| Summary: | In this dissertation, algorithms for creating estimated potentials for metals and modeling of nano composites are developed. The efficacy of the algorithms for estimated potentials were examined. The algorithm was found to allow molecular dynamic and Monte Carlo modeling to be included in the potential building process. Additionally, the spline based equations caused issues with the elastic constants and Young’s modulus due to extra local minima. Two algorithms were developed for improved modeling of nano composites: one was a random number generation algorithm for initializing polymer, second was a bonding algorithm for controlling bonds between polymer and nano particle. Both algorithms were effective in their tasks. Additionally, the algorithms for improved nano composite modeling were used for preliminary material design of PMMA metal oxide nano composite systems. The results from the molecular dynamic simulations show the bonding between polymer matrix and nanoparticle has a large effect on the Young’s modulus and if this bonding could be controlled, the tensile properties of PMMA-metal oxide nano composites could be tailored to the applications’ requirements. The simulations also showed bonding had caused changes in the density of the material which than effected the energy on the polymer chain and the Young’s modulus. A model was than developed showing the relationship between density and the chain energy, and density and the Young’s modulus. This model can be used for a better understanding and further improvement of PMMA-metal oxide nano composites.
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