GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

We present a versatile parallelised genetic algorithm, which is able to perform global optimisation from first principles for pure and mixed free clusters in the gas phase, supported on surfaces or in the presence of one or several atomic or molecular species (ligands or adsorbates). The genetic alg...

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Bibliographic Details
Published in:Nanoscale 2019-05, Vol.11 (18), p.9042-9052
Main Authors: Jäger, Marc, Schäfer, Rolf, Johnston, Roy L
Format: Article
Language:English
Subjects:
Adsorbates
Clusters
Electron spin
First principles
Genetic algorithms
Graphene
Hydrogen sulfide
Hyperspaces
Ligands
Nanoalloys
Nanoparticles
Optimization
Organic chemistry
Potential energy
Quantum chemistry
Software packages
Vapor phases
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Summary:We present a versatile parallelised genetic algorithm, which is able to perform global optimisation from first principles for pure and mixed free clusters in the gas phase, supported on surfaces or in the presence of one or several atomic or molecular species (ligands or adsorbates). The genetic algorithm is coupled to different quantum chemical software packages in order to permit a large variety of methods for the global optimisation. The genetic algorithm is also capable of optimising different electronic spin multiplicities explicitly, which allows global optimisation on several potential energy hypersurfaces in parallel. We employ the genetic algorithm to study ligand-passivated clusters [Cd3Se3(H2S)3]+ and to investigate adsorption of [Pt6(H2O)2]+ supported on graphene. The explicit consideration of the electronic spin multiplicity during global optimisation is investigated for nanoalloy clusters Pt4V2.
ISSN:2040-3364
2040-3372
DOI:10.1039/c9nr02031d