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Title: Multitribe evolutionary search for stable Cu–Pd–Ag nanoparticles using neural network models
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.  more » « less
Award ID(s):
1821815
NSF-PAR ID:
10093502
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Physical Chemistry Chemical Physics
Volume:
21
Issue:
17
ISSN:
1463-9076
Page Range / eLocation ID:
8729 to 8742
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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