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  • Open Access

Seeing moiré: Convolutional network learning applied to twistronics

Diyi Liu1, Mitchell Luskin1, and Stephen Carr2

  • 1School of Mathematics, University of Minnesota-Twin Cities, Minneapolis, Minnesota 55455, USA
  • 2Department of Physics and Brown Theoretical Physics Center, Brown University, Providence, Rhode Island 02912-1843, USA

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Phys. Rev. Research 4, 043224 – Published 30 December, 2022

DOI: https://doi.org/10.1103/PhysRevResearch.4.043224

Abstract

Moiré patterns made of two-dimensional (2D) materials represent highly tunable electronic Hamiltonians, allowing a wide range of quantum phases to emerge in a single material. Current modeling techniques for moiré electrons require significant technical work specific to each material, impeding large-scale searches for useful moiré materials. In order to address this difficulty, we have developed a material-agnostic machine learning approach and test it here on prototypical one-dimensional (1D) moiré tight-binding models. We utilize the stacking dependence of the local density of states (SD-LDOS) to convert information about electronic band structure into physically relevant images. We then train a neural network that successfully predicts moiré electronic structure from the easily computed SD-LDOS of aligned bilayers. This network can satisfactorily predict moiré electronic structures, even for materials that are not included in its training data.

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Outline

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Phys. Rev. Research 4, 043224– Published 30 December, 2022
  • Received 22 July 2022
  • Revised 16 August 2022
  • Accepted 22 November 2022
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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