MUSE: Multi-algorithm collaborative crystal structure prediction

TL;DR

MUSE is a versatile, multi-algorithm framework for crystal structure prediction that combines several algorithms and innovative operators to achieve high efficiency and success rates across various material systems.

Contribution

This work introduces MUSE, a novel multi-algorithm collaborative environment with new operators and adaptive schemes, significantly improving crystal structure prediction performance.

Findings

Achieved 100% success rate in tests

Demonstrated high efficiency across different material types

Enhanced diversity and adaptability in structure prediction

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.