Predicting Interfacial Thermal Conductance and Thermal Conductivity across Multilayer TiS<sub>2</sub>/MoS<sub>2</sub> van der Waals Heterostructures Using Moment Tensor Potentials
Bibliographic record
Abstract
Thermal conductivity calculations using classical molecular dynamics (MD) simulations are significantly influenced by the selection of accurate interatomic potentials. However, these interatomic potentials are not readily available for several novel two-dimensional (2D) materials and their heterostructures. Here, we propose a machine learning interatomic potential (MLIP) and D3-dispersion correction approach to determine the out-of-plane thermal conductivity and interfacial thermal conductance of multilayer TiS 2 /MoS 2 heterostructures. We employ an MLIP-based moment tensor potential (MTP) to characterize the intralayer interactions within layers, while the interlayer interactions are characterized using the D3-dispersion correction. This approach demonstrates greater accuracy compared with Lennard-Jones-based potentials for interlayer interactions. Furthermore, this work uses a simplified supercell of the TiS 2 /MoS 2 heterostructure and cost-effective ab initio molecular dynamics (AIMD) simulations with a duration of less than 1 ps. Additionally, this work investigates the effects of tuning the ratio of TiS 2 to MoS 2 layers on the interfacial thermal conductance and the overall out-of-plane thermal conductivity of the multilayer heterostructure. This MLIP-based methodology provides a reliable approach for estimating the thermal properties of multilayer heterostructures.
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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 | 0.000 |
| 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 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".