In this thesis we develop computational techniques for modelling molecular selforganisation.\ud After a short review of the current nanotechnological applications of\ud molecular self-assembly and the main problems encountered in modelling the selforganised\ud behaviour of chemical systems, we introduce a set of methods, from both\ud chemistry and complexity science, for the prediction of self-assembled structures,\ud with particular focus on Monte Carlo (MC) based methods.\ud We apply the MC method to two systems of experimental interest. First we\ud model the silica nanoparticles on the surface of spherical polystyrene latex droplets,\ud synthesised by the S. Bon Group at the University of Warwick, as a set of soft\ud spheres on a spherical surface, to study their packing patterns as a function of\ud the broadening of the nanoparticle size distribution. Then we develop a hexagonal\ud lattice model for the study of the two-dimensional self-organisation of planar\ud molecules capable of complementary interactions, to study their phase diagrams as\ud a function of the strength of their complementary interactions and bonding motif.\ud In both cases, the phases are characterised using a number of order parameters.\ud We show that these simplified models are able to reproduce the experimental observations.\ud We then develop an Agent Based (AB) algorithm, traditionally used for the\ud study of complex systems, for the modelling of molecular self-organisation. In this\ud algorithm, an agent is identified with a stable portion of the system under investigation.\ud The agents can then evolve following a set of rules which include elements\ud of adaptation (new configurations induce new types of moves) and learning (past\ud successful choices are repeated), in order to drive the system towards its lowest\ud energy configuration. We first apply the method to the study of the packing of a\ud set of idealised shapes, then we extend it to the study of a realistic system. The\ud latter is achieved by linking the AB algorithm to an available molecular mechanics\ud code, in order to calculate the interaction energies of atomistic models. In both\ud cases we compare the AB result with that of MC based methods, showing that\ud for all the systems studied, the AB method consistently finds significantly lower\ud energy minima than the MC algorithms in less computing time. Finally, we show\ud how the AB algorithm can be used as a part of the protocol to calculate the phase\ud diagram of a rigid organic molecule (1,4-benzene-dicarboxylic acid or TPA) with\ud less computational effort than standard techniques
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