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The generalized simulated annealing algorithm in the low energy electron diffraction search problem.

Eduardo Dos R Correia¹, Von B Nascimento, Caio M C de Castilho

¹Grupo de Física de Superfícies e Materiais, Instituto de Física, Universidade Federal da Bahia, Campus Universitário da Federação, 40210-340, Salvador, BA, Brazil.

Journal of Physics. Condensed Matter : an Institute of Physics Journal

|June 22, 2011

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The generalized simulated annealing (GSA) algorithm was applied to the Low-Energy Electron Diffraction (LEED) search problem. Fast simulated annealing (FSA) with a q(V) parameter of 2.0 proved most effective for structural optimization across various parameters.

Area of Science:

Materials Science
Computational Physics
Surface Science

Background:

Low-Energy Electron Diffraction (LEED) is a crucial technique for surface structure determination.
Optimization algorithms are essential for efficiently analyzing complex LEED data.
Generalized Simulated Annealing (GSA) offers a framework for complex optimization tasks.

Purpose of the Study:

To evaluate the effectiveness of the Generalized Simulated Annealing (GSA) algorithm for LEED structural searches.
To investigate the impact of the visiting distribution function (q(V) parameter) on GSA performance.
To compare GSA performance across varying numbers of structural parameters for the CdTe(110) system.

Main Methods:

Application of the GSA algorithm to structural optimization problems.

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Systematic variation of the number of parameters (2 to 10) in structural searches.

Analysis of results using scaling relations and probability of convergence.

Focus on the CdTe(110) system for theory-theory comparison.

Main Results:

The effectiveness of GSA was assessed based on its performance in LEED structural searches.
The visiting distribution function, specifically the q(V) parameter, significantly influences GSA's success.
Fast Simulated Annealing (FSA), a variant of GSA with q(V) = 2.0, demonstrated superior performance.
FSA showed optimal search capabilities across a range of 2 to 10 parameters.

Conclusions:

Fast Simulated Annealing (FSA) with q(V) = 2.0 is identified as the most effective search strategy within the GSA framework for LEED.
The choice of the visiting distribution function is critical for optimizing GSA's efficiency in surface structure determination.
The findings provide valuable insights for refining computational methods in surface science research.