title: A genetic algorithm approach to probing the evolution of self-organized nanostructured systems.
abstract: We present a new methodology, based on a combination of genetic algorithms and image morphometry, for matching the outcome of a Monte Carlo simulation to experimental observations of a far-from-equilibrium nanosystem. The Monte Carlo model used simulates a colloidal solution of nanoparticles drying on a solid substrate and has previously been shown to produce patterns very similar to those observed experimentally. Our approach enables the broad parameter space associated with simulated nanoparticle self-organization to be searched effectively for a given experimental target morphology.
authors.affiliation: School of Computer Science & IT, The University of Nottingham, Nottingham NG8 1BB, UK.
authors.forename: Peter
authors.initials: P
authors.lastname: Siepmann
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Martin
authors.forename: Ioan
authors.initials: I
authors.lastname: Vancea
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Moriarty
authors.forename: Natalio
authors.initials: N
authors.lastname: Krasnogor
mesh_terms: D000465:Algorithms; D003102:Colloids / Q000737:chemistry*; D003198:Computer Simulation; D005823:Genetics / Q000706:statistics & numerical data*; D008956:Models, Chemical; D009010:Monte Carlo Method; D053758:Nanoparticles / Q000737:chemistry*
chemical_list: D003102:Colloids