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Minimization of small silicon clusters using the space-fixed modified genetic algorithm method

✍ Scribed by J.A. Niesse; Howard R. Mayne

Publisher: Elsevier Science
Year: 1996
Tongue: English
Weight: 479 KB
Volume: 261
Category: Article
ISSN: 0009-2614
DOI: 10.1016/0009-2614(96)01000-7

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✦ Synopsis

A space-fixed modified genetic algorithm (SFMGA) approach was used to obtain global minima for the silicon clusters (Si) n using a semiempirical potential. One modification to the usual GA is the use of gradient-driven minimization of each geometry. A novel feature of the method is the use of space-fixed atomic coordinates. The advantages of these coordinates are discussed. The method found all minima previously reported for n = 3-10, and improved on those for n = 5-8. That the commonly-used method of seeding a cluster can actually detract from minimization efficiency, is also shown.

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