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SUMMARY:Design of Advanced Materials?
DTSTART:20191107T171500
DTEND:20191107T181500
DTSTAMP:20260504T073626Z
UID:ccd1025f34d62df886b0f51ee50f322d847e9e4bee7a0fd9cce25015
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Matthew Rosseinsky\nUniversity of Liverpool\nThe knowled
 ge we have developed through the synthesis and experimental study of exten
 ded solids allows us to efficiently identify new materials\, in many cases
  with scientifically interesting or technically important changes in prope
 rties. An example is the chemical control of the transparent conducting be
 haviour of correlated metals (1)\, evaluated as epitaxial films through op
 tical and transport data. The selection of d0 cations to stabilise oxygen-
  oxygen bond formation upon deep oxidation of lithium ion cathodes is a fu
 rther example (2). Here computation provides underpinning guidance in the 
 selection of experimental targets.\nHowever\, the large potential range of
  accessible compositions and structures challenges our present capabilitie
 s. As part of the current interest in exploring computationally-enabled ro
 utes to new materials\, we are developing computational tools for the iden
 tification of stable new compositions. We have recently (3) been able to p
 redict ab initio the regions of composition space that afford new material
 s\, and then subsequently isolate those materials experimentally\, using t
 he computation of the energies of probe structures identified by new cryst
 al structure prediction methods (4) to explore the space. The presentation
  will discuss the potential offered by informatics approaches often referr
 ed to as machine learning in such work.\n(1) J.L. Stoner et al.\, Advanced
  Functional Materials 29\, 1808609\, 2019 (2) Z. Taylor et al.\, J. Am. Ch
 em. Soc. 141\, 7333\, 2019\n(3) C. Collins et al.\, Nature 546\, 280-284\,
  2017\n(4) C. Collins et al.\, Faraday Discussions 211\, 117\, 2018
LOCATION:BCH 2201 https://plan.epfl.ch/?room==BCH%202201
STATUS:CONFIRMED
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