arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2212.04519cond-mat.str-el
收藏

用统计方法检测方-八边形晶格中的拓扑相

Detecting topological phases in the square-octagon lattice with statistical methods

Paul Wunderlich, Francesco Ferrari, Roser Valentí

AI总结:

本研究引入特征工程方法补充现有统计学习技术,识别与非平庸拓扑态相关的哈密顿量参数多项式组合,并用于研究费米能级处于高阶范霍夫奇点的方-八边形晶格的拓扑相。

摘要:

目前,学界正深入讨论存在于kagome(笼目)、方-八边形网络等阿基米德晶格上的电子系统,因其或可实现拓扑绝缘相。但由于可能的哈密顿量参数空间庞大,以无偏方式精准定位最受关注的电子拓扑态并非易事。针对这一问题,近期开发的一种统计学习方法提供了解决思路[T. Mertz和R. Valentí,《物理评论研究》第3卷,013132(2021)],该方法基于对大量标注有拓扑指数的随机紧束缚哈密顿量数据集的分析。在本研究中,我们引入特征工程方法对该技术进行补充,该方法有助于识别与非平庸拓扑态相关的哈密顿量参数的多项式组合。作为演示,我们采用该方法研究了方-八边形晶格上可能出现的拓扑相,重点关注系统费米能级处于高阶范霍夫奇点的情况,这与近期针对kagome晶格在范霍夫填充下拓扑相的研究相类似。

英文摘要:

Electronic systems living on Archimedean lattices such as kagome and square-octagon networks are presently being intensively discussed for the possible realization of topological insulating phases. Coining the most interesting electronic topological states in an unbiased way is however not straightforward due to the large parameter space of possible Hamiltonians. A possible approach to tackle this problem is provided by a recently developed statistical learning method [T. Mertz and R. Valentí, Phys. Rev. Research 3, 013132 (2021)], based on the analysis of a large data sets of randomized tight-binding Hamiltonians labeled with a topological index. In this work, we complement this technique by introducing a feature engineering approach which helps identifying polynomial combinations of Hamiltonian parameters that are associated with non-trivial topological states. As a showcase, we employ this method to investigate the possible topological phases that can manifest on the square-octagon lattice, focusing on the case in which the Fermi level of the system lies at a high-order van Hove singularity, in analogy to recent studies of topological phases on the kagome lattice at the van Hove filling.

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。

↑