What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N†‡
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.
- This article is part of the themed collection: Methods and applications of crystal structure prediction