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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. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011.
Original language | English |
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Pages (from-to) | 391-400 |
Number of pages | 10 |
Journal | Journal of Computational Chemistry |
Volume | 33 |
Issue number | 4 |
DOIs | |
Publication status | Published - 5 Feb 2012 |
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Dive into the research topics of 'Development and optimization of a novel genetic algorithm for identifying nanoclusters from scanning transmission electron microscopy images.'. Together they form a unique fingerprint.Projects
- 1 Finished
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Towards an Atomic-scale understanding of the 3D Structures of size-selected Clusters on Surfaces
Li, Z., Johnston, R. & Palmer, R.
Engineering & Physical Science Research Council
1/02/10 → 17/01/14
Project: Research Councils