Disorder induced phase transition in magnetic higher-order topological insulator: A machine learning study

doi:10.1088/1674-1056/ab4582

Abstract Previous studies presented the phase diagram induced by the disorder existing separately either in the higher-order topological states or in the topological trivial states, respectively. However, the influence of disorder on the system with the coexistence of the higher-order topological states and other traditional topological states has not been investigated. In this paper, we investigate the disorder induced phase transition in the magnetic higher-order topological insulator. By using the convolutional neural network and non-commutative geometry methods, two independent phase diagrams are calculated. With the comparison between these two diagrams, a topological transition from the normal insulator to the Chern insulator is confirmed. Furthermore, the network based on eigenstate wavefunction studies also presents a transition between the higher-order topological insulator and the Chern insulator.

Keywords: disorder effects convolution neural network higher-order topological states

Received: 10 June 2019
Revised: 12 September 2019
Accepted manuscript online:

PACS:	05.70.Fh	(Phase transitions: general studies)
	72.80.Ng	(Disordered solids)
	03.65.Vf	(Phases: geometric; dynamic or topological)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11822407) and the Priority Academic Program Development of Jiangsu Higher Education Institutions, China.

Corresponding Authors: Zhiqiang Zhang
E-mail: zqzhang2018@stu.suda.edu.cn

Cite this article:

Zixian Su(苏子贤), Yanzhuo Kang(康艳卓), Bofeng Zhang(张博峰), Zhiqiang Zhang(张智强), Hua Jiang(江华) Disorder induced phase transition in magnetic higher-order topological insulator: A machine learning study 2019 Chin. Phys. B 28 117301

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