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Comparison of Taboo Search Methods for Atomic Cluster Global Optimization with a Basin-Hopping Algorithm.

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Summary
This summary is machine-generated.

The basin-hopping algorithm (BHA) was enhanced with taboo search to improve atomic cluster energy surface exploration. Rejecting moves into recently visited regions proved more efficient for multifunnel systems.

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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Chemical Physics

Background:

  • Exploring atomic cluster potential energy surfaces is crucial for understanding material properties.
  • The basin-hopping algorithm (BHA) is a common method for this exploration.
  • Standard BHA can be inefficient in multifunnel systems, where the search may revisit configurations.

Purpose of the Study:

  • To enhance the basin-hopping algorithm (BHA) for more efficient exploration of atomic cluster potential energy surfaces.
  • To investigate the impact of incorporating taboo search methods to prevent revisiting configurations.
  • To compare the performance of two distinct taboo search modes on various potential energy surfaces.

Main Methods:

  • Incorporation of taboo search into the basin-hopping algorithm (BHA).
  • Implementation and testing of two taboo search modes: resetting to random coordinates or rejecting moves into taboo regions.
  • Evaluation on Lennard-Jones potential clusters (LJ38) and a semi-empirical tight binding potential for Au55 clusters.

Main Results:

  • The mode that rejects moves into taboo regions demonstrated improved performance for LJ38 and Au55 clusters compared to the resetting mode.
  • This rejection mode enhances the efficiency of BHA for exploring multifunnel systems.
  • Both taboo search modes showed limited improvement for systems with more than two funnels.

Conclusions:

  • Integrating taboo search, particularly the rejection mode, offers a viable strategy to improve basin-hopping algorithm efficiency for certain multifunnel systems.
  • Further algorithmic development is needed to address challenges in highly complex multifunnel energy landscapes.
  • The findings provide insights into optimizing computational methods for atomic cluster structure prediction.