First-principles analysis of intravalley and intervalley electron-phonon scattering in thermoelectric materials
Bibliographic record
Abstract
Intervalley collisions, which scatter electrons from one valley or band to another, can be detrimental to thermoelectric performance in materials with multiple valleys/bands. In this study, density functional theory is used to investigate the electron-phonon scattering characteristics of three lead chalcogenides (PbS, PbSe, PbTe) and three half-Heuslers (ScNiBi, ScPdSb, ZrNiSn), which all possess multiple equivalent conduction valleys, in order to characterize and analyze their intravalley/intervalley components. To elucidate what controls the degree of intravalley and intervalley transitions, the scattering rates are decomposed into the product of the phase space (a measure of how much scattering is possible) and the average electron-phonon coupling. To help guide the search for improved thermoelectric and high-conductivity materials, simple and approximate approaches are demonstrated that can be adopted to identify materials with reduced intervalley scattering, which circumvent the need for computationally demanding electron-phonon scattering calculations. In addition, the benefits of selecting materials with large-energy zone-edge phonons are explored in the limit $\ensuremath{\hbar}\ensuremath{\omega}$ $\ensuremath{\gg}$ ${k}_{B}T$, and found to potentially suppress intervalley processes by up to an order of magnitude, leading to a 70% and 100% increase in conductivity and power factor, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".