Cation Vacancies with and without O–O Dimerization: Identifying Reconstructed Defect Configurations in Metal Oxides
Abstract Point defects in solid-state materials are conventionally modeled as lattice-derived defects, generated by adding or removing atoms from symmetric bulk lattice structures, followed by local geometry optimization. This approach often yields symmetric, weakly relaxed local minima that remain close to the parent lattice. Here, we show that this assumption can fail qualitatively for vacancies in many metal oxides. Rather than retaining a lattice-derived geometry with localized holes on oxygen sites, cation vacancies can drive extensive reconstruction, producing defect configurations in which the O–O bonds and O–O–O chains become the preferred charge-compensation motifs. We combined the minima hopping (MH) structure prediction algorithm with machine-learned interatomic potentials (MLIPs), hybrid QM/MM embedded-cluster calculations, and periodic density functional theory (DFT) calculations to investigate cation vacancies across a broad series of metal oxides. Our results show a clear oxidation-state-dependent trend. In the charge-neutral state, cation vacancies in oxides with formal oxidation states of +4 or higher favor reconstructed defect configurations containing O–O bonds or O–O–O chains with bond lengths of approximately 1.4 Å, lowering the defect formation energy by up to several electronvolts relative to the conventional structure. By contrast, in oxides with lower cation oxidation states, such reconstructions are generally unfavorable at charge neutrality but can become energetically more favorable at positive charge states. These findings define oxygen-bond formation as a general reconstruction pathway for cation vacancies in metal oxides and show that global structure searches are essential for predictive defect modeling beyond conventional lattice-derived local minima.
Authors
- Jamal Abdul Nasir (ORCID: https://orcid.org/0000-0002-0474-1610)
- Alexey A. Sokol (ORCID: https://orcid.org/0000-0003-0178-1147)
- Stefan Goedecker (ORCID: https://orcid.org/0000-0002-3580-4186)
- Taifeng Liu (ORCID: https://orcid.org/0000-0002-6869-7022)
- Thomas W. Keal (ORCID: https://orcid.org/0000-0001-8747-3975)
- Jingcheng Guan (ORCID: https://orcid.org/0000-0002-9109-8984)
- C. Richard A. Catlow (ORCID: https://orcid.org/0000-0002-1341-1541)
- Xingfan Zhang (ORCID: https://orcid.org/0000-0003-0852-4194)
- John Buckeridge (ORCID: https://orcid.org/0000-0002-2537-5082)
- You Lü (ORCID: https://orcid.org/0000-0002-7524-4179)
- Liam Morgan (ORCID: https://orcid.org/0000-0001-9314-3497)
- Scott M. Woodley (ORCID: https://orcid.org/0000-0003-3418-9043)
- Qing Hou
- Thomas Durrant
Institutions
- University of Shanghai for Science and Technology (CN)
- Henan University (CN)
- University of Basel (CH)
- Daresbury Laboratory (GB)
- London South Bank University (GB)
- University College London (GB)
- Cardiff University (GB)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2026-10-07
- DOI
- https://doi.org/10.1021/jacs.6c15378
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00