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Machine learning the microscopic form of nematic order in twisted double-bilayer graphene

Author

Listed:
  • João Augusto Sobral

    (University of Stuttgart
    University of Innsbruck)

  • Stefan Obernauer

    (University of Innsbruck)

  • Simon Turkel

    (Columbia University)

  • Abhay N. Pasupathy

    (Columbia University
    Brookhaven National Laboratory)

  • Mathias S. Scheurer

    (University of Stuttgart
    University of Innsbruck)

Abstract

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. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample. Using electronic nematic order in twisted double-bilayer graphene as an example, we show that incorporating correlations between the local density of states at different energies allows convolutional neural networks not only to learn the microscopic nematic order parameter, but also to distinguish it from heterostrain. These results demonstrate that neural networks are a powerful method for investigating the microscopic details of correlated phenomena in moiré systems and beyond.

Suggested Citation

  • João Augusto Sobral & Stefan Obernauer & Simon Turkel & Abhay N. Pasupathy & Mathias S. Scheurer, 2023. "Machine learning the microscopic form of nematic order in twisted double-bilayer graphene," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-40684-1
    DOI: 10.1038/s41467-023-40684-1
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    Cited by:

    1. Bowen Hou & Jinyuan Wu & Diana Y. Qiu, 2024. "Unsupervised representation learning of Kohn–Sham states and consequences for downstream predictions of many-body effects," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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