Courses at the Master level

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

EPFL 


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).


ETHZ  


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.



ETHZ  

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.


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