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SUMMARY:Machine learned force fields and potential energy surfaces - Prof.
  Gabor Csanyi (Engineering Laboratory)
DTSTART:20200826T083000Z
DTEND:20200826T090000Z
UID:TALK150448@talks.cam.ac.uk
CONTACT:Joseph Nelson
DESCRIPTION:There is a long tradition in computational chemistry and mater
 ials science of representing the Born-Oppenheimer potential energy surface
  of molecules\, clusters of molecules and extended materials using empiric
 al force fields on the one hand\, and also\, for small systems\, using sys
 tematic expansions that have essentially arbitrary accuracy. The formalism
  of “machine learning” (non-parametric function fitting in high dimens
 ions) unites these approaches. New kinds of parametrisations are the resul
 t\, with a computational expense in between that of simple force fields an
 d quantum chemistry\, and leading to diverse applications. Notable work in
  my group include potentials for amorphous carbon\, silicon with defects\,
  other difficult elemental systems such as boron and phosphorus\, as well 
 as regression of molecular properties. I will outline my vision for the br
 ight future of force field modelling.\n\n*This talk will be held online us
 ing Zoom. Please "register your email address here":https://forms.gle/9Rpf
 2BekocwLpmHD9 to receive Zoom links via email.*\n\n*Workshop website*: htt
 ps://www.mtg.msm.cam.ac.uk/events/msm-aimr-joint-online-workshop-2020\n\n
LOCATION:Online - Zoom
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