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UID:20250910T133959EDT-9262SIv0Du@132.216.98.100
DTSTAMP:20250910T173959Z
DESCRIPTION:Abstract:\n\nWhen modeling materials and molecules at the atomi
 c scale\, achieving a realistic level of complexity and making quantitativ
 e predictions are usually conflicting goals. Data-driven techniques have m
 ade great strides towards enabling simulations of materials in realistic c
 onditions with uncompromising accuracy. In particular\, statistical regres
 sion techniques have become very fashionable as a tool to predict the prop
 erties of systems at the atomic scale\, sidestepping much of the computati
 onal cost of accurate quantum chemical calculations\, and making it possib
 le to perform simulations that require thorough statistical sampling witho
 ut compromising on the accuracy of the electronic structure model.\n	In thi
 s talk I will argue how data-driven modelling can be rooted in a mathemati
 cally rigorous and physically-motivated symmetry-adapted framework\, and d
 iscuss the benefits of such a principled approach.\n	I will present several
  examples demonstrating how the combination of machine-learning and atomis
 tic simulations can offer useful insights on the behavior of complex syste
 ms\, and discuss the challenges towards an integrated modeling framework i
 n which physics- and data-driven steps can be combined to improve the accu
 racy\, the computational efficiency and the transferability of predictions
 \, from interatomic potentials to electronic-structure properties.\n\nBio:
 \n\nMichele 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 Col
 lege. Since 2013 he leads the laboratory for Computational Science and Mod
 eling 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 an
 d rationalize structure-property relations.  He has been awarded the IBM R
 esearch Forschungspreis in 2010\, the Volker Heine Young Investigator Awar
 d in 2013\, an ERC Starting Grant in 2016\, and the IUPAP C10 Young Scient
 ist Prize in 2018.\n
DTSTART:20211123T180000Z
DTEND:20211123T193000Z
LOCATION:Zoom link: https://mcgill.zoom.us/j/84211926109?pwd=MkdCRnJ4a2M1Nz
 NFTFdZYnR5RzJvdz09
SUMMARY:Chemical Society Seminar: Michele Ceriotti - Machine learning at th
 e atomic scale
URL:/chemistry/channels/event/chemical-society-seminar
 -michele-ceriotti-machine-learning-atomic-scale-333768
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