Computational chemistry: This graduate/undergraduate level course intends to familiarize students with the basics concepts of quantum chemistry and provides practical training of how to perform such calculations on a supercomputer. Depending on the instructor, the class is conducted in an active-learning / dynamic lecturing manner and students work on a computational project pertaining to their own area of research throughout the semester, and present the results during a class mini-symposium at the end of the semester.

CHEM 7300, Advanced Physical Chemistry: This course, intended for first-year graduate students, covers the basics of quantum mechanics and classical thermodynamics before connecting those two topics to introduce the foundations of statistical thermodynamics.

CHEM 7330, Chemical Kinetics: This course is an advanced course of the chemical kinetics principles applied in various systems. The connection between information obtained with computational chemistry and experimentally measured rate constants is a main goal of this course.

CHEM 7350, Quantum and Statistical Mechanics: This class’s aim is to enable graduate students with a background in quantum mechanics to understand the concepts that underlie results of ab initio molecular electronic structure calculations that are produced by modern quantum chemistry software and that appear in the current literature. The quantum mechanics part will constitute the bulk of the course. At the end of the semester, a brief introduction to statistical mechanics will be discussed to provide students with the foundation for further, individual studies in statistical mechanics. This is an active-learning / dynamic lecturing class with an ample room for discussion and students present a lecture on a topic of their choice at the end of the semester.

CHEM 7370, Special Topics in Physical Chemistry: The course focuses on the Coupled Cluster (CC) theory for ground and excited electronic states. CC is a very robust approach to describing electronic correlation in a systematically improvable manner. The latter feature makes it possible to estimate errors of calculations independently of experiment and hence unleashes the predictive power of the Schrödinger equation. CC may therefore guide experimental research. The course focuses on the derivation of CC starting from the Schrödinger equation and ending with implementable tensor expressions, which students derive themselves at the end of the semester and use these tensor expressions to write a simple CC code python.

Department of Physics

PHYS 7400, Statistical Physics: Lattice Vibrations, band description of electronic states in metals, semiconductors and insulators, and magnetic, super-conducting and defect properties of solids.

PHYS 8700, Advanced Solid-State Physics: Atomic and electronic structures of solids and the associated electrical, optical and transport properties.

PHYS 5500/6500, Fundamentals of Physics:  Math Methods. This graduate/undergraduate level course intends to This course gives an overview of the fundamental mathematical tools that are useful and needed for graduate level physics courses.

Department of Materials Engineering

MATL 5500/6500, Introduction to Computational Materials Science: This graduate/undergraduate level course introduces students to a variety of materials simulation techniques and provides hands‐on experience with using density functional theory-based simulations. Students will learn fundamental concepts underlying computational approaches in materials science, including classical empirical potentials and quantum first-principles energy models, molecular dynamics simulations, Monte-Carlo methods and basic principles of materials informatics.

MATL 5970/6970, Machine Learning and Materials: This graduate/undergraduate level course aims to introduce students to the basic principles of machine learning and its applications in materials science. You will get hands-on experience in the use of the Python language for data analysis and learn the best practices in developing and interpreting machine learning models for materials property predictions. Prior knowledge of machine learning and Python is not required, but some familiarity with coding languages would be helpful.

Department of Chemical Engineering

CHEN 6970, Molecular Dynamics and Monte Carlo Simulations: This is a 3-credit special topics lecture course that teaches molecular dynamics and Monte Carlo methods for classical particle-based simulations in various statistical-mechanical ensembles. Emphasis is placed on hands-on experience with numerical algorithms, computer programming, and science and engineering applications.

Department of Mechanical Engineering

MECH 7970, Manufacturing Processes and Systems: This graduate level course intends to provide students with an integrated treatment of the analysis of traditional and non-traditional manufacturing processes, their selection and planning, within an economic framework.

MECH 7970, Advanced Topics in Crystal Plasticity: This graduate level course focuses on introducing students the mechanisms governing the yielding, fracture, and fatigue behaviors of crystalline metallic materials and familiarizing them with the common numerical tools to simulate the deformation of polycrystalline microstructures.

MECH 7330, Advanced Failure Analysis of Engineering Materials: This graduate level course focuses on concepts central for failure analysis of engineering materials, which include the theories of elasticity and continuum plasticity, crystal plasticity, fatigue and fracture.