Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures
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Bibliographic record
Abstract
Predicting the thermal conductivity of two-dimensional (2D) heterostructures is challenging and cannot be adequately resolved using conventional computational approaches. To address this challenge, we propose a new and efficient approach that combines first-principles density functional theory (DFT) calculations with a machine-learning interatomic potential (MLIP) methodology to determine the thermal conductivity of a novel 2D van der Waals TiS2/MoS2 heterostructure. We leverage the proposed approach to estimate the thermal conductivities of TiS2/MoS2 heterostructures as well as bilayer-TiS2 and bilayer-MoS2. A unique aspect of this approach is the combined implementation of the moment tensor potential for short-range (intralayer) interactions and the D3-dispersion correction scheme for long-range (interlayer) van der Waals interactions. This approach employs relatively inexpensive computational DFT-based datasets generated from ab initio molecular dynamics simulations to accurately describe the interatomic interactions in the bilayers. The thermal conductivities of the bilayers exhibit the following trend: bilayer-TiS2 > bilayer-MoS2 > the TiS2/MoS2 heterostructure. In addition, this work makes the case that the 2D bilayers exhibit considerably higher thermal conductivities than bulk graphite, a common battery anode material, indicating the potential to utilize 2D heterostructures in thermal management applications and energy storage devices. Furthermore, the MLIP-based methodology provides a reliable approach for estimating the thermal conductivity of bilayers and heterostructures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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 it