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Significant advances have been made in predicting new topological materialsusing high-throughput empirical descriptors or symmetry-based indicators. Todate, these approaches have been applied to materials in existing databases,and are severely limited to systems with well-defined symmetries, leaving amuch larger materials space unexplored. Using tetradymites as a prototypicalclass of examples, we uncover a novel two-dimensional descriptor by applying anartificial intelligence (AI) based approach for fast and reliableidentification of the topological characters of a drastically expanded range ofmaterials, without prior determination of their specific symmetries anddetailed band structures. By leveraging this descriptor that contains only theatomic number and electronegativity of the constituent species, we have readilyscanned a huge number of alloys in the tetradymite family. Strikingly, nearlyhalf of which are identified to be topological insulators, revealing a muchlarger territory of the topological materials world. The present work alsoattests the increasingly important role of such AI-based approaches in modernmaterials discovery.

Artificial Intelligence for High-Throughput Discovery of Topological Insulators: the Example of Alloyed Tetradymites