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SUMMARY:Special MechE Colloquium: Physics based and data-driven multiscale
  materials modelling
DTSTART:20200430T160000
DTEND:20200430T170000
DTSTAMP:20260407T215509Z
UID:72136d9bb2efd992ea1540f8fa9598536b575fc8393f18e16af6f4a9
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Michele Ceriotti\, Laboratory of Computational Science a
 nd Modelling\, School of Engineering\, Institute of Materials\, EPFL\nAbst
 ract:\nMachine learning models are proving to be extremely effective in p
 redicting the properties of atomistic configurations of matter\, circumve
 nting the need for time consuming electronic structure calculations when 
 modeling materials at the atomic scale. The most successful schemes achie
 ve transferability by means of a local representation of structures\, in 
 which the problem of predicting a property is broken down into the predic
 tion of local\, atom-centered contributions. I will presented an overview
  of these approaches\, including examples of applications to different c
 lasses of materials. Locality\, however\, breaks down when describing lon
 g-range inter-atomic forces\, such as those arising due to electrostatic
  interactions. I will present a possible solution to this conundrum base
 d on the long-distance equivariant (LODE) framework\, that combines a loc
 al description of matter with the appropriate\, long-range asymptotic beh
 avior of interactions. \n\nBio:\nMichele Ceriotti is a Tenure-Track Assi
 stant Professor in the department of Materials Science at EPFL where he h
 as established the Laboratory of Computational Science and Modeling (COSM
 O). His research spans different classes of compounds\, including hydrogen
 -bonded compounds\, metals and materials for energy applications\, with th
 e goal of increasing both the predictive and interpretative power of compu
 ter simulations when it comes to understanding the relationships between s
 tructure and properties of materials. Dr. Ceriotti obtained his PhD in Phy
 sics from ETH Zurich in 2010\, working in the group of Michele Parrinello
  to develop algorithms to improve several aspects of molecular dynamics s
 imulations. These included linear-scaling electronic structure methods to 
 simulate larger systems\, a novel framework to use correlated-noise Langev
 in dynamics to manipulate with exquisite precision the sampling properties
  of molecular dynamics\, and a non-linear dimensionality reduction method 
 to describe in a coarse-grained manner the configuration space of structur
 ally complex materials. After graduating\, he moved to Oxford. After a bri
 ef collaboration with Andrea Cavalleri and Nicola Marzari\, he joined the 
 group of David Manolopoulos in the department of Theoretical Chemistry. He
  combined path integral molecular dynamics and correlated-noise generalize
 d Langevin equations to dramatically reduce the computational burden assoc
 iated with the modeling of the quantum properties of light nuclei. These e
 nhanced methods made it possible to understand some features of the behavi
 or of water\, including quantum fluctuations of the hydrogen bond\, isotop
 e effects on the melting of water\,and isotope fractionation at the water-
 vapor interface. He joined EPFL in Fall 2013.
LOCATION:Zoom https://epfl.zoom.us/j/96418170418
STATUS:CONFIRMED
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