The global optimization of nanoparticles, such as pure or bimetallic metal clusters, has become a very important and sophisticated research field in modern nanoscience. The possibility of using more rigorous quantum chemical first principle methods during the global optimization has been facilitated by the development of more powerful computer hardware as well as more efficient algorithms. In this review, recent advances in first principle global optimization methods are described, with the main focus on genetic algorithms coupled with density functional theory for optimizing sub-nanometre metal clusters and nanoalloys.

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