Finite-temperature properties of antiferroelectric perovskite $\rm PbZrO_3$ from deep learning interatomic potential
Huazhang Zhang, Hao-Cheng Thong, Louis Bastogne, Churen Gui, Xu He, Philippe Ghosez

TL;DR
This paper develops a deep learning interatomic potential for PbZrO3, enabling large-scale simulations of its finite-temperature phases, phase transitions, and electric-field responses, addressing challenges of traditional first-principles methods.
Contribution
The authors introduce a deep learning model that accurately reproduces multiple phases of PbZrO3 and enables detailed finite-temperature and electric-field studies beyond previous computational limitations.
Findings
Successfully reproduces known and new phases of PbZrO3
Reconciles experimental and theoretical phase stability
Simulates electric-field induced phase transitions
Abstract
The prototypical antiferroelectric perovskite (PZO) has garnered considerable attentions in recent years due to its significance in technological applications and fundamental research. Many unresolved issues in PZO are associated with large length- and time-scales, as well as finite temperatures, presenting significant challenges for first-principles density functional theory studies. Here, we introduce a deep learning interatomic potential of PZO, enabling investigation of finite-temperature properties through large-scale atomistic simulations. Trained using an elaborately designed dataset, the model successfully reproduces a large number of phases, in particular, the recently discovered 80-atom antiferroelectric phase and ferrielectric phase, providing precise predictions for their structural and dynamical properties. Using this model, we investigated phase…
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Taxonomy
TopicsMultiferroics and related materials · Ferroelectric and Piezoelectric Materials · Perovskite Materials and Applications
