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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".