Twisting phonons in complex crystals with quasi-one-dimensional substructures
Bibliographic record
Abstract
A variety of crystals contain quasi-one-dimensional substructures, which yield distinctive electronic, spintronic, optical and thermoelectric properties. There is a lack of understanding of the lattice dynamics that influences the properties of such complex crystals. Here we employ inelastic neutron scatting measurements and density functional theory calculations to show that numerous low-energy optical vibrational modes exist in higher manganese silicides, an example of such crystals. These optical modes, including unusually low-frequency twisting motions of the Si ladders inside the Mn chimneys, provide a large phase space for scattering acoustic phonons. A hybrid phonon and diffuson model is proposed to explain the low and anisotropic thermal conductivity of higher manganese silicides and to evaluate nanostructuring as an approach to further suppress the thermal conductivity and enhance the thermoelectric energy conversion efficiency. This discovery offers new insights into the structure-property relationships of a broad class of materials with quasi-one-dimensional substructures for various applications. Quasi-one-dimensional substructures have distinctive properties, but the lattice dynamics are poorly understood. Here, Chen et al.use inelastic neutron scattering and density functional theory to discover that numerous low-energy optical vibrational modes including a twisting polarization are present in higher manganese silicides.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".