Seeing moiré: Convolutional network learning applied to twistronics
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2022-12-01
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| Series: | Physical Review Research |
| Online Access: | http://doi.org/10.1103/PhysRevResearch.4.043224 |
| _version_ | 1797210575332179968 |
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| author | Diyi Liu Mitchell Luskin Stephen Carr |
| author_facet | Diyi Liu Mitchell Luskin Stephen Carr |
| author_sort | Diyi Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-24T10:12:46Z |
| format | Article |
| id | doaj.art-5c2c60b992364bdabd8fdd16a95939f3 |
| institution | Directory Open Access Journal |
| issn | 2643-1564 |
| language | English |
| last_indexed | 2024-04-24T10:12:46Z |
| publishDate | 2022-12-01 |
| publisher | American Physical Society |
| record_format | Article |
| series | Physical Review Research |
| spelling | doaj.art-5c2c60b992364bdabd8fdd16a95939f32024-04-12T17:27:22ZengAmerican Physical SocietyPhysical Review Research2643-15642022-12-014404322410.1103/PhysRevResearch.4.043224Seeing moiré: Convolutional network learning applied to twistronicsDiyi LiuMitchell LuskinStephen CarrMoiré 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.http://doi.org/10.1103/PhysRevResearch.4.043224 |
| spellingShingle | Diyi Liu Mitchell Luskin Stephen Carr Seeing moiré: Convolutional network learning applied to twistronics Physical Review Research |
| title | Seeing moiré: Convolutional network learning applied to twistronics |
| title_full | Seeing moiré: Convolutional network learning applied to twistronics |
| title_fullStr | Seeing moiré: Convolutional network learning applied to twistronics |
| title_full_unstemmed | Seeing moiré: Convolutional network learning applied to twistronics |
| title_short | Seeing moiré: Convolutional network learning applied to twistronics |
| title_sort | seeing moire convolutional network learning applied to twistronics |
| url | http://doi.org/10.1103/PhysRevResearch.4.043224 |
| work_keys_str_mv | AT diyiliu seeingmoireconvolutionalnetworklearningappliedtotwistronics AT mitchellluskin seeingmoireconvolutionalnetworklearningappliedtotwistronics AT stephencarr seeingmoireconvolutionalnetworklearningappliedtotwistronics |