Michele Ceriotti

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. ResearchCurrent Research Fields Atomistic computer simulations, statistical mechanics, machine learning, molecular dynamics, nuclear quantum effects, aqueous systems, molecular materials, high-entropy materials. Teaching & PhD PhD Students Arslan Mazitov, Johannes Martin Spies, Markus Harald Fasching, Sandra Saade, Joseph William Abbott, Egor Rumiantsev, Sofiia Chorna, Matthias Linus Kellner, Qianjun Xu, Wei Bin How, Filippo Bigi, Alessandro Forina Past EPFL PhD Students 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, Divya Suman 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. Itcovers the basics (molecular dynamics and monte carlo sampling) and also more advanced topics (accelerated samplingof rare events, and non-linear dimensionality reduction) 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