HITS-SIMPLAIX joint colloquium Michele Ceriotti: Atomic-scale modeling in the age of machine learning

By Michele Ceriotti, EPFL STI SMX-GE, Lausanne, Switzerland

When modeling materials and molecules at the atomic scale, achieving a realistic level of complexity and making quantitative predictions are usually conflicting goals.
Data-driven techniques have made great strides towards enabling simulations of materials in realistic conditions with uncompromising accuracy.
In this talk I will summarize the core concepts that have driven the extraordinarily fast progress of the field, discussing the relationship to more general concepts in geometric machine learning.
I will describe some of the most promising modeling techniques that combine physics-inspired and data-driven paradigms, indicate the most pressing open challenges, and present several compelling examples ranging from water to semiconductors and from metals to molecular materials.

Short CV:

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 http://ipi-code.org and http://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.

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