Whilst technological advancements have allowed imaging at atomic resolution using scanning transmission electron microscopy (STEM), identification of nanocluster structures has proven difficult due to their low thermal stability, and often resultant low-symmetry. In this work, we look at a novel solution to this problem using a genetic algorithm (GA). GAs are search methods for the minimization of statistical problems based on natural evolution. We develop a STEM model first described by Curley et al. (2007) and, using high-symmetry cluster structures as test subjects, look at the effectiveness and efficiency of the GA at optimizing orientation parameters for a cluster when compared to a model solution. We find for a 309-atom icosahedron that a random minimizing search would prove more efficient than a GA; however, for a 309-atom decahedron the GA becomes more effective and efficient than a random search. We predict that as we continue to lower symmetry of our test cases, we will find the GA becomes even more efficient at optimizing this otherwise computationally expensive problem.
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