Abstract
Large-scale moiré systems are extraordinarily sensitive, with even minute atomic shifts leading to significant changes in electronic structures. Here, we investigate the lattice relaxation effect on moiré band structures in twisted bilayer MoTe2 with two approaches: (a) large-scale plane-wave basis first principle calculation down to 2.88°, (b) transfer learning structure relaxation + local-basis first principles calculation down to 1.1°. We use two types of van der Waals corrections: the D2 method of Grimme and the density-dependent energy correction, and find that the density-dependent energy correction yields a continuous evolution of bandwidth with twist angles. Based on the above results. we develop a complete continuum model with a single set of parameters for a wide range of twist angles, and perform many-body simulations at ν = −1, −2/3, −1/3.
| Original language | English |
|---|---|
| Article number | 262 |
| Journal | Communications Physics |
| Volume | 7 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2024 |
Funding
We are grateful to Tingxin Li, Taige Wang, Trithep Devakul, Fengcheng Wu, and Allan Macdonald for their helpful discussions. Y.Z. thanks Quansheng Wu and Jianpeng Liu for the cross-check on DFT parameters. L.F. and C.F. are partly supported by the Catalyst Fund of the Canadian Institute for Advanced Research. Y.Z. is supported by the start-up fund and the seed grant from the AI Tennessee Initiative at the University of Tennessee Knoxville. The research by J. L. was primarily supported by the National Science Foundation Materials Research Science and Engineering Center program through the UT Knoxville Center for Advanced Materials and Manufacturing (DMR-2309083). The machine learning simulations and large matrix diagonalizations are performed on H100 nodes provided by the AI Tennessee Initiative. We are grateful to Tingxin Li, Taige Wang, Trithep Devakul, Fengcheng Wu, and Allan Macdonald for their helpful discussions. Y.Z. thanks Quansheng Wu and Jianpeng Liu for the cross-check on DFT parameters. L.F. and C.F. are partly supported by the Catalyst Fund of the Canadian Institute for Advanced Research. Y.Z. is supported by the start-up fund and the seed grant from the AI Tennessee Initiative at the University of Tennessee Knoxville. The research by J. L. was primarily supported by the National Science Foundation Materials Research Science and Engineering Center program through the UT Knoxville Center for Advanced Materials and Manufacturing (DMR-2309083). The machine learning simulations and large matrix diagonalizations are performed on H100 nodes provided by the AI Tennessee Initiative.