Peter Siepmann

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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(More)
Natural world processes have, for nearing fifty years, been the inspiration for a number of artificial intelligence methods [Hir00, Koz92]. In this project, the model of Darwinian evolution will be used to develop a system that, through combining distributed processing technologies with evolutionary population-and search-based algorithms, can be used to(More)
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