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Physics Based and Data-driven Multiscale Materials Modeling

June 23, 2020 from 10:00 am11:00 am

Zoom Webinar with Michele Ceriotti
Materials Science and Engineering
EPFL, Lausanne, Switzerland

Abstract

Machine learning models are proving to be extremely effective in predicting the properties of atomistic configurations of matter, circumventing the need for time consuming electronic structure calculations when modeling materials at the atomic scale.

The most successful schemes achieve transferability by means of a local representation of structures, in which the problem of predicting a property is broken down into the prediction of local, atom-centered contributions. I will presented an overview of these approaches, including examples of applications to different classes of materials.

Locality, however, breaks down when describing long-range inter-atomic forces, such as those arising due to electrostatic interactions. I will present a possible solution to this conundrum based on the long-distance equivariant (LODE) framework, that combines a local description of matter with the appropriate, long-range asymptotic behavior of interactions.

 

Biography

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 has led 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.

 

For Zoom information please contact dei-info@udel.edu

 

Details

Date:
June 23, 2020
Time:
10:00 am — 11:00 am