The GA implemented within the Atomic Simulations Environment (ASE) software package 38 has been utilized. A niching fitness function is employed to efficiently search across the full compositional convex hull. When initializing the traditional GA, the population size is set to 150 candidates. The method for selecting parents is handled by roulette wheel selection. Selection probabilities are directly related to the ascribed fitness, which accounts for the stabilities of the nanoparticle. Offspring are created by either mating two parents, or by mutating a single candidate. The mating and mutation routines are mutually exclusive and thus are not allowed to stack i.e. performing crossover and mutation before evaluation. Cut and splice crossover functions, described by Deaven and Ho, are used to generate new candidates with a call probability of 0.6. Random permutation mutations are utilized with a call probability of 0.2, e.g. swapping the positions of two random atoms of different elemental species. A random swap mutation is also employed with a call probability of 0.2, where one atom type is swapped for another. Convergence criteria is assigned through a lack of progression in the population e.g. the fitness of the population does not change for a number of generations. The GA is run with relatively loose convergence criteria, when there has no observed change in the population for two generations, the search is concluded.