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arXiv 2104.11237cond-mat.str-elcond-mat.dis-nnquant-ph
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基于局部曲率的相互作用拓扑绝缘体有监督学习算法

A supervised learning algorithm for interacting topological insulators based on local curvature

Paolo Molignini, Antonio Zegarra, Evert van Nieuwenburg, R. Chitra, Wei Chen

摘要:

固态系统中的拓扑序通常由在整个布里渊区上的适当曲率函数的积分来计算。在拓扑相变的单粒子光谱间隙关闭,曲率函数发散,并改变在某些高对称点的布里渊区的符号。这些通用属性建议引入监督机器学习方案,该方案仅使用高对称点处的曲率函数作为输入数据。我们将此方案应用于各种相互作用的拓扑绝缘体在不同的维度和对称类,并证明了一个人工神经网络训练的非相互作用的数据可以准确地预测所有的拓扑相位的相互作用的情况下,很少的数值工作。有趣的是,该方法揭示了一个无处不在的相互作用引起的拓扑量子多临界性的例子研究。

英文摘要:

Topological order in solid state systems is often calculated from the integration of an appropriate curvature function over the entire Brillouin zone. At topological phase transitions where the single particle spectral gap closes, the curvature function diverges and changes sign at certain high symmetry points in the Brillouin zone. These generic properties suggest the introduction of a supervised machine learning scheme that uses only the curvature function at the high symmetry points as input data. We apply this scheme to a variety of interacting topological insulators in different dimensions and symmetry classes, and demonstrate that an artificial neural network trained with the noninteracting data can accurately predict all topological phases in the interacting cases with very little numerical effort. Intriguingly, the method uncovers a ubiquitous interaction-induced topological quantum multicriticality in the examples studied.

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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