Charge-transfer effect in hard x-ray<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>1</mml:mn><mml:mi>s</mml:mi></mml:mrow></mml:math>and<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mn>2</mml:mn><mml:mi>p</mml:mi></mml:mrow></mml:math>photoemission spectra:<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>LDA</mml:mi><mml:mo>+</mml:mo><mml:mi>DMFT</mml:mi></mml:math>and cluster-model analysis
Bibliographic record
Abstract
We study $1s$ and $2p$ hard x-ray photoemission spectra (XPS) in a series of late transition metal oxides: ${\mathrm{Fe}}_{2}{\mathrm{O}}_{3}$ ($3{d}^{5}$), ${\mathrm{FeTiO}}_{3}$ ($3{d}^{6}$), CoO ($3{d}^{7}$), and NiO ($3{d}^{8}$). The experimental spectra are analyzed with two theoretical approaches: ${\mathrm{MO}}_{6}$ cluster model and local density approximation (LDA) $+$ dynamical mean-field theory (DMFT). Owing to the absence of the core-valence multiplets and spin-orbit coupling, $1s$ XPS is found to be a sensitive probe of chemical bonding and nonlocal charge-transfer screening, providing complementary information to $2p$ XPS. The $1s$ XPS spectra are used to assess the accuracy of the ab initio $\mathrm{LDA}+\mathrm{DMFT}$ approach, developed recently to study the material-specific charge-transfer effects in core-level XPS.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".