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Keynote Seminar: Atomistic simulations in the age of machine learning

Monday 22nd November
12.30pm - 2pm
Online via MS Teams

Register online here


Michele Ceriotti, Ecole Polytechnique Federale de Lausanne


When modelling materials and molecules at the atomic scale, achieving a realistic level of complexity and making quantitative predictions are usually conflicting goals.

In this talk I will summarize the core concepts that have driven the extraordinarily fast progress of the field, discuss some of the most promising modelling techniques that combine physics-inspired and data-driven paradigms to predict atomic-scale properties ranging from the interatomic potential to the electronic charge density and the molecular Hamiltonian.

I will indicate the most pressing open challenges in the field, and present several compelling examples including aqueous systems, semiconductors, metals and molecular materials.

Michele Ceriotti received his Ph.D. in Physics from ETH Zürich in 2010. 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. His research revolves around the atomic-scale modelling of materials, based on the sampling of quantum and thermal fluctuations and on the use of machine learning to predict and rationalize structure-property relations. He has been awarded the IBM Research Forschungspreis in 2010, the Volker Heine Young Investigator Award in 2013, an ERC Starting Grant in 2016, and the IUPAP C10 Young Scientist Prize in 2018.