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arXiv 2008.11213cond-mat.mes-hallphysics.comp-phquant-ph
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基于统计和机器学习方法的工程拓扑阶段

Engineering Topological Phases Guided by Statistical and Machine Learning Methods

Thomas Mertz, Roser Valentí

摘要:

寻找具有拓扑性质的材料是一项持续的努力。在这篇文章中,我们提出了一个系统的统计方法支持的机器学习技术,能够构建拓扑模型的一个通用的晶格没有先验知识的相图。通过从随机分布中采样紧约束参数向量,我们获得了用相应的拓扑指数标记的数据集。然后分析该标记的数据以提取与拓扑分类最相关的那些参数并找到它们最可能的值。我们发现,参数的边缘分布已经定义了一个拓扑模型。附加信息隐藏在参数之间的相关性中。在这里,我们提出了作为概念的证明的预测的Halfton模型的原型拓扑绝缘体的蜂窝晶格在Altland-Zirnbauer(AZ)类A。该算法可直接应用于任何其他AZ类或格,并可推广到相互作用系统。

英文摘要:

The search for materials with topological properties is an ongoing effort. In this article we propose a systematic statistical method supported by machine learning techniques that is capable of constructing topological models for a generic lattice without prior knowledge of the phase diagram. By sampling tight-binding parameter vectors from a random distribution we obtain data sets that we label with the corresponding topological index. This labeled data is then analyzed to extract those parameters most relevant for the topological classification and to find their most likely values. We find that the marginal distributions of the parameters already define a topological model. Additional information is hidden in correlations between parameters. Here we present as a proof of concept the prediction of the Haldane model as the prototypical topological insulator for the honeycomb lattice in Altland-Zirnbauer (AZ) class A. The algorithm is straightforwardly applicable to any other AZ class or lattice and could be generalized to interacting systems.

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。

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