Evolving better nanoparticles: Genetic algorithms for optimising cluster geometries
Research output: Contribution to journal › Article
Colleges, School and Institutes
A review is presented of the design and application of genetic algorithms for the geometry optimisation of clusters and nanoparticles, where the interactions between atoms, ions or molecules are described by a variety of potential energy functions. A general introduction to genetic algorithms is followed by a detailed description of the genetic algorithm program that we have developed to identify the lowest energy isomers for a variety of atomic and molecular clusters. Examples are presented of its application to model Morse clusters, ionic MgO clusters and bimetallic "nanoalloy" clusters. Finally, a number of recent innovations and possible future developments are discussed.
|Number of pages||15|
|Publication status||Published - 1 Jan 2003|