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dc.contributor.authorMolignini, P
dc.contributor.authorZegarra, A
dc.contributor.authorvan Nieuwenburg, E
dc.contributor.authorChitra, R
dc.contributor.authorChen, W
dc.date.accessioned2021-11-17T00:30:27Z
dc.date.available2021-11-17T00:30:27Z
dc.date.issued2021
dc.identifier.issn2542-4653
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/330686
dc.description.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>
dc.publisherStichting SciPost
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleA supervised learning algorithm for interacting topological insulators based on local curvature
dc.typeArticle
prism.issueIdentifier3
prism.publicationDate2021
prism.publicationNameSciPost Physics
prism.volume11
dc.identifier.doi10.17863/CAM.78131
dcterms.dateAccepted2021-09-08
rioxxterms.versionofrecord10.21468/SciPostPhys.11.3.073
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2021-09-01
dc.contributor.orcidMolignini, Paolo [0000-0001-6294-3416]
dc.identifier.eissn2542-4653
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/P009565/1)
cam.issuedOnline2021-09-28


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International