A supervised learning algorithm for interacting topological insulators based on local curvature
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Authors
Molignini, P
Zegarra, A
van Nieuwenburg, E
Chitra, R
Chen, W
Publication Date
2021Journal Title
SciPost Physics
ISSN
2542-4653
Publisher
Stichting SciPost
Volume
11
Issue
3
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Molignini, P., Zegarra, A., van Nieuwenburg, E., Chitra, R., & Chen, W. (2021). A supervised learning algorithm for interacting topological insulators based on local curvature. SciPost Physics, 11 (3) https://doi.org/10.21468/SciPostPhys.11.3.073
Abstract
<jats:p>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.
We 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.</jats:p>
Sponsorship
Engineering and Physical Sciences Research Council (EP/P009565/1)
Identifiers
External DOI: https://doi.org/10.21468/SciPostPhys.11.3.073
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330686
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