Seeing moiré: convolutional network learning applied to twistronics

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 requires 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 bandstructure 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.

11 pages, 12 figures

References in corpus (8)

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Cited by in corpus (3)

• Moiré plane wave expansion model for scanning tunneling microscopy simulations of incommensurate two-dimensional materials
• Review of the tight-binding method applicable to the properties of moiré superlattices
• Moiré spintronics: Emergent phenomena, material realization and machine learning accelerating discovery