Phonon Transport in Disordered Two-Dimensional Graphene
Bibliographic record
Abstract
We employ molecular dynamics (MD) simulations, equipped with a machine-learned interatomic potential, to examine the impact of structural disorder on phonon transport in two-dimensional graphene. By generating amorphous structures through Monte Carlo methods, we systematically investigate how defects modify thermal conductivity and vibrational properties. Our findings demonstrate that a minimal defect concentration of 0.2% significantly degrades thermal conductivity: the out-of-plane component declines by over an order of magnitude, while the in-plane component decreases by half. Spectral analyses reveal that low-frequency flexural acoustic (ZA) modes, which predominantly govern heat transport in pristine graphene, are substantially suppressed in defective samples. In defective graphene, the mean free paths of ZA phonons are reduced by approximately half an order of magnitude compared to pristine graphene, with this reduction exceeding that of in-plane phonon modes; however, the decrease is not preferentially biased toward low frequencies as initially hypothesized. Furthermore, only minor localization effects are observed at low frequencies (PPR ≈ 0.9). These observations suggest a more complex scenario, implying that defects may preferentially disrupt normal scattering processes at low frequencies, potentially necessitating a deeper examination of hydrodynamic effects
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".