For theoretical studies of a cluster model, the first and fundamental step is to find its most stable structure, i.e., the global minimum (GM) on its potential energy surface (PES). Many existing algorithms for such global optimization problems suffer from several defects like that they can be applied only on small systems and are not flexible enough to treat complicated clusters like molecular or surface-supported clusters. We have developed the artificial bee colony (ABC) algorithm for efficient global optimization of any kinds of clusters, and it is wrapped in a program called ABCluster. The ABC algorithm is inspired by the foraging behavior of bee colonies. The bee colonies have developed a strategy to look for the best nectar as their food source, and the ABC algorithm manipulates a population of cluster structures by mimicking this strategy to locate the GM. By applying various kinds of operators on the population in the algorithm process, the probability of the GM within the population increases. The ABC algorithm has been optimized for various kinds of atomic and molecular clusters in gas phase, electric field, ligated, and surface-supported form. The ABC algorithm has been proved to be effective in finding reasonable GMs and ABCluster has become a useful tool for chemists.

ABCluster: Enables agile and efficient global optimization of chemical clusters

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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