On the problem of cluster structure diversity and the value of data mining.
PHYS CHEM CHEM PHYS
8438 - 8445.
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.
|Title:||On the problem of cluster structure diversity and the value of data mining|
|Keywords:||EVOLUTIONARY ALGORITHMS, GEOMETRY OPTIMIZATION, STRUCTURE PREDICTION, GENETIC ALGORITHM, CRYSTAL-STRUCTURE, NANOPARTICLES, SOLIDS, CARBON, OXIDE, CAGE|
|UCL classification:||UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Maths and Physical Sciences
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