Layerwise stratification and band reordering in twisted multilayer MoTe 2

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Authors: Yueyao Fan, Xiao-Wei Zhang, Yusen Ye, Xiaoyu Liu, Chong Wang, Kaijie Yang, Di Xiao, Ting Cao
Cited by: 1
References: 49

Abstract

We introduce a physics-informed training-data generation strategy that efficiently captures the complete interlayer interactions in multilayer moiré systems, enabling a machine-learning force field transferable across layer numbers and stacking configurations, beyond twist angles. Applying this to multilayer twisted MoTe 2 (tMoTe 2 ), we identify a structural and electronic stratification: The two moiré 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 ultrasmall twist angle regime (≲1 ° ), where in-plane domain formation is well known, but rather at intermediate angles (2 to 5 ° ). Simultaneously, interlayer hybridization across the MI–bulk boundary is strongly suppressed, leading to electronic isolation. In twisted double bilayer MoTe 2 , 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 moiré systems and reveals structural–electronic separation phenomena absent in bilayer systems.

Fields of Study & Subjects

2D Materials and Applications Topological Materials and Phenomena Machine Learning in Materials Science

Publication details

Journal / source
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Volume
123
Issue
23
Pages
e2532550123–e2532550123
Publication date
2 June 2026
Language
EN