Revised Basin-Hopping Monte Carlo Algorithm for Structure Optimization of Clusters and Nanoparticles

Suggestions for improving the Basin-Hopping
Monte Carlo (BHMC)
algorithm for unbiased global optimization of clusters and nanoparticles
are presented. The traditional basin-hopping exploration scheme with
Monte Carlo sampling is improved by bringing together novel strategies
and techniques employed in different global optimization methods,
however, with the care of keeping the underlying algorithm of BHMC
unchanged. The improvements include a total of eleven local and nonlocal
trial operators tailored for clusters and nanoparticles that allow
an efficient exploration of the potential energy surface, two different
strategies (static and dynamic) of operator selection, and a filter
operator to handle unphysical solutions. In order to assess the efficiency
of our strategies, we applied our implementation to several classes
of systems, including Lennard-Jones and Sutton-Chen clusters with
up to 147 and 148 atoms, respectively, a set of Lennard-Jones nanoparticles
with sizes ranging from 200 to 1500 atoms, binary Lennard-Jones clusters
with up to 100 atoms, (AgPd)55 alloy clusters described
by the Sutton-Chen potential, and aluminum clusters with up to 30
atoms described within the density functional theory framework. Using
unbiased global search our implementation was able to reproduce successfully
the great majority of all published results for the systems considered
and in many cases with more efficiency than the standard BHMC. We
were also able to locate previously unknown global minimum structures
for some of the systems considered. This revised BHMC method is a
valuable tool for aiding theoretical investigations leading to a better
understanding of atomic structures of clusters and nanoparticles.