|About this Abstract
||2016 TMS Annual Meeting & Exhibition
||Hume-Rothery Award Symposium: Thermodynamics of Materials
||Genetic Algorithm Structure Optimization Applied to Defect Clusters and Nanoparticles with Integrated Experimental Data
||Dane Morgan, Min Yu, Amy Kaczmarowski, Hyunseok Ko, Paul Voyles
|On-Site Speaker (Planned)
In this presentation we discuss application of the genetic algorithm (GA) approach to modeling of nanoscale defect clusters embedded in a bulk material and to extraction of nanoparticle structures from integrated simulation and experimental information. We show that by defining appropriate regions in the system it is possible to use GA to determine the fundamentally metastable structure of defect clusters in a bulk system, providing a powerful tool for finding these often complex and unintuitive structures. We also demonstrate how GA can be applied to a cost function consisting of the total energy and the error on forward simulation of experimental data to determine structures that both match known experimental data and minimize the total energy. We develop this latter approach for use with STEM data and demonstrate the method by determining both model structures and a full three-dimensional Au nanoparticle structure with about 5,000 atoms.
||Planned: A print-only volume