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.

arXiv comment11 pages, 12 figures

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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