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Issue 17, 2019
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Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models

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Abstract

We present an approach based on two bio-inspired algorithms to accelerate the identification of nanoparticle ground states. We show that a symbiotic co-evolution of nanoclusters across a range of sizes improves the search efficiency considerably, while a neural network constructed with a recently introduced stratified training scheme delivers an accurate description of interactions in multielement systems. The method's performance has been examined in extensive searches for stable elemental (30–80 atoms), binary (50, 55, and 80 atoms), and ternary (50, 55, and 80 atoms) Cu–Pd–Ag clusters. The best candidate structures identified with the neural network model have consistently lower energy at the density functional theory level compared with those found with traditional interatomic potentials.

Graphical abstract: Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models

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Publication details

The article was received on 11 Feb 2019, accepted on 02 Apr 2019 and first published on 02 Apr 2019


Article type: Paper
DOI: 10.1039/C9CP00837C
Phys. Chem. Chem. Phys., 2019,21, 8729-8742

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    Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models

    S. Hajinazar, E. D. Sandoval, A. J. Cullo and A. N. Kolmogorov, Phys. Chem. Chem. Phys., 2019, 21, 8729
    DOI: 10.1039/C9CP00837C

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