pyGlobOpt: Parallel Global Optimization Software for Nanoclusters, Materials, and Beyond

With the ever-increasing demand for atomistic structures representative of real-life systems as well as the ad-vent of exascale computers, it has now become necessary and possible to use advanced global optimization (GO) techniques to intelligently sample the potential energy surface (PES). Given the previous studies demonstrating the relative efficiency of the artificial bee colony (ABC) swarm intelligence algorithm for chemical systems, we turn to focus on maximizing the potential of this tool. This is achieved by producing a new software; pyGlobOpt is the first ABC algorithm tool that has an asynchronously parallel implementation, in practice this means that the number of concurrent sample geometries we are able to evaluate is only lim-ited to the size of computer available to us. Furthermore, pyGlobOpt interfaces directly with the atomistic simulation environment providing a huge array of potential energetic evaluators at our disposal to drive our algorithm. In this work, we show the implementation of GO algorithm and demonstrate its utility in a num-ber of examples, including the recovery of a Buckminster fullerene from a random distribution of C atoms and cluster distribution on the surface.