Machine learning the microscopic form of nematic order in twisted double-bilayer graphene
Machine learning the microscopic form of nematic order in twisted double-bilayer graphene
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moir...
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Machine learning the microscopic form of nematic order in twisted double-bilayer graphene
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TN_cdi_doaj_primary_oai_doaj_org_article_4d332ac27e844334b85bb7d352a654a4
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4d332ac27e844334b85bb7d352a654a4
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-023-40684-1
