NWPEsSe: An Adaptive-Learning Global Optimization Algorithm for Nanosized Cluster Systems

Global optimization constitutes an
important and fundamental problem
in theoretical studies in many chemical fields, such as catalysis,
materials, or separations problems. In this paper, a novel algorithm has been developed
for the global optimization of large systems including neat and ligated
clusters in the gas phase and supported clusters in periodic boundary
conditions. The method is based on an updated artificial bee colony
(ABC) algorithm method, that allows for adaptive-learning during the
search process. The new algorithm is tested against four classes of
systems of diverse chemical nature: gas phase Au55, ligated
Au82+, Au8 supported on graphene oxide and defected rutile, and a large
cluster assembly [Co6Te8(PEt3)6]­[C60]n, with sizes
ranging between 1 and 3 nm and containing up to 1300 atoms. Reliable
global minima (GMs) are obtained for all cases, either confirming
published data or reporting new lower energy structures. The algorithm
and interface to other codes in the form of an independent program,
Northwest Potential Energy Search Engine (NWPEsSe), is freely available,
and it provides a powerful and efficient approach for global optimization
of nanosized cluster systems.