Stable Polar Oxynitrides through Epitaxial Strain
Li Zhu, Hiroyuki Takenaka, R. E. Cohen

TL;DR
This study uses computational methods to predict stable and metastable polar oxynitride perovskites under high pressure and epitaxial strain, highlighting their potential for synthesis and applications in ferroelectric materials.
Contribution
It introduces a comprehensive computational approach to identify stable polar oxynitrides under various pressure and strain conditions, expanding the understanding of their stability and synthesis possibilities.
Findings
Several thermodynamically stable polar oxynitride perovskites predicted under high pressure.
Ferroelectric polar phases can be stabilized and synthesized at high pressure on suitable substrates.
YSiO₂N remains metastable up to 600 K under compressive strain, indicating thermal stability.
Abstract
We investigate energetically favorable structures of ABON oxynitrides as functions of pressure and strain via swarm-intelligence-based structure prediction methods, DFT lattice dynamics and first-principles molecular dynamics. We predict several thermodynamically stable polar oxynitride perovskites under high pressures. In addition, we find that ferroelectric polar phases of perovskite-structured oxynitrides can be thermodynamically stable and synthesized at high pressure on appropriate substrates. The dynamical stability of the ferroelectric oxynitrides under epitaxial strain at ambient pressure also imply the possibility to synthesize them using pulsed laser deposition or other atomic layer deposition methods. Our results have broad implications for further exploration of other oxynitride materials as well. We performed first-principles molecular dynamics and find that the polar…
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.
Taxonomy
TopicsInorganic Chemistry and Materials · Machine Learning in Materials Science · Electronic and Structural Properties of Oxides
