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• W2120813013 abstract "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." @default.
• W2120813013 created "2016-06-24" @default.
• W2120813013 creator A5064011663 @default.
• W2120813013 creator A5064468010 @default.
• W2120813013 creator A5085709641 @default.
• W2120813013 date "2011-11-25" @default.
• W2120813013 modified "2023-10-10" @default.
• W2120813013 title "Development and optimization of a novel genetic algorithm for identifying nanoclusters from scanning transmission electron microscopy images" @default.
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• W2120813013 doi "https://doi.org/10.1002/jcc.21976" @default.
• W2120813013 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22120989" @default.
• W2120813013 hasPublicationYear "2011" @default.
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