Twisted多层MoTe₂中的分层与能带重排
Layerwise Stratification and Band Reordering in Twisted Multilayer MoTe$_2$
AI总结：
该研究通过物理指导策略揭示多层扭曲MoTe₂中结构和电子分层现象，发现中间扭角下分层最强，导致能带重排和非线性极化。
摘要：
我们提出了一种通用且基于物理的策略，用于生成训练数据，以使机器学习力场在广范围的扭角和堆叠层数的莫尔系统中具有较高的准确性。将此方法应用于多层扭曲MoTe₂（tMoTe₂），我们识别出一种结构和电子分层：两个莫尔界面（MI）层即使在厚多层中仍保留显著的晶格重构，而外层像本征层则表现出快速衰减的畸变。令人惊讶的是，这种分层在超小扭角范围（<~1°）最强，而该范围中平面域的形成已众所周知，但事实上最强的分层出现在中间角度（2-5°）。同时，MI-本征层边界处的层间杂化被强烈抑制，导致电子隔离。在扭曲双层MoTe₂中，这种分层导致前沿价带中同时存在蜂窝状和三角状晶格图案。我们进一步表明，扭角和弱栅极可以产生属于这两种图案的能带位移，产生陈数能带重排和非线性电极化，伴随适度的空穴掺杂。我们的方法允许高效模拟多层莫尔系统，并揭示了在双层系统中不存在的结构-电子分离现象。
英文摘要：
We introduce a generalizable, physics informed strategy for generating training data that enables a machine learning force field accurate over a broad range of twist angles and stacking layer numbers in moire systems. Applying this to multilayer twisted MoTe2 (tMoTe2), we identify a structural and electronic stratification: the two moire interface (MI) layers retain substantial lattice reconstruction even in thick multilayers, while outer bulk like layers show rapidly attenuated distortions.Surprisingly, this stratification becomes strongest not in the ultra-small twist angle regime (<~1°), where in plane domain formation is well known, but rather at intermediate angles (2-5°). Simultaneously, interlayer hybridization across the MI-bulk boundary is strongly suppressed, leading to electronic isolation. In twisted double bilayer MoTe2, this stratification gives rise to coexisting honeycomb and triangular lattice motifs in the frontier valence bands. We further demonstrate that twist angle and weak gating can create energy shift of bands belonging to the two motifs, producing Chern band reordering and nonlinear electric polarization with modest hole doping. Our approach allows efficient simulation of multilayer moire systems and reveals structural-electronic separation phenomena absent in bilayer systems.
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