Layerwise Stratification and Band Reordering in Twisted Multilayer MoTe<sub>2</sub>

ORAL

Abstract

We present a physics-guided framework for constructing transferable machine-learning force fields capable of capturing moiré lattice energetics across a wide range of structural configurations. Applied to multilayer twisted transition-metal dichalcogenides (TMDs), this approach uncovers an unexpected structural and electronic stratification that persists deep into the bulk, leading to emergent electronic textures and topology-sensitive band rearrangements under modest external tuning. These findings highlight a new regime of structural-electronic decoupling beyond bilayer moiré systems.

*This work is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, under Contract No. DE-SC0025327. The development of machine-learning enabled methods and advanced codes was supported by the Computational Materials Sciences Program funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences, and Engineering Division, PNNL FWP 83557. Y.F was supported by the U.S.-Japan University Partnership for Workforce Advancement and Research and Development in Semiconductors (UPWARDS) . This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award BES-ERCAP0032546 and BES-ERCAP0033256.This work was also facilitated through the use of advanced computational, storage, and networking infrastructure provided by the Hyak supercomputer system and funded by the University of Washington Molecular Engineering Materials Center at the University of Washington (DMR-2308979).

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Presenters

  • Yueyao Fan

    • University of Washington

Authors

  • Yueyao Fan

    • University of Washington

  • Xiao-Wei Zhang

    • University of Washington

  • Chong Wang

    • University of Washington

  • Xiaoyu Liu

    • University of Washington

  • Yusen Ye

    • University of Washington

  • Kai-Jie Yang

    • University of Washington
    • The univeristy of Washington - Seattle

  • Di Xiao

    • University of Washington

  • Ting Cao

    • University of Washington