An efficient genetic algorithm for structure prediction at the nanoscale

Abstract

We have developed and implemented a new global optimization technique based on a Lamarckian genetic algorithm with the focus on structure diversity. The key process in the efficient search on a given complex energy landscape proves to be the removal of duplicates that is achieved using a topological analysis of candidate structures. The careful geometrical prescreening of newly formed structures and the introduction of new mutation move classes improve the rate of success further. The power of the developed technique, implemented in the Knowledge Led Master Code, or KLMC, is demonstrated by its ability to locate and explore a challenging double funnel landscape of a Lennard-Jones 38 atom system (LJ). We apply the redeveloped KLMC to investigate three chemically different systems: ionic semiconductor (ZnO), metallic Ni and covalently bonded C. All four systems have been systematically explored on the energy landscape defined using interatomic potentials. The new developments allowed us to successfully locate the double funnels of LJ, find new local and global minima for ZnO clusters, extensively explore the Ni and C (the buckminsterfullerene, or buckyball) potential energy surfaces.

What is the best or most relevant global minimum for nanoclusters? Predicting, comparing and recycling cluster structures with WASP@N

“…Within our database, we implemented the approach rst adopted in the KLMC soware 47 to address the challenge of maintaining the diversity of structures during a genetic algorithm search. The approach relies on the NAUTY soware package (No AUTomorphisms, Yes?)…”

“…A Lamarckian genetic algorithm (GA) approach implemented in the KLMC so-ware package 47 was also used to locate LM on the energy landscape dened by the same set of interatomic potentials (semi-classical level of theory) as those used in the data-mining investigation. We note that the ability of the KLMC GA 47 to locate LM and GM efficiently has been proven for various types of system, and thus it is chosen here as a method for providing reliable data that we can use to assess the results obtained using the data-mining approach.…”

“…We note that the ability of the KLMC GA 47 to locate LM and GM efficiently has been proven for various types of system, and thus it is chosen here as a method for providing reliable data that we can use to assess the results obtained using the data-mining approach. The population of each GA run was set to 200 candidate structures, with the initial random structures generated within a 15Å Â 15Å Â 15Å cubic simulation box.…”

Synthesis Target Structures for Alkaline Earth Oxide Clusters

“…These structures were themselves generated using an EA [2] to search for all low-lying local minima on the energy landscape, defined by IPs originally fitted to reproduce the structure and properties of bulk BaO [22] for each composition (value of n). The lower energy minima structures were then further optimised using DFT.…”

“…Unfortunately, even relatively efficient algorithms for structure prediction of clusters require exploration of large regions of conformational space; in our previous paper [1], we chose to use an evolutionary algorithm (EA) [2] in combination with a set of classical interatomic potentials (IPs) followed by refinement of results with density functional theory (DFT), for (BaO) n clusters with n ≤ 18 and n = 24. For each cluster size, the top 20 structures were uploaded into the freely available HIVE online database [3] of published atomic structures of nanoclusters.…”