Matty, M, Zhang, Y, Papić, Z et al. (1 more author) (2019) Multifaceted machine learning of competing orders in disordered interacting systems. Physical Review B, 100 (15). 155141. ISSN 2469-9950
Abstract
While the nonperturbative interaction effects in the fractional quantum Hall regime can be readily simulated through exact diagonalization, it has been challenging to establish a suitable diagnostic that can label different phases in the presence of competing interactions and disorder. Here we introduce a multifaceted framework using a simple artificial neural network (ANN) to detect defining features of a fractional quantum Hall state, a charge-density-wave state and a localized state using the entanglement spectra and charge density as independent input. We consider the competing effects of a perturbing interaction (l=1 pseudopotential ΔV1), a disorder potential W, and the Coulomb interaction to the system at filling fraction ν=1/3. Our phase diagram benchmarks well against previous estimates of the phase boundary along the axes of our phase diagram where other measures exist. Moreover, exploring the entire two-dimensional phase diagram, we establish the robustness of the fractional quantum Hall state and map out the charge-density-wave microemulsion phase wherein droplets of the charge-density-wave region appear before the charge density wave is completely disordered. Hence we establish that the ANN can access and learn the defining traits of topological as well as broken symmetry phases using multifaceted inputs of entanglement spectra and charge density.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2019 American Physical Society. This is an author produced version of a paper accepted for publication in Physical Review B. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Physics and Astronomy (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/R020612/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 23 Sep 2019 15:07 |
Last Modified: | 30 Jun 2020 14:50 |
Status: | Published |
Publisher: | American Physical Society |
Identification Number: | 10.1103/PhysRevB.100.155141 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:151194 |