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arXiv · 1303.2802

MUSE: Multi-algorithm collaborative crystal structure prediction

Zhong-Li Liu
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Abstract

The algorithm and testing of the Multi-algorithm-collaborative Universal Structure-prediction Environment ({\sc Muse}) are detailed. Presently, in {\sc Muse} I combined the evolutionary, the simulated annealing, and the basin hopping algorithms to realize high-efficiency structure predictions of materials under certain conditions. {\sc Muse} is kept open and other algorithms can be added in future. I introduced two new operators, slip and twist, to increase the diversity of structures. In order to realize the self-adaptive evolution of structures, I also introduced the competition scheme among the ten variation operators, as is proved to further increase the diversity of structures. The symmetry constraints in the first generation, the multi-algorithm collaboration, the ten variation operators, and the self-adaptive scheme are all key to enhancing the performance of {\sc Muse}. To study the search ability of {\sc Muse}, I performed extensive tests on different systems, including the metallic, covalent, and ionic systems. All these present tests show {\sc Muse} has very high efficiency and 100% success rate.

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Zhong-Li Liu. 2014-03-19. MUSE: Multi-algorithm collaborative crystal structure prediction. https://doi.org/10.1016/j.cpc.2014.03.017

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