Disruption of superlattice phonons by interfacial mixing
Bibliographic record
Abstract
Molecular dynamics simulations and lattice dynamics calculations are used to study the vibrational modes and thermal transport in Lennard-Jones superlattices with perfect and mixed interfaces. The secondary periodicity of the superlattices leads to a vibrational spectrum (i.e., dispersion relation) that is distinct from the bulk spectra of the constituent materials. The mode eigenvectors of the perfect superlattices are found to be good representations of the majority of the modes in the mixed superlattices for up to 20% interfacial mixing, allowing for extraction of phonon frequencies and lifetimes. Using the frequencies and lifetimes, the in-plane and cross-plane thermal conductivities are predicted using a solution of the Boltzmann transport equation (BTE), with agreement found with predictions from the Green-Kubo method for the perfect superlattices. For the mixed superlattices, the Green-Kubo and BTE predictions agree for the cross-plane direction, where thermal conductivity is dominated by low-frequency modes whose eigenvectors are not affected by the mixing. For the in-plane direction, mid-frequency modes that contribute to thermal transport are disrupted by the mixing, leading to an underprediction of thermal conductivity by the BTE. The results highlight the importance of using a dispersion relation that includes the secondary periodicity when predicting phonon properties in perfect superlattices and emphasize the challenges of estimating the effects of disorder on phonon properties.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".