人工智能用于高精度发现拓扑绝缘体：以合金辉碲铋矿为例
Artificial Intelligence for High-Throughput Discovery of Topological Insulators: the Example of Alloyed Tetradymites
摘要：
在使用高通量经验描述符或基于熵的指标预测新拓扑材料方面已经取得了重大进展。到目前为止，这些方法已被应用于现有数据库中的材料，并严格限于具有明确定义的对称性的系统，留下了更大的材料空间未被探索。使用tetradymites作为一个典型的类的例子，我们发现了一种新的二维描述符，通过应用人工智能（AI）为基础的方法，快速和可靠的识别的拓扑特征的急剧扩大的范围内的材料，而无需事先确定其特定的对称性和详细的能带结构。通过利用这个描述符，只包含原子序数和电负性的组成物种，我们已经很容易扫描了大量的合金的辉碲铋矿家庭。引人注目的是，其中近一半被确定为拓扑绝缘体，揭示了拓扑材料世界的更大领域。目前的工作也证明了这种基于人工智能的方法在现代材料发现中越来越重要的作用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
