On the problem of cluster structure diversity and the value of data mining

A A Sokol, C R A Catlow, M Miskufova, S A Shevlin, A A Al-Sunaidi, Aron Walsh, S M Woodley

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Data mining, involving cross examination of cluster structure pools collected for ZnO, GaN, LiF and AgI, has been applied to predict plausible cluster structures of related binary materials. We consider the energy landscapes of (MX) 12 clusters for materials that possess tetrahedral bulk phases, wurtzite or sphalerite, including LiF, BeO, BN, AlN, SiC, CuF, ZnO, GaN, GeC and AgI. The energy is evaluated using the hybrid PBEsol0 density functional for structures optimised at the PBEsol level. We report a novel encapsulated iodide structure for AgI and a series of new CuF structures, where significant differences are found between the results for the two functionals.
Original languageEnglish
Pages (from-to)8438-8445
Number of pages8
JournalPhysical Chemistry Chemical Physics
Issue number30
Publication statusPublished - 2010


  • solids
  • crystal-structure
  • evolutionary algorithms
  • oxide
  • genetic algorithm
  • carbon
  • structure prediction
  • cage
  • nanoparticles
  • geometry optimization

Fingerprint Dive into the research topics of 'On the problem of cluster structure diversity and the value of data mining'. Together they form a unique fingerprint.

Cite this