Deterministic and Efficient Switching of Sliding Ferroelectrics
Shihan Deng, Hongyu Yu, Junyi Ji, Changsong Xu, Hongjun Xiang

TL;DR
This paper introduces a neural network-based approach to model 2D sliding ferroelectrics, demonstrating high Curie temperatures and a universal method for deterministic polarization switching using inclined electric fields.
Contribution
The study develops a multi-task equivariant neural network for accurate potential prediction in boron nitride bilayers, enabling efficient polarization switching strategies applicable to various 2D ferroelectric materials.
Findings
Achieved a Curie temperature of up to 1500K in simulations.
Discovered that inclined electric fields enable deterministic and lower-threshold polarization switching.
Demonstrated universality of the inclined field strategy for other 2D ferroelectric systems.
Abstract
Recent studies highlight the scientific importance and broad application prospects of two-dimensional (2D) sliding ferroelectrics, which prevalently exhibit vertical polarization with suitable stackings. It is crucial to understand the mechanisms of sliding ferroelectricity and to deterministically and efficiently switch the polarization with optimized electric fields. Here, applying our newly developed DREAM-Allegro multi-task equivariant neural network, which simultaneously predicts interatomic potentials and Born effective charges, we construct a comprehensive potential for boron nitride () bilayer. The molecular dynamics simulations reveal a remarkably high Curie temperature of up to 1500K, facilitated by robust intralayer chemical bonds and delicate interlayer van der Waals(vdW) interactions. More importantly, it is found that, compared to the out-of-plane electric…
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Taxonomy
TopicsAcoustic Wave Resonator Technologies · Modular Robots and Swarm Intelligence · Advanced MEMS and NEMS Technologies
