Efficient Crystal Structure Prediction Based on the Symmetry Principle

Crystal structure prediction (CSP) is an evolving field aimed at discerning crystal structures with minimal prior information. Despite the success of various CSP algorithms, their practical applicability remains circumscribed, particularly for large and complex systems. Here, to address this challenge, we show an evolutionary structure generator within the MAGUS (Machine Learning and Graph Theory Assisted Universal Structure Searcher) framework, inspired by the symmetry principle. This generator extracts both global and local features of explored crystal structures using group and graph theory. By integrating an on-the-fly space group miner and fragment reorganizer, augmented by symmetry-kept mutation, our approach generates higher-quality initial structures, reducing the computational costs of CSP tasks. Benchmarking tests show up to fourfold performance improvements. The method also proves valid in complex phosphorus allotrope systems. Furthermore, we apply our approach to the diamond-silicon (111)-(7 × 7) surface system, identifying up to 42 metastable structures within an 18 meV Å energy range, demonstrating the efficacy of our approach in navigating challenging search spaces.