An evolutionary algorithm for the surface structure problem

Martinez Mateo, Jesus and López Fagúndez, Mª Francisca and Martín Gago, José Angel and Martín Ayuso, Vicente (2009). An evolutionary algorithm for the surface structure problem. In: "10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops", June 10-12, 2009, Salamanca. ISBN 978-3-642-02480-1. pp. 280-283.

Description

Title: An evolutionary algorithm for the surface structure problem
Author/s:
  • Martinez Mateo, Jesus
  • López Fagúndez, Mª Francisca
  • Martín Gago, José Angel
  • Martín Ayuso, Vicente
Item Type: Presentation at Congress or Conference (Article)
Event Title: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops
Event Dates: June 10-12, 2009
Event Location: Salamanca
Title of Book: Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living
Date: 2009
ISBN: 978-3-642-02480-1
Volume: 5518
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (267kB) | Preview

Abstract

Many macroscopic properties: hardness, corrosion, catalytic activity, etc. are directly related to the surface structure, that is, to the position and chemical identity of the outermost atoms of the material. Current experimental techniques for its determination produce a “signature” from which the structure must be inferred by solving an inverse problem: a solution is proposed, its corresponding signature computed and then compared to the experiment. This is a challenging optimization problem where the search space and the number of local minima grows exponentially with the number of atoms, hence its solution cannot be achieved for arbitrarily large structures. Nowadays, it is solved by using a mixture of human knowledge and local search techniques: an expert proposes a solution that is refined using a local minimizer. If the outcome does not fit the experiment, a new solution must be proposed again. Solving a small surface can take from days to weeks of this trial and error method. Here we describe our ongoing work in its solution. We use an hybrid algorithm that mixes evolutionary techniques with trusted region methods and reuses knowledge gained during the execution to avoid repeated search of structures. Its parallelization produces good results even when not requiring the gathering of the full population, hence it can be used in loosely coupled environments such as grids. With this algorithm, the solution of test cases that previously took weeks of expert time can be automatically solved in a day or two of uniprocessor time.

More information

Item ID: 19131
DC Identifier: http://oa.upm.es/19131/
OAI Identifier: oai:oa.upm.es:19131
Official URL: http://link.springer.com/chapter/10.1007%2F978-3-642-02481-8_40
Deposited by: Memoria Investigacion
Deposited on: 16 Sep 2013 15:37
Last Modified: 21 Apr 2016 17:22
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM