Courses at the Master level

Courses available for Master Level Students.
(Last revision March 2018)


Fundamentals of solid-state materials, Nicola Marzari

Taught in 2017-18

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 & Francesco Stellacci

Taught in 2017-18

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, Nicola Marzari

Taught in 2017-18

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,Craig W. Carter & Michele Ceriotti

Taught in 2017-18

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.

Quantum simulations of materials: Properties and spectroscopies, Nicola Marzari

Taught in 2017-18

This course presents the theory and application of quantum simulations to model, understand, and predict the properties of real materials, including electronic structure and first-principles approaches, temperature and thermodynamic averages, or how to obtain materials' properties from simulations.

Computer simulation of physical systems I, Alfredo Pasquarello

Taught in 2017-18

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

Computer simulation of physical systems II, Alfredo Pasquarello

Not taught in 2017-18

The course treats the model of diffusion limited aggregation, density functional theory and its applications, and the solution of the Schrödinger equation by variational and diffusion Monte Carlo.

Computational physics III, Oleg Yazyev

Taught in 2017-18

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 2017-18

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.

Applied molecular quantum chemistry, François Rotzinger

Not taught in 2017-18

This course is an introduction of the bases allowing the computation of chemical reactions and spectroscopic properties of molecules or ions in the gas phase and in solution using quantum chemical methods.

Molecular quantum dynamics, Jiri Vanicek 

Taught in 2017-18

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 2017-18

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 2017-18 in French

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

Computational methods in molecular quantum mechanics, Sara Bonella

Taught in 2017-18

This course will discuss the main methods for the simulation of quantum time dependent properties for molecular systems. Basic notions of density functional theory and of its time dependent version will be covered in the context of adiabatic and non adiabatic dynamics.

Systems for data science, Christoph Koch

Taught in 2017-18

This course covers fundamental principles for understanding and building systems for managing and analyzing large amounts of data.

Mathematics of data: from theory to computation, Volkan Cevher

Taught in 2017-18

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 2017-18

This course gives an overview of simulation techniques that are useful for the computational modeling of materials and molecules at the atomistic level. The students will learn about basic and advanced methods to evaluate thermodynamic averages by molecular dynamics, including accelerated sampling for the study of rare events, and non-linear dimensionality reduction to study structurally-complex systems.


Molecular and materials modeling, Joost van de Vondele and Daniele Passerone

This course introduces the basic techniques to interpret experiments with contemporary atomistic simulation. These techniques include force fields or density functional theory (DFT) based molecular dynamics and Monte Carlo. Structural and electronic properties, thermodynamic and kinetic quantities, and various spectroscopies will be simulated for nanoscale systems.

Introduction to computational physics, Hans Herrmann

This course offers an introduction to computer simulation methods for physics problems and their implementation on PCs and super computers: classical equations of motion, partial differential equations (wave equation, diffusion equation, Maxwell's equation), Monte Carlo simulations, percolation, phase transitions.

Programming techniques for scientific simulations I, Roger Käppeli

This lecture provides an overview of programming techniques for scientific simulations. The focus is on advances C++ programming techniques and scientific software libraries. Based on an overview over the hardware components of PCs and supercomputer, optimization methods for scientific simulation codes are explained.
Videos of the course given in 2013 are visible here.

Computational quantum physics, Alexey Soluyanov

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.

University of Zurich

Condensed matter electronic structure theory, Jürg Hutter & Marcella Mauri-Iannuzzi

This lecture presents computational methods and theoretical approaches commonly used to determine many properties of materials. The presented techniques are based on the fundamental equations for the electrons and can provide insight into the physics and chemistry of real systems and observed phenomena. It focuses on density functional theory, which is the most widely used approach in the field of condensed matter electronic structure calculations.

University of Basel

Computational Quantum Mechanics Based Design of Matter: Discovering Novel Molecules, Liquids, or Materials 2 CP, Anatole von Lilienfeld

How to computationally design new molecules and materials is the focus of this course. First, it discusses the under- lying fundamentals that relate structure to function. Functions covered include biological, chemical and physical properties. Thereafter, several optimization strategies such as high-throughput screening, stochastic approaches (genetic algorithms), and gradient based methods are explained. Finally, successful application examples from the literature are reviewed. 

Università della Svizzera italiana

Molecular Dynamics and Monte Carlo Methods, Igor Pivkin

Taught in 2017-18

This course serves as an introduction to the basic principle of molecular simulation using molecular dynamics and Monte Carlo sampling. We will present the algorithms and techniques used to implement these sampling methods. We will show how molecular simulations can be analyzed using the concepts of order parameters and free energy surfaces. We will also discuss the challenges of obtaining proper sampling in molecular simulations and how they can be tackled by employing advanced sampling techniques like umbrella sampling, metadynamics, and replica-exchange.
The techniques and algorithms introduced will be motivated by considering real-life applications of molecular simulations in various fields of physics, chemistry, and biology. Hands-on examples will be presented using simple programs and open source software packages. Final examination will be based on projects where the students need to implement some of the methods and algorithms covered in the course.

Other courses of interest at USI are featured in this list.

Other locations 

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

Additionally, the MolSim-2018 school is held by CECAM, January 8-19, 2018, and organized by Berend Smit and others. This school provides training in the field of simulation techniques for the study of many-particle (molecular) systems.