Domain-Wall-Mediated Ultralow-Barrier Sliding and Pinning in Ferroelectric Moiré Superlattices Revealed by Machine Learning

Using machine-learning molecular dynamics, we reveal spontaneous, thermally driven interlayer sliding in ferroelectric MoS₂ moiré 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 moiré pattern.

Such thermally driven sliding is inconsistent with a 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 moiré pattern. Furthermore, sulfur vacancies trigger a sliding-to-pinning transition, with ∼0.1% S vacancies already sufficient to convert the long-range sliding into localized oscillations.