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Issue 18, 2019
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GIGA: a versatile genetic algorithm for free and supported clusters and nanoparticles in the presence of ligands

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Abstract

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.

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

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Publication details

The article was received on 07 Mar 2019, accepted on 16 Apr 2019 and first published on 26 Apr 2019


Article type: Paper
DOI: 10.1039/C9NR02031D
Nanoscale, 2019,11, 9042-9052

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

    M. Jäger, R. Schäfer and R. L. Johnston, Nanoscale, 2019, 11, 9042
    DOI: 10.1039/C9NR02031D

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