Understanding disorder-induced zero-bias anomalies in systems with short-range interactions: An atomic-limit perspective
Bibliographic record
Abstract
Motivated by the novel electronic behaviors seen in transition-metal oxides, we look for physical insight into disordered, strongly correlated systems by exploring the atomic limit. In recent work, the atomic limit has provided a useful reference point in systems with strong local interactions. For comparison with experiments, the exploration of nonlocal interactions is of interest. In the atomic limit, both the case of on-site interactions alone and the case of infinite-range $(1/r)$ interactions are well understood; however, not so the intervening possibilities. Here we study the atomic limit of the extended Anderson-Hubbard model using classical Monte Carlo to calculate the single-particle density of states. We show that the combination of nearest-neighbor interactions and site disorder produces a zero-bias anomaly caused by residual charge ordering, and the addition of on-site interactions has a nonmonotonic effect on the depth of this zero-bias anomaly. A key conclusion is that the form of the density of states in this classical system strongly resembles density of states results obtained for the full extended Anderson-Hubbard model when $U<4V$.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".