Interplay between metavalent bonds and dopant orbitals enables the design of SnTe thermoelectrics
Bibliographic record
Abstract
Abstract Engineering the electronic band structures upon doping is crucial to improve the thermoelectric performance of materials. Understanding how dopants influence the electronic states near the Fermi level is thus a prerequisite to precisely tune band structures. Here, we demonstrate that the Sn-s states in SnTe contribute to the density of states at the top of the valence band. This is a consequence of the half-filled p-p σ-bond (metavalent bonding) and its resulting symmetry of the orbital phases at the valence band maximum (L point of the Brillouin zone). This insight provides a recipe for identifying superior dopants. The overlap between the dopant s- and the Te p-state is maximized, if the spatial overlap of both orbitals is maximized and their energetic difference is minimized. This simple design rule has enabled us to screen out Al as a very efficient dopant to enhance the local density of states for SnTe. In conjunction with doping Sb to tune the carrier concentration and alloying with AgBiTe 2 to promote band convergence, as well as introducing dislocations to impede phonon propagation, a record-high average ZT of 1.15 between 300 and 873 K and a large ZT of 0.36 at 300 K is achieved in Sn 0.8 Al 0.08 Sb 0.15 Te-4%AgBiTe 2 .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".