Clusters or nanoparticles are aggregates of between a few and many millions of atoms or
molecules. They may consist of identical atoms, or molecules, or two or more different …

The current status of global optimization in computational molecular science is characterized
in this article by focusing on one particular area, evolutionary algorithms applied to cluster …

A genetic algorithm approach is applied to the optimization of the potential energy of a wide
range of binary metallic nanoclusters, Ag–Cu, Ag–Ni, Au–Cu, Ag–Pd, Ag–Au, and Pd–Pt …

Recent advances in computational methods for searching for the most stable structures of
alloy nanoparticles are reviewed. A methodology based on extensive global optimization …

Using a DFT-based genetic algorithm (GA) approach, we have determined the most stable
structure and stoichiometry of a 309-atom icosahedral AuCu nanoalloy, for potential use as …

Using a global optimization approach that directly searches for the composition of greatest
stability, we have been able to find the particularly stable structures for binary Lennard …

The putative global minimum structures of bimetallic Cu m Ag n nanoalloys for all (m, n) with
N= m+ n from 2 to 60 atoms have been determined. The embedded-atom method was used …

We propose an evolutionary algorithm (EA) for global optimization of binary atomic clusters.
The method is applied to binary Lennard–Jones (BLJ) clusters as a testing ground, while a …

The structure of the global total-energy minimum for all bimetallic Ni m Ag n nanoalloys with
m+ n= N= 2− 60 atoms has been identified theoretically by combining the embedded-atom …

A new global optimization algorithm, based on the concept of excitable walkers, is proposed.
The walkers perform parallel Monte Carlo walks on the locally minimized potential energy …