Domain and switching dynamics in antiferroelectric PbZrO₃: Machine learning molecular dynamics simulation

Abstract

Antiferroelectric (AFE) materials have received great attention because of their potential applications in the energy sector. Nevertheless, the properties of AFE materials have not been explored for a long time, especially the atomic-scale understanding of AFE domain walls. Here, using first-principles-based machine learning potentials, we identify the atomic structures, energies, and dynamic properties of the domain walls for AFE lead zirconate. It is found that the domain wall can reduce the critical antiferroelectric-ferroelectric transition field. During the electric field-driven polarization switching process, the domain wall is immobile. Importantly, we observe that a distinct domain structure spontaneously forms in bulk lead zirconate upon annealing at 300 K. The domain structure exhibits an alternating array of clockwise–anticlockwise vortexes along radial with continuous polarization rotation. This anomalous AFE vortex is derived from the energy degeneracy in four possible orientations of the polarization order, which can enhance the dielectric response in the terahertz. The current results give an implication for the emergence of AFE vortex in AFE materials as well as ferroelectric materials.

Keywords

antiferroelectricity / domain wall / machine learning / molecular dynamics

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.