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SUMMARY:New Directions for Random Search - Professor Chris Pickard\, Unive
 rsity of Cambridge
DTSTART:20190522T131500Z
DTEND:20190522T141500Z
UID:TALK115339@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Genuinely new knowledge and scientific insight can be obtained
  about matter by combining random numbers with reliable and efficient firs
 t principles methods. Diverse ensembles of initial structures can be gener
 ated\, and structurally optimised. The resulting low energy structures are
  candidates for stable\, and metastable\, phases and/or defects that might
  be experimentally realised. This\, of course\, depends on a sufficiently 
 broad and thorough sampling of configuration space. \n\nAlgorithms which a
 ttempt to learn from (computational) experience are necessarily sequential
 \, and correlated. A purely random strategy\, as employed by Ab Initio Ran
 dom Structure Searching (AIRSS)\,[1\,2] is entirely parallel\, and a natur
 al fit to the high throughput computation (HTC) paradigm. The absence of c
 orrelation between the independent random samples ensures that it is possi
 ble to estimate when a sufficiently dense sampling has been achieved (or a
 t least\, has not been achieved). Challenging cases can be tackled by desi
 gning the initial random structures so that they focus the search in regio
 ns of configuration space that are anticipated to yield success. \n\nThe d
 esign of these random “sensible” structures will be explored\, along w
 ith some new directions which promise to accelerate random search\,[3] and
  recent applications to materials.\n\n[1] C. J. Pickard\, and R. J. Needs\
 , Phys. Rev. Lett.\, 97 (4)\, 045504 (2006) & Journal of Physics-Condensed
  Matter\, 23(5)\, 053201 (2011)\n[2] Released under the GPL2 license: http
 ://www.mtg.msm.cam.ac.uk/Codes/AIRSS\n[3] C. J. Pickard\, “Hyperspatial 
 optimization of structures”\, Phys. Rev. B\, 99\, 054102 (2019)\n
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
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