A genetic algorithm approach to probing the evolution of self-organized nanostructured systems.
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.
Siepmann
Peter
P
School of Computer Science & IT, The University of Nottingham, Nottingham NG8 1BB, UK.
Martin
Christopher P
CP
Vancea
Ioan
I
Moriarty
Philip J
PJ
Krasnogor
Natalio
N
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*
D003102:Colloids