Development and optimization of a novel genetic algorithm for identifying nanoclusters from scanning transmission electron microscopy images

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.

Kinematic HAADF-STEM image simulation of small nanoparticles

“…The output of data provides the starting point of the multislice simulation if one wants to study multiple electron scattering effects or surface photon effects by comparing the absolute intensity Aveyard et al, 2014). This software can potentially be incorporated with automatic image recognition to enable easier searching for a particular nanoparticle structure (Logsdail et al, 2012;Flores et al, 2003) and help with sample tilting under STEM mode.…”

Possibility of an integrated transmission electron microscope: enabling complex in-situ experiments

“…Of course, great care must be taken in the development of such an integrated system, so as not to lose the strengths and core imaging modes of the individual techniques. However, the incorporation of recent and rapid advancements in genetic algorithms [42][43][44] will mitigate a number of complex design variables by adapting existing designs for fully functional state-of-the-art control systems and by including multiple design options in a single device. It is our hope that this evolutionary approach would minimize the trade-offs in capabilities by finding unexpected and unconventional design paths not envisioned by classical microscope design strategies.…”

First principles global optimization of metal clusters and nanoalloys

“…Many different GAs have been developed for the GO of clusters and applied to a myriad of cluster systems [8,10,[38][39][40][41][42]46,58,81,98,104,105,111,113,[117][118][119][120][121][122][123][124][125][126][127][128][129][130][131].…”