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SUMMARY:New directions for random search - Chris Pickard (University of Ca
 mbridge)
DTSTART:20190226T150000Z
DTEND:20190226T154500Z
UID:TALK120859@talks.cam.ac.uk
CONTACT:INI IT
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. Algorithms which attem
 pt to learn from (computational) experience are necessarily sequential\, a
 nd correlated. A purely random strategy\, as employed by Ab Initio Random 
 Structure Searching (AIRSS)\,[1\,2] is entirely parallel\, and a natural f
 it to the high throughput computation (HTC) paradigm. The absence of corre
 lation between the independent random samples ensures that it is possible 
 to estimate when a sufficiently dense sampling has been achieved (or at le
 ast\, has not been achieved). Challenging cases can be tackled by designin
 g the initial random structures so that they focus the search in regions o
 f configuration space that are anticipated to yield success. The design of
  these random &ldquo\;sensible&rdquo\; structures will be explored\, along
  with some new directions which promise to accelerate random search\,[3] a
 nd recent applications to materials.<br>[1] C. J. Pickard\, and R. J. Need
 s\, Phys. Rev. Lett.\, 97 (4)\, 045504 (2006) &amp\; Journal of Physics-Co
 ndensed<br>Matter\, 23(5)\, 053201 (2011)<br>[2] Released under the GPL2 l
 icense: <a target="_blank" rel="nofollow" href="http://www.mtg.msm.cam.ac.
 uk/Codes/AIRSS">http://www.mtg.msm.cam.ac.uk/Codes/AIRSS</a><br>[3] C. J. 
 Pickard\, &ldquo\;Hyperspatial optimization of structures&rdquo\;\, Phys. 
 Rev. B\, 99\, 054102 (2019)<br>*C.J.P. is supported by the Royal Society t
 hrough a Royal Society Wolfson Research Merit award<br>and the EPSRC throu
 gh Grants No. EP/P022596/1.<br>Biography:<br>Chris Pickard is the Sir Alan
  Cottrell Professor of Materials Science in the Department of Materials<br
 >Science and Metallurgy\, University of Cambridge. Previously he was Profe
 ssor of Physics\, University<br>College London (2009-2015)\, and Reader in
  Physics\, University of St Andrews (2006-2008). He<br>has held both EPSRC
  Advanced and Leadership Research Fellowships\, and is currently a Royal<b
 r>Society Wolfson Research Merit Award holder (2015). He is a lead develop
 er of the widely used<br>CASTEP code\, and introduced both the GIPAW appro
 ach to the prediction of magnetic resonance<br>parameters and Ab Initio Ra
 ndom Structure Searching (AIRSS). In 2015 he won the Rayleigh Medal<br>and
  Prize of the Institute of Physics\, awarded for distinguished research in
  theoretical\, mathematical<br>or computational physics.<br><a target="_bl
 ank" rel="nofollow" href="http://www.msm.cam.ac.uk/department/profiles/por
 trait/Pickard.jpg">http://www.msm.cam.ac.uk/department/profiles/portrait/P
 ickard.jpg</a><br>Web page:<br><a target="_blank" rel="nofollow" href="htt
 p://www.mtg.msm.cam.ac.uk/">http://www.mtg.msm.cam.ac.uk/</a><br>
LOCATION:Seminar Room 2\, Newton Institute
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