The DFT-genetic algorithm approach for global optimization of subnanometer bimetallic clusters

Research output: Chapter in Book/Report/Conference proceedingChapter

Authors

Colleges, School and Institutes

Abstract

Over the past two decades, there have been significant developments of sophisticated search algorithms, in particular, genetic algorithms (GAs). GAs have been used to predict the structures (and hence the physical and chemical properties) of mixed-metal “nanoalloy” clusters, with an emphasis on subnanometer clusters, via the coupling of the GA with electronic structure calculations, particularly density functional theory. In collaboration with a number of theoreticians and experimentalists, we have employed different computational approaches and high-performance computing architectures to develop our in-house GA codes and used them to complement experimental studies. In this chapter, we review the basics of GAs, as applied to clusters, and historical developments of the GA programs to allow the exploration of cluster energy surfaces and the search for global minima, at different levels of theory. A range of example applications are introduced, included optical, electronic, and catalytic applications of free and surface-supported pure metal clusters and nanoalloys. Finally, we discuss some possible future developments.

Details

Original languageEnglish
Title of host publicationFrontiers of Nanoscience
EditorsStefan Bromley, Scott Woodley
Publication statusPublished - 1 Jan 2019

Publication series

NameFrontiers of Nanoscience
PublisherElsevier
Volume12
ISSN (Print)1876-2778
ISSN (Electronic)1876-276X

Keywords

  • Genetic algorithms, Global optimization, Nanoalloys, Nanoparticles, Subnanometer clusters