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Michele Ceriotti

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

Mission

Development and application of statistical sampling and machine-learning algorithms to achieve predictive atomic-scale modelling of molecules and materials, and to understand structure-property relations.
Michele Ceriotti received his Ph.D. in Physics from ETH Zürich. He spent three years in Oxford as a Junior Research Fellow at Merton College. Since 2013 he leads the laboratory for Computational Science and Modeling, in the institute of Materials at EPFL, that focuses on method development for atomistic materials modeling based on statistical mechanics and machine learning. He is one of the core developers of several open-source software packages, including metatensor.org, ipi-code.org and chemiscope.org, and proudly serves the atomistic modeling community as an associate editor of the Journal of Chemical Physics, as a moderator of the physics.chem-ph section of the arXiv, and as an editorial board member of Physical Review Materials.

Awards

Volker Heine Young Investigator Award

2013

ERC Starting Grant

European Research Council

2016

IUPAP-C10 Young Scientist Prize

IUPAP

2018

ERC Consolidator Grant

European Research Council

2021

Fellow of the European Lab for Learning and Intelligent Systems (ELLIS)

ELLIS

2023

E. Bright Wilson Prize

Department of Chemistry, Harvard University

2024

Research

Current Research Fields

Atomistic computer simulations, statistical mechanics, machine learning, molecular dynamics, nuclear quantum effects, aqueous systems, molecular materials, high-entropy materials. 

Teaching & PhD

Current Phd

Arslan Mazitov, Johannes Martin Spies, Markus Harald Fasching, Sandra Saade, Divya Suman, Joseph William Abbott, Egor Rumiantsev, Sofiia Chorna, Matthias Linus Kellner, Qianjun Xu, Wei Bin How, Filippo Bigi, Alessandro Forina

Past Phd As Director

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

Courses

Introduction to atomic-scale modeling

MSE-305

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.

Lecture series on scientific machine learning

PHYS-754

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.

Statistical mechanics

MSE-421

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

MSE-639

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)