From 1095ace0945c9262495f2539695fbbd5649a4f51 Mon Sep 17 00:00:00 2001 From: YB_HU <138468445+RussellHu41@users.noreply.github.com> Date: Thu, 16 Jan 2025 16:30:00 +0800 Subject: [PATCH] Create ABACUS_15_01_2025.md --- source/_posts/ABACUS_15_01_2025.md | 70 ++++++++++++++++++++++++++++++ 1 file changed, 70 insertions(+) create mode 100644 source/_posts/ABACUS_15_01_2025.md diff --git a/source/_posts/ABACUS_15_01_2025.md b/source/_posts/ABACUS_15_01_2025.md new file mode 100644 index 0000000..c54a8a9 --- /dev/null +++ b/source/_posts/ABACUS_15_01_2025.md @@ -0,0 +1,70 @@ +--- +title: "What Can ABACUS Do Too? | ABACUS + DeepH + KPROJ: Unraveling the Electronic Structure of MoSe₂/WSe₂ Moiré Lattices" +date: 2025-01-15 +categories: +- ABACUS +--- + +Recently, the research group led by Mingxing Chen from the College of Physics and Electronic Science at Hunan Normal University investigated the evolution of flat bands in the MoSe₂/WSe₂ moiré lattices of transition metal dichalcogenides using deep learning potential and Hamiltonian models. In this study, they employed the domestic first-principles software ABACUS, which is based on the linear combination of atomic orbitals (LCAO) basis set, to prepare machine learning datasets. Subsequently, they used DeepH, based on E3 equivariant graph neural networks, to train machine learning models for predicting the moiré lattice Hamiltonian. Diagonalizing the machine learning Hamiltonian yielded the electronic structure of the moiré system. Thereafter, they utilized the self-developed band unfolding program KPROJ to project the wavefunctions of the moiré supercell onto the k-points of the primitive cell and obtained the unfolded band structure, which was used to study the influence of the twist angle on the band structure of transition metal dichalcogenides. This work revealed that the flat bands originating from the valence band edge can stem from the Γ and K valleys. When the H-stacked MoSe₂/WSe₂ moiré lattice has a twist angle of 3.89˚ and spin-orbit coupling (SOC) is not considered, a flat band with a bandwidth of approximately 5 meV appears below the valence band edge at the K point. Subsequently, as the twist angle decreases, this flat band rapidly shifts upward and becomes approximately 20 meV higher than the valence band at the K point. When the twist angle further decreases to 1.7˚, multiple flat bands emerge. The research found that spin-orbit coupling leads to a large spin splitting, comparable to that in the untwisted system (approximately 0.45 eV), and is almost independent of twisting and stacking. Therefore, in the presence of spin-orbit coupling, the flat band in the K valley remains at the top of the valence band. The band unfolding results indicate that the flat bands formed by the Γ valley and the K valley exhibit distinct responses to the twist angle. The Γ valley flat band is highly sensitive to the interlayer coupling and thus rapidly shifts upward as the twist angle decreases. Conversely, the K valley flat band has a weaker dependence on the interlayer coupling and is primarily affected by structural reconstruction. Hence, a relatively small angle (2.13˚) is required to generate the K valley flat band. As the twist angle decreases, this flat band transforms from a honeycomb lattice to a triangular lattice. The relevant research, titled "Evolution of flat bands in MoSe₂/WSe₂ moiré lattices: A study combining machine learning and band unfolding methods", was published in Physical Review B [Phys. Rev. B 110, 235410 (2024)]. Doctoral students Shengguo Yang and Jiaxin Chen are the first and second authors respectively, and Mingxing Chen is the corresponding author. + + + +## Research Background + +Moiré lattices have become ideal quantum simulation platforms for exploring new physics due to the fact that the electronic flat bands within them can induce numerous novel physical phenomena and effects and are modulated by the twist angle. However, the large size of such systems poses a significant challenge to existing first-principles methods for calculating their electronic structures, making it difficult to understand the evolution of electronic flat bands and related electronic properties. Although first-principles calculations have been conducted on the twisted bilayer transition metal dichalcogenide (tbTMD) systems with twist angles as small as 1.54˚ [1], the first-principles calculations are extremely time-consuming because this type of lattice usually contains a large number of atoms (8322 atoms). Recently, deep learning methods such as deep potential molecular dynamics (DeePMD) [2,3,4] and deep learning DFT Hamiltonian (DeepH) [5,6] have demonstrated that machine learning methods can efficiently and accurately simulate the structures and energy bands of large-scale systems. Applying these methods to twisted systems will contribute to understanding the influence of twisting on the electronic properties of two-dimensional materials. Nevertheless, due to the use of large supercells in such calculations, band folding occurs in the band structure, which may obscure some essential characteristics of the flat bands. Especially for tbTMD, the Γ and K valley flat bands may be mixed in the band structure calculated from the supercell, thus making it difficult to understand the origin and evolution of the flat bands. + +In addition, recent scanning tunneling microscope experiments have shown that the H-stacked MoSe₂/WSe₂ moiré system exhibits a deep moiré potential exceeding 300 meV in the valence band [7], and the moiré potential shows a monotonic behavior with the change of the moiré stripe period. As its size increases from 6 nm (~3.15˚) to 17 nm (~1.08˚), new flat band signals appear at the HMX sites. However, the valley origin remains to be explored. + +## Research Methods + +In this work, a high-quality DP model trained for MoSe₂/WSe₂ was used to perform structural relaxation on the rigid moiré lattice. Subsequently, ABACUS was employed to generate high-precision datasets required by DeepH and conduct graph neural network training. The obtained high-quality DeepH model was used for band structure calculations. Finally, the KPROJ program was used to obtain the unfolded band structure. The calculation flow chart is shown in Figure 1. + +