Michele Ceriotti

MXG 337 (Bâtiment MXG)
Station 12
CH-1015 Lausanne

MXF 110 (Bâtiment MXF)
Station 12
CH-1015 Lausanne

MXG 337 (Bâtiment MXG)
Station 12
CH-1015 Lausanne

Web site:  Web site:  https://cosmo.epfl.ch/

PH A2 364 (Bâtiment PH)
Station 3
CH-1015 Lausanne

Administrative data

Fields of expertise

Atomistic computer simulations, statistical mechanics, machine learning, molecular dynamics, nuclear quantum effects, molecular materials, nucleation. 


Infoscience publications

Teaching & PhD


Materials Science and Engineering

PhD Programs

Doctoral Program in Materials Science and Engineering

Doctoral Program in Physics

Doctoral Program in Chemistry and Chemical Engineering


Summer School on Theoretical modelling nanoscale

(Coursebook not yet approved by the section)

Introduction to atomic-scale modeling

This course provides an introduction to the modeling of matter at the atomic scale, using interactive jupyter notebooks to see several of the core concepts of materials science in action.

Statistical mechanics

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.

Statistical methods in atomistic computer simulations

The 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).

CCMX Winter School - Additive Manufacturing of Metals and the Material Science Behind It

This course is designed to cover a series of important scientific aspects related to the field of additive manufacturing of metals and alloys and to provide an in-depth review of corresponding fundamentals. It features 9 modules consisting of presentations given by lecturers and the participants.

Lecture series on scientific machine learning

Machine learning is a data analysis and computational tool that in the last two decades brought groundbreaking progress into many modern technologies. What is more, machine learning is becoming an indispensable tool enabling progress in many scientific disciplines where knowledge is deduced from data. This course will present some recent works in this direction. In the first part of the