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Structural search and feature extraction are a central subject in modernmaterials design, the efficiency of which is currently limited, but can bepotentially boosted by machine learning (ML). Here, we develop an ML-basedprediction-analysis framework, which includes a symmetry-based combinatorialcrystal optimization program (SCCOP) and a feature additive attribution model,to significantly reduce computational costs and to extract property-relatedstructural features. Our method is highly accurate and predictive, and extractsstructural features from desired structures to guide materials design. As acase study, we apply our new approach to a two-dimensional B-C-N system, whichidentifies 28 previously undiscovered stable structures out of 82 compositions;our analysis further establishes the structural features that contribute mostto energy and bandgap. Compared to conventional approaches, SCCOP is about 10times faster while maintaining a comparable accuracy. Our new framework isgenerally applicable to all types of systems for precise and efficientstructural search, providing new insights into the relationship betweenML-extracted structural features and physical properties.

Graph deep learning accelerated efficient crystal structure search and feature extraction