Temperature dependent ferroelectricity in strained KTaO3 with machine learned force field
Yu Zhu, Luigi Ranalli, Taikang Chen, Wei Ren, and Cesare Franchini

TL;DR
This study uses machine learning-enhanced simulations to explore how strain induces ferroelectricity in KTaO3, revealing strain's critical role in stabilizing ferroelectric phases up to room temperature.
Contribution
It introduces a combined density functional theory and machine learned force field approach to accurately study strain-induced ferroelectricity in KTaO3, including anharmonic effects.
Findings
Strain induces ferroelectric polarization in KTaO3.
Higher-order anharmonic effects are significant in the phase transition.
Ferroelectricity can be stabilized up to 300 K with appropriate strain.
Abstract
Ferroelectric materials are a class of dielectrics that exhibit spontaneous polarization which can be reversed under an external electric field. The emergence of ferroelectric order in incipient ferroelectrics is a topic of considerable interest from both fundamental and applied perspectives. Among the various strategies explored, strain engineering has been proven to be a powerful method for tuning ferroelectric polarization in materials. In the case of KTaO3, first principles calculations have suggested that strain can drive a ferroelectric phase transition. In this study, we investigate the impact of in-plane uniaxial and biaxial strain, ranging from 0% to 1%, on pristine KTaO3 to explore its potential for ferroelectricity induction via inversion symmetry breaking. By integrating density functional theory calculations with the stochastic self-consistent harmonic approximation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
