A DFT+U study of defect association and oxygen migration in samarium-doped ceria
Bibliographic record
Abstract
A specialized genetic algorithm (GA) is used to search the structural space of samarium-doped ceria (SDC) for the most energetically stable configurations which will predominate in low temperature fuel cells. A systematic investigation of all configurations of 3.2% SDC and a GA investigation of 6.6% SDC are presented for the first time at the DFT+U level of theory. It was found that Sm atoms prefer to occupy the nearest neighbor (NN) position relative to the oxygen vacancy at 3.2%, while at 6.6%, a balance exists between various Sm-vacancy interactions and the vacancies prefer to be separated by ∼6 Å. Also, the migration barriers for oxygen diffusion are calculated amongst the best structures in 3.2% and 6.6% SDC and are found to be comparable to the barriers in Gd-doped ceria at the DFT+U level of theory. While the migration calculations provide insight on the oxygen diffusion mechanism in this material, the favored configurations from our GA enable future research on concentrated SDC and contribute to the atomistic understanding of the influence of dopant-vacancy and vacancy-vacancy interactions on ionic conductivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".