  Crystal structure prediction has been a subject of topical interest, but
remains a substantial challenge, especially for complex structures as it deals
with the global minimization of the extremely rugged high-dimensional potential
energy surface. In this manuscript, a symmetry-orientated divide-and-conquer
scheme was proposed to construct a symmetry tree graph, where the entire search
space is decomposed into a finite number of symmetry-dependent subspaces. An
artificial intelligence-based symmetry selection strategy was subsequently
devised to select the low-lying subspaces with high symmetries for global
exploration and in-depth exploitation. Our approach can significantly simplify
the problem of crystal structure prediction by avoiding exploration of the most
complex P1 subspace on the entire search space and have the advantage of
preserving the crystal symmetry during structure evolution, making it well
suitable for predicting the complex crystal structures. The effectiveness of
the method has been validated by successful prediction of the candidate
structures of binary Lennard-Jones mixtures and high-pressure phase of ice,
containing more than one hundred atoms in the simulation cell. The work,
therefore, opens up an opportunity towards achieving the long-sought goal for
crystal structure prediction of complex systems.

Materials Science;Computational Physics