What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N

Abstract

To address the question posed in the title, we have created, and now report details of, an open-access database of cluster structures with a web-assisted interface and toolkit as part of the WASP@N project. The database establishes a map of connectivities within each structure, the information about which is coded and kept as individual labels, called hashkeys, for the nanoclusters. These hashkeys are the basis for structure comparison within the database, and for establishing a map of connectivities between similar structures (topologies). The database is successfully used as a key element in a data-mining study of (MX) 12 clusters of three binary compounds (LiI, SrO and GaAs) of which the database has no prior knowledge. The structures are assessed on the energy landscapes determined by the corresponding bulk interatomic potentials. Global optimisation, using a Lamarckian genetic algorithm, is used to search for low lying minima on the same energy landscape to confirm that the data-mined structures form a representative sample of the landscapes, with only very few structures missing from the close energy neighbourhood of the respective global minima.

Structure prediction of crystals, surfaces and nanoparticles

“…Tentative global minimum atomic structures for clusters of magnesium silicates as measured using DFT with the PBE0 hybrid functional, as found by Escatllar et al 16 Given clusters of compounds with the same stoichiometry can adopt similar atomic structures it may be more efficient to data-mine published atomic configurations as opposed to performing a search on the energy landscape. [129][130] Reference 13 provides a more complete description of computational modelling of nanoparticles.…”

“…Given that clusters of compounds with the same stoichiometry can adopt similar atomic structures it may be more efficient to data mine published atomic configurations as opposed to performing a search on the energy landscape [129,130]. Woodley & Bromley [13] provide a more complete description of these and other aspects of computational modelling of nanoparticles.…”

BCN-M: A Free Computational Tool for Generating Wulff-like Nanoparticle Models with Controlled Stoichiometry

“…Thus, a confident theoretical study must unravel the rich energy landscape including all the NP isomers. This is commonly performed with global optimization algorithms. − …”