An evolutionary algorithm for the surface structure problem

Martinez Mateo, Jesus; López Fagúndez, Mª Francisca; Martín Gago, José Angel y Martín Ayuso, Vicente (2009). An evolutionary algorithm for the surface structure problem. En: "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.

Descripción

Título: An evolutionary algorithm for the surface structure problem
Autor/es:
  • Martinez Mateo, Jesus
  • López Fagúndez, Mª Francisca
  • Martín Gago, José Angel
  • Martín Ayuso, Vicente
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 10th International Work-Conference on Artificial Neural Networks, IWANN 2009 Workshops
Fechas del Evento: June 10-12, 2009
Lugar del Evento: Salamanca
Título del Libro: Distributed computing, artificial intelligence, bioinformatics, soft computing, and ambient assisted living
Fecha: 2009
ISBN: 978-3-642-02480-1
Volumen: 5518
Materias:
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 19131
Identificador DC: http://oa.upm.es/19131/
Identificador OAI: oai:oa.upm.es:19131
URL Oficial: http://link.springer.com/chapter/10.1007%2F978-3-642-02481-8_40
Depositado por: Memoria Investigacion
Depositado el: 16 Sep 2013 15:37
Ultima Modificación: 21 Abr 2016 17:22
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