Thermally activated defect tolerance of the oxygen vacancies in CeO2 revealed by machine-learning molecular dynamics

Abstract

Thermal instability and uncontrolled defect dynamics continue to be major obstacles to the reliable functioning of oxide-based energy materials under practical operating conditions. Due to its propensity for reversibly producing oxygen vacancies, cerium dioxide (CeO2) is widely used in energy-related applications; nevertheless, the finite-temperature stability mechanisms leading to vacancy tolerance are still poorly understood. In this work, machine-learning molecular dynamics has been applied to thoroughly examine the dynamical and thermodynamic stability of oxygen vacancies in bulk CeO2 at device-relevant temperatures (300–500 K). By directly linking vacancy formation energetics with finite-temperature lattice dynamics, a quantitative vacancy stability paradigm that encompasses both dynamic resilience and energetic accessibility has been presented. According to our findings, the oxygen vacancies in CeO2 dynamically accommodate lattice distortions and intrinsically modest formation energies, which allow thermal disorders without long-range structural deterioration. Even at high temperatures, the root-mean-square displacement, mean-square displacement, and radial distribution analyses show reduced defect-driven diffusion and maintained crystallinity. We create a hierarchical vacancy stability phase map that outlines a wide defect-tolerant operating window for CeO2 by integrating these descriptors. These findings establish the physical origin of defect tolerance in ceria and provide a material-level framework for the rational design and thermal optimization of defect-engineered oxide energy materials.

Graphical abstract: Thermally activated defect tolerance of the oxygen vacancies in CeO2 revealed by machine-learning molecular dynamics

Article information

Article type
Paper
Submitted
02 Mar 2026
Accepted
11 Jun 2026
First published
26 Jun 2026

Phys. Chem. Chem. Phys., 2026, Advance Article

Thermally activated defect tolerance of the oxygen vacancies in CeO2 revealed by machine-learning molecular dynamics

Z. Ali, K. Ali, H. Anwar and M. Zahid, Phys. Chem. Chem. Phys., 2026, Advance Article , DOI: 10.1039/D6CP00774K

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements