Domain and switching dynamics in antiferroelectric PbZrO<sub>3</sub>: 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.

Authors

Institutions

Publication Details

Journal
Materials Genome Engineering Advances
Published
2025-04-24
DOI
https://doi.org/10.1002/mgea.70012
Citations
4
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
2.07
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Yubai Shi, Ri He, Liang Si, Yao Wu et al.
4 citations
Materials Genome Engineering Advances
Machine Learning in Materials Science
2.07
article

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

Yubai Shi, Ri He, Liang Si, Yao Wu, Ruoyu Wang, Shi Liu
article en
4 citations

Abstract

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.

Materials Genome Engineering AdvancesVol. 3(2)
University of Science and Technology of China (CN), Chinese Academy of Sciences (CN), Suzhou University of Science and Technology (CN), Westlake University (CN), Northwest University (CN), University of Chinese Academy of Sciences (CN), Ningbo Institute of Industrial Technology (CN), Huazhong University of Science and Technology (CN)
Openalex Percentile: Top 14%
Machine Learning in Materials Science
2.07
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.