Michele Ceriotti

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michele.ceriotti@epfl.ch +41 21 693 29 39 http://cosmo.epfl.ch/

+41 21 693 29 39
EPFL > VPA-AVP-DLE > AVP-DLE-EDOC > EDMX-ENS

EPFL STI IMX COSMO
MXG 337 (Bâtiment MXG)
Station 12
1015 Lausanne

+41 21 693 29 39
Office:  MXG 337
EPFL > STI > IMX > COSMO

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

+41 21 693 29 39
EPFL > STI > IMX > IMX-GE

+41 21 693 29 39
EPFL > STI > STI-DEC > STI-DIR

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Administrative data

Fields of expertise

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

Publications

Infoscience publications

Teaching & PhD

Teaching

Materials Science and Engineering

PhD Students

Abbott Joseph William, Bigi Filippo, Fasching Markus Harald, Forina Alessandro, How Wei Bin, Huguenin-Dumittan Kevin Kazuki, Kellner Matthias Linus, Mazitov Arslan, Rumiantsev Egor, Saade Sandra, Suman Divya,

Past EPFL PhD Students

Anelli Andrea , Baldi Edoardo , Ben Mahmoud Chiheb , Cheng Bingqing , Gasparotto Piero , Giofré Daniele , Goscinski Alexander Jan , Grisafi Andrea , Helfrecht Benjamin Aaron , Imbalzano Giulio , Kapil Venkat , Lopanitsyna Nataliya , Maksimov Dmitrii , Musil Félix Benedito Clément , Nigam Jigyasa , Pozdnyakov Sergey ,

Courses

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)

Lecture series on scientific machine learning

This lecture presents ongoing work on how scientific questions can be tackled using machine learning. Machine learning enables extracting knowledge from data computationally and in an automatized way. We will learn on examples how this is influencing the very scientific method.