Electronic Descriptors Lower Oxygen-Vacancy Formation-Energy Error Relative to Crystal Structure Alone in Non-Magnetic Oxides
Oxygen vacancies control conduction, catalysis and ion transport in oxides, and an error of 0.3 eV in their neutral formation energy changes their concentration at 1000 K thirty-fold. Graph networks predict this energy from structure alone, but whether electronic descriptors of the pristine host from density-functional theory help is untested at matched training oxides. We compared a structure-only network (S) with one also given 22 such descriptors (D) on 1726 vacancy sites in 818 non-magnetic oxides of the Kumagai database, testing on unseen formulas. S reached 0.297 eV, the published accuracy. With 654 training oxides and equal tuning, D lowered the site error by 0.033 [0.021, 0.045] eV (95% interval), inside a 0.05 eV tolerance (1.8-fold in concentration). Tuned alone in a wider search, D* gained 0.069 [0.060, 0.078] eV, above it, but 0.039 [0.030, 0.048] eV over default-initialised S. The descriptors remove 11 to 23% of the error of S, depending on tuning. Within oxides the gain is concentrated in hosts whose cations have an empty valence s shell: empty-shell post-transition hosts gain 0.075 [0.050, 0.102] eV more than lone-pair hosts. Descriptors predicted from structure never surpass S, so the gain requires computed descriptors; where these are unavailable, S suffices for neutral vacancies.
Authors
- Palaash Gang (ORCID: https://orcid.org/0009-0006-7448-9488)
- Qi Wu (ORCID: https://orcid.org/0009-0006-1874-6478)
- Aiden Lee (ORCID: https://orcid.org/0009-0007-3281-8420)
Institutions
- University of California System (US)
Publication Details
- Journal
- ChemRxiv
- Published
- 2026-10-29
- DOI
- https://doi.org/10.26434/chemrxiv.15011247/v1
- Primary Topic
- Machine Learning in Materials Science
- Type
- preprint
Funders
- National Science Foundation
- University of California, San Diego