ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Machine learning non-Hermitian topological phases

Narayan, B and Narayan, A (2021) Machine learning non-Hermitian topological phases. In: Physical Review B, 103 (3).

phy_rev_B_103-3_2021.pdf - Published Version

Download (780kB) | Preview
Official URL: https://doi.org/10.1103/PhysRevB.103.035413


Non-Hermitian topological phases have gained widespread interest due to their unconventional properties, which have no Hermitian counterparts. In this work, we propose to use machine learning to identify and predict non-Hermitian topological phases, based on their winding number. We consider two examples - non-Hermitian Su-Schrieffer-Heeger model and its generalized version in one dimension and non-Hermitian nodal line semimetal in three dimensions - to demonstrate the use of neural networks to accurately characterize the topological phases. We show that for the one-dimensional model, a fully connected neural network gives an accuracy greater than 99.9% and is robust to the introduction of disorder. For the three-dimensional model, we find that a convolutional neural network accurately predicts the different topological phases.

Item Type: Journal Article
Publication: Physical Review B
Publisher: American Physical Society
Additional Information: The copyright for this article belongs to the Author.
Keywords: Convolutional neural networks; Machine learning, Fully connected neural network; One dimension; One-dimensional model; Su Schrieffer Heeger model; Three dimensions; Three-dimensional model; Topological phasis; Winding number, Topology
Department/Centre: Division of Chemical Sciences > Solid State & Structural Chemistry Unit
Date Deposited: 17 Aug 2023 09:24
Last Modified: 17 Aug 2023 09:24
URI: https://eprints.iisc.ac.in/id/eprint/82806

Actions (login required)

View Item View Item