Machine-Learning-Based Thermal Conductivity Prediction in Two-Dimensional TiS2/MoS2 Van Der Waals Heterostructures
Bibliographic record
Abstract
Abstract Two-dimensional (2D) materials and heterostructures display unique thermal characteristics compared to their bulk counterparts. However, the accurate estimation of the thermal conductivity of 2D materials, particularly of 2D van der Waals heterostructures, presents significant challenges for both computational and experimental methods. In this study, we propose a computationally efficient approach to investigate the thermal conductivity of 2D TiS2/MoS2 van der Waals heterostructures. Our approach utilizes machine-learning interatomic potentials (MLIPs) to predict the thermal conductivity of the heterostructure. This approach effectively incorporates intralayer interactions by utilizing moment tensor potentials (MTP) trained with computationally inexpensive density functional theory (DFT)-based datasets. These datasets are generated from ab-initio molecular dynamics (AIMD) trajectories over less than 1 ps, while the interlayer van der Waals interactions are calibrated using the D3-dispersion correction method. By explicitly incorporating the missing dispersion contribution into the MTP, this method provides greater accuracy in predicting interlayer interactions than the widely applied Lennard-Jones (LJ) potential. Finally, molecular dynamics (MD) simulations are conducted to determine the thermal conductivity of the TiS2/MoS2 heterostructures using the derived potential parameters. This study enhances our understanding of thermal transport in van der Waals (vdW) heterostructures, leveraging MLIPs to explore new nanostructured materials with superior thermal conductivity.
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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) | 0.001 | 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".