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

TL;DR

This paper introduces an AI-driven method to rapidly identify topological insulators within a vast materials space, bypassing traditional symmetry-based constraints, and uncovers a significant number of new topological materials.

Contribution

The authors develop a novel two-dimensional AI descriptor based on atomic number and electronegativity, enabling efficient discovery of topological insulators in alloyed tetradymites without detailed band structure analysis.

Findings

Nearly 50% of scanned alloys are topological insulators.

The AI descriptor effectively predicts topological properties across a large materials set.

The approach expands the known territory of topological materials.

Abstract

Significant advances have been made in predicting new topological materials using high-throughput empirical descriptors or symmetry-based indicators. To date, these approaches have been applied to materials in existing databases, and are severely limited to systems with well-defined symmetries, leaving a much larger materials space unexplored. Using tetradymites as a prototypical class of examples, we uncover a novel two-dimensional descriptor by applying an artificial intelligence (AI) based approach for fast and reliable identification of the topological characters of a drastically expanded range of materials, without prior determination of their specific symmetries and detailed band structures. By leveraging this descriptor that contains only the atomic number and electronegativity of the constituent species, we have readily scanned a huge number of alloys in the tetradymite family. Strikingly, nearly half of which are identified to be topological insulators, revealing a much larger territory of the topological materials world. The present work also attests the increasingly important role of such AI-based approaches in modern materials discovery.