On the Atomistic Interactions That Direct Ion Conductivity and Defect Segregation in the Bulk and Surface of Samarium-Doped Ceria: A Genetic Algorithm Study
Bibliographic record
Abstract
We study the (111) surface of 10.3%, 14.3%, and 18.5% samarium-doped ceria (SDC) using a genetic algorithm (GA) to search for the most energetically stable configurations. In all cases, both Sm ions and oxygen vacancies segregate to the surface, which is similar to experimental findings for 5.3% SDC. (1) Importantly, at the optimal doping level of SDC (∼11%), where conductivity is maximal, defect segregation is limited such that vacancies remain 6 Å apart and pairs of vacancies do not form. At higher concentrations, pairs of vacancies are present, which likely contributes to the observed decrease in ionic conductivity. We also investigate the low-energy bulk structure of SDC from 10.3% to 18.5%, at the DFT+U level of theory, which has not been previously reported. The DFT+U energetics allow us to gain further insight on the fundamental interactions that influence ionic conductivity and defect segregation, as well as to confirm the insight reported from classical simulations. (2) The low-energy configurations found by our GA search enable future studies of SDC at experimentally relevant concentrations and identify the important interactions at the bulk and surface of the fuel cell electrolyte.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".