Courses at the Master level

Courses available for Master Level Students.
(Last revision Jan 2021)


Fundamentals of solid-state materials, Nicola Marzari

Taught in 2020-2021

This course focuses on the fundamentals of quantum mechanics as applied to atoms, molecules, and solids and to explain the electronic, optical, and magnetic properties of solids.    

Statistical mechanics, Michele Ceriotti

Taught in 2020-21

This course presents an introduction to statistical mechanics geared towards materials scientists. The concepts of macroscopic thermodynamics will be related to a microscopic picture and a statistical interpretation. Lectures and exercises will be complemented with hands-on simulation projects.

Atomistic and quantum simulations of materials, Giovanni Pizzi

Taught in 2020-21

This course covers theory and application of quantum simulations to model, understand, and predict the properties of real materials. 

Modelling problem solving, computing and visualisation II

Not taught in 2020-21

Some of this course will be given via video conference and will be simultaneously taught with an MIT subject . The course covers development and design of models for materials processes and structure-property relations. It emphasizes techniques for solving equations from models or simulating and visualizing behavior. Topics include symmetry, structure, thermodynamics, solid state physics, mechanics, and data analysis. 

Computer simulation of physical systems I, Alfredo Pasquarello

Taught in 2020-21

The main topics covered by this course are ordinary differential equations, classical molecular dynamics, random variables, random walks, and Monte Carlo integration.

Computational physics III, Oleg Yazyev

Taught in 2020-21

This course teaches the students practical skills needed for solving modern physics problems by means of computation. A number of examples illustrate the utility of numerical computations in various domains of physics. This course deals with Fourier series and transforms, linear systems and matrix manipulation and eigenvalues problems.

Physical and computational organic chemistry, Clémence Corminboeuf

Taught in 2020-21

This course introduces computational organic electronic structure methods as well as physical organic concepts to illustrate the stability and reactivity of organic molecules and rationalise reaction mechanisms.

Molecular quantum dynamics, Jiri Vanicek 

Taught in 2020-21

The course covers several exact, approximate, and numerical methods to solve the time-dependent molecular Schrödinger equation, and applications including calculations of molecular electronic spectra. More advanced topics include introduction to the semiclassical methods and Feynman path integral.

Introduction to electronic structure methods, Ursula Röthlisberger

Taught in 2020-21

This course gives a repetition of the basic concepts of quantum mechanics and main numerical algorithms used for practical implementations, and basic principles of electronic structure methods: Hartree-Fock, many body perturbation theory, configuration interaction, coupled-cluster theory, density functional theory.

Molecular dynamics & Monte-Carlo simulations, Ursula Röthlisberger

Taught in 2020-21 in French

This course is an introduction to molecular dynamics and Monte-Carlo simulation methods. 

Mathematics of data: from theory to computation, Volkan Cevher

Taught in 2020-21

This course reviews recent advances in convex optimization and statistical analysis in the wake of Big Data. It provides an overview of the emerging convex formulations and their guarantees, describes scalable solution techniques, and illustrates the role of parallel and distributed computation.

Statistical methods in atomistic computer simulations, Michele Ceriotti

Taught in 2020-21

This course gives an overview of atomistic simulation methods, combining theoretical lectures and hands-on sessions. It covers the basics (molecular dynamics and Monte Carlo sampling) and also more advanced topics (accelerated sampling of rare events, and non-linear dimensionality reduction).


Computational quantum physics, Titus Neupert, Mark H. Fischer

Taught in 2020-21

This course provides an introduction to simulation methods for quantum systems, starting with the one-body problem and finishing with quantum field theory, with special emphasis on quantum many-body systems. Both approximate methods (Hartree-Fock, density functional theory) and exact methods (exact diagonalization, quantum Monte Carlo) are covered.

Introduction to machine learning for the sciences, Titus Neupert, Mark H. Fischer

Taught in 2020-21

This course is an introduction to the basic concepts of machine learning, including supervised and unsupervised learning with neural networks, reinforcement learning, and methods to make the learned results interpretable. The material is presented with scientific research applications in mind, where data has often very peculiar structure and quantitative accuracy is paramount.

Other locations 

In addition to these classes, relevant courses are held regularly at the