The three-dimensional, hydrogen-bonded structure of liquid water plays an important role in many chemical systems of interest in the fields of engineering and the physical sciences. Valuable information on the structure of aqueous solutions and on the role of water in such systems can often be obtained from small to medium-sized clusters; therefore, the development of accurate and efficient global optimization techniques for molecular clusters remains an active area of research. In this study, a hybrid genetic algorithm is being developed for the global optimization of molecular clusters described by the effective fragment potential (EFP). The algorithm, as written, employs the standard genetic operators crossover, mutation and selection in addition to a nonstandard local optimization operator. A previous Monte Carlo/simulated annealing study by this author showed that EFP is as accurate as MP2 (second order perturbation theory) in describing small water clusters. It is expected that the hybrid genetic algorithm will produce similarly accurate results but will outperform Monte Carlo in speed and efficiency. The results of this study will produce a global optimization tool that will contribute to molecular level studies of water and aqueous solutions.

Genetic algorithm for the global optimization of water clusters described by the effective fragment potential

246th National Meeting (2013)

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