Prediction of Thermal Conductivity of Two-Dimensional Superlattices of Graphene and Boron Nitride by Equilibrium Molecular Dynamics
Bibliographic record
Abstract
Graphene is a promising material to design faster microprocessors given its exceptionally high thermal conductivity. However, due to its null electronic band gap, graphene must be combined with high-electric conductivity materials such as boron nitride to manufacture competitive alternatives to traditional semiconductors. Thus, the goal of this study is to determine the thermal conductivities and heat capacities of two-dimensional superlattices of graphene and boron nitride as a function of the secondary periodicity and interface orientation. We apply the Green-Kubo method to atomic trajectories calculated with Molecular Dynamics to determine the thermal conductivities of superlattices with periodicities between one and five in the armchair and zigzag orientations at 300 K. Results show that conductivities increase with decreasing periodicity, in good agreement with predictions made with Harmonic Lattice Dynamics. Thermal conductivities parallel to the interface are significantly higher than those perpendicular to the interface in the armchair configuration and vice versa in the zigzag orientation. Moreover, the heat capacities are practically independent of the periodicity and interface orientation up to 1500 K.
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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 | 0.000 | 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".