Graph deep learning accelerated efficient crystal structure search and feature extraction

Structural search and feature extraction are a central subject in modern materials design,the efficiency of which is currently limited,but can be potentially boosted by machine learning(ML).Here,we develop an ML-based prediction-analysis framework,which includes a symmetry-based combinatorial crystal optimization program(SCCOP)and a feature additive attribution model,to significantly reduce computational costs and to extract property-related structural features.Our method is highly accurate and predictive,and extracts structural features from desired structures to guide materials design.We first test SCCOP on 35 typical compounds to demonstrate its generality.As a case study,we apply our approach to a two-dimensional B-C-N system,which identifies 28 previously undiscovered stable structures out of 82 compositions;our analysis further establishes the structural features that contribute most to energy and bandgap.Compared to conventional approaches,SCCOP is about 10 times faster while maintaining a comparable accuracy.Our framework is generally applicable to all types of systems for precise and efficient structural search,providing insights into the relationship between ML-extracted structural features and physical properties.