MUSE: Multi-algorithm collaborative crystal structure prediction

The algorithm and testing of the Multi-algorithm-collaborative Universal Structure-prediction Environment (Muse) are detailed. Presently, in MUSE I combined the evolutionary, the simulated annealing, and the basin hopping algorithms to realize high-efficiency structure predictions of materials under certain conditions. 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 MUSE. To study the search ability of MUSE, I performed extensive tests on different systems, including the metallic, covalent, and ionic systems. All these present tests show that MUSE has very high efficiency and 100% success rate. (C) 2014 Elsevier B.V. All rights reserved.