Abstract
A real-space genetic algorithm for the optimization of defect structures embedded in bulk crystalline materials is developed. The purpose of this method is to enable automated prediction of stable structures for a range of embedded clusters, including radiation induced defect clusters, dopant clusters, and small precipitates. The method is applied to the prediction of small interstitial clusters in cubic SiC, BCC Fe, and BCC Fe–Cr random alloys for radiation damage applications. The performance of the method is analyzed and compared to alternative techniques such as basin hopping. The technique is able to reproduce small-size defects that had been previously identified as stable or metastable structures as well as predict new mid-size defects. The structure optimization program (StructOpt) developed in this study is available under open source licensing as part of the MAterials Simulation Toolkit (MAST) and can be obtained from https://pypi.python.org/pypi/MAST.
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