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SUMMARY:Recent approaches to the fitting or learning of interatomic potent
 ials for molecules and materials - Bastiaan J Braams\, Centrum Wiskunde &a
 mp\; Informatica (CWI)\, Amsterdam\, The Netherlands
DTSTART:20190320T093000Z
DTEND:20190320T103000Z
UID:TALK121726@talks.cam.ac.uk
CONTACT:Prof. Gabor Csanyi
DESCRIPTION:Over the past several years big data methods\, including but n
 ot limited to use of deep convolutional neural networks\, have been very s
 uccessful in computer science applications and there is increasing effort 
 to apply big data or machine learning methods to problems in physical scie
 nce and engineering. Conversely we are seeing that problems from physical 
 science are influencing machine learning research done in computer science
  environments. In the talk I will provide a survey of recently developed m
 ethods for the construction of effective interatomic potentials and force 
 fields for atomistic modelling of molecular and condensed phase systems. I
  will also show how this work is influencing developments in machine learn
 ing through the concept of deep neural networks that are invariant or cova
 riant (equivariant) with respect to groups of discrete or continuous trans
 formations. In the case of atomistic systems these transformations are the
  spatial point group of translations\, rotations and reflections and the p
 ermutational symmetry group associated with the labeling of chemically ide
 ntical atoms.\n
LOCATION:Oatley 1 Seminar Room\, Dept. of Engineering (Trumpington St)
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