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- W4313304068 abstract "Moir'e 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 moir'e electrons requires significant technical work specific to each material, impeding large-scale searches for useful moir'e 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) moir'e 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 moir'e electronic structure from the easily computed SD-LDOS of aligned bilayers. This network can satisfactorily predict moir'e electronic structures, even for materials that are not included in its training data." @default.
- W4313304068 created "2023-01-06" @default.
- W4313304068 creator A5036018970 @default.
- W4313304068 creator A5069117153 @default.
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- W4313304068 date "2022-12-30" @default.
- W4313304068 modified "2023-10-14" @default.
- W4313304068 title "Seeing moiré: Convolutional network learning applied to twistronics" @default.
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- W4313304068 doi "https://doi.org/10.1103/physrevresearch.4.043224" @default.
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