Domain-Wall-Mediated Ultralow-Barrier Sliding and Pinning in Ferroelectric Moir\'e Superlattices Revealed by Machine Learning
Jia-Wen Li, Sheng Meng, Xinghua Shi, Jin Zhang, and Wei-Hai Fang

TL;DR
This study uses machine learning to uncover ultralow-barrier, domain-wall-mediated sliding dynamics in ferroelectric MoS2 moiré superlattices, revealing thermally driven motion and vacancy-induced pinning.
Contribution
It demonstrates that sliding in ferroelectric moiré superlattices is governed by domain walls with ultralow barriers, not rigid layer translation, advancing understanding of microscopic dynamics.
Findings
Thermally driven interlayer sliding occurs at ~1 m/s at 300 K.
Sliding proceeds via an almost barrierless pathway along domain walls.
Sulfur vacancies induce a transition from sliding to localized oscillations.
Abstract
Sliding ferroelectrics built from stacked nonpolar monolayers enable out-of-plane polarization and unconventional switching via interlayer sliding, yet the microscopic sliding dynamics remain unclear. Using machine-learning molecular dynamics, we reveal spontaneous thermally driven interlayer sliding in ferroelectric MoS2 moir\'e superlattices, with relative velocities on the order of 1 m/s at 300 K. Instead of rigid translation of the entire bilayer, the motion appears as a global drift of the moir\'e pattern. Such thermally driven sliding is inconsistent with the meV/atom-scale rigid-sliding barrier. In contrast, when constrained relaxation is allowed, the sliding proceeds along an almost barrierless pathway that directly reproduces the global drift of the moir\'e pattern. Furthermore, sulfur vacancies trigger a sliding-to-pinning transition, with about 0.1% S vacancies already…
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