Advancing Thermal Management with Machine-Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides
Bibliographic record
Abstract
To achieve seamless heat dissipation, it is essential to use materials with high thermal conductivity to improve thermal management. In this study, we have utilized ab initio and machine learning techniques to systematically explore the lattice thermal conductivity of BN and other group 13 nitride based bulk and bilayer materials. By employing data-driven training of potentials of different atomic configurations at different time steps obtained from the AIMD data, we have demonstrated the comparability of the results obtained from the machine-learned potentials compared to the density functional theory (DFT) based calculations on thermal conductivity. Furthermore, we examined the significance of four phonon interactions in group 13 nitrides by comparing the calculated values with the available experimental values. Notably, bilayer AlN exhibits a high thermal conductivity (881 W m –1 K –1 ) due to its stronger covalent bonding, which contradicts the trend observed from B to In. Our study highlights that the machine learning potential-based approach can provide more accurate results than DFT, paving the way for future robust investigations of materials using high-throughput screening techniques.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".