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SUMMARY:Structure prediction and computational materials discovery
DTSTART:20170531T163000
DTEND:20170531T173000
DTSTAMP:20260411T110545Z
UID:487b49497021a3db84b0df353413f5e09ee6552fe450f1ad6a9cb56c
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Chris Pickard\nUniversity of Cambridge\nUK\nThe first pr
 inciples computation of materials properties was once restricted to the i
 nvestigation of a few structures at a time. The dramatic growth in the ava
 ilability of computational resources now allows many thousands of computa
 tions to be performed independently and at the same time. One use of this
  power is to screen for novel or extreme materials\, taking existing data
 bases of crystal structures\, or modifications of them\, and computing th
 eir properties. While making use of existing knowledge of materials is an
  excellent starting point\, in many cases\, important structures are miss
 ing\, not known\, or even\nreadily accessible. This is particularly the ca
 se for materials under extreme conditions\, non-crystalline material stru
 cture\, such defects – point defects\, interfaces\, or surfaces – and
  nanostructures. To complement what we already know\, it is essential to
  be able to make good\, diverse and realistic\, suggestions of possible s
 tructures at the atomic level. I will describe a straightforward approach
  to first principles structure prediction - Ab Initio Random Structure Se
 arching (AIRSS)[1]\, and discuss applications from materials under extreme
  compression to grain boundary and interface structure determination[2].\
 n[1] C. J. Pickard\, and R. J. Needs\, Journal of Physics-Condensed Matter
 \, 23(5)\, 053201 (2011)\n[2] G. Schusteritsch\, and C. J. Pickard Phys. 
 Rev. B90\, 035424 (2014). \n\n 
LOCATION:BCH 2201 https://plan.epfl.ch/?room==BCH%202201
STATUS:CONFIRMED
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