  Molecular self-assembly will not become a routine method for building
nanomaterials unless our ability to predict the outcome of this process is
dramatically improved. Even then, reliable strategies for realizing molecular
assemblies with novel properties are required for building nanomaterials for
specific device applications. In this paper, I simulate the self-assembly of
metal phthalocyanine derivatives adsorbed to gold(111) surfaces using a
detailed statistical mechanical model and a new computational method based upon
genetic algorithms and Markov chain Monte Carlo (MCMC). This method yields
predictions that are not only are superior to those of ordinary MCMC but also
show good agreement with experimental results. Crucially, it is predicted that
molecular assemblies displaying locally disordered magnetic moments - and
having potential applications as non-Gaussian noise sources for device
applications - can be realized by the simple strategy of introducing asymmetry
into the phthalocyanine ligands.
