Application of Genetic Algorithms in Nanoscience: Cluster Geometry Optimization

Authors:
Roy L. Johnston;Thomas V. Mortimer-Jones;Christopher Roberts;Sarah Darby;Frederick R. Manby
Affiliations:
-;-;-;-;-
Venue:
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
Year:
2002

Citing 3
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Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
A limited memory algorithm for bound constrained optimization

SIAM Journal on Scientific Computing
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning

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Abstract

An account is presented of the design and application of Genetic Algorithms for the geometry optimization (energy minimization) of clusters and nanoparticles, where the interactions between atoms, ions or molecules are described by a variety of potential energy functions (force fields). Adetailed description is presented of the Birmingham Cluster Genetic Algorithm Program, developed in our group, and two specific applications are highlighted: the use of a GAto optimize the geometry and atom distribution in mixed Cu-Au clusters; and the use of an energy predator in an attempt to identify the lowest six isomers of C40.