The determination of the global minimum of a potential energy surface is an important and fundamental problem in computational chemistry of catalysis and separation. As the system size increases, and without the formal mathematical conditions that differentiate local minima from the global minimum, it may become a daunting task. Global optimization algorithms have been the subject of studies for decades, resulting in methods like simulated annealing [1], Monte Carlo minimization [2], basin hopping [3], or stochastic walking [4]. However, some of these codes are either not readily available, are limited to specific systems, or lack robust interfaces to other computational chemistry programs. Here, we will present an efficient global optimization software, the Northwest Potential Energy Surface Search Engine (NWPEsSe) [5], which is based on a swarm intelligence algorithm [2]. The code has been successfully applied to a wide range of systems, from the formation of complex ionic liquid clusters to the adsorption of organic molecules on metal oxide surface and other large nanosized systems. In addition, we have developed a number of interfaces to several popular quantum chemistry and statistical mechanics programs such as LAMMPS, GROMACS, MOPAC, xTB, ORCA, CP2K, PSI4, VASP, etc. We anticipate that the flexibility and efficiency of NWPEsSe will make it a useful tool for many scientists in different fields. NWPEsSe is available at https://github.com/pnnl/NWPEsSE .

NWPEsSe: A global optimization algorithm based on swarm intelligence to sample the lowest minima of large nanostructures

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