Machine Learning Insights into Band Alignments of van der Waals Heterostructures
Bibliographic record
Abstract
The integration of two-dimensional (2D) materials into van der Waals heterostructures (vdWHs) enables the stacking of atomically thin layers through weak vdW interactions, offering tunable properties for next-generation optoelectronic and catalytic applications. In this study, we systematically predicted the band alignment types of over 32,000 vdWHs, constructed by pairing any two of the 256 semiconductor monolayers from the 2D Semiconductor Computational Database (2DSdb) [ J. Phys. Chem. Lett. 2022 13, 11581], based on Anderson’s rule. Nearly 100 features were extracted from the physical properties of these vdWHs to establish a descriptor database for the triclassification of vdWHs using machine learning models, including extreme gradient boosting, random forest, and gradient boosting classifier, achieving accuracy rates between 85 and 87%. SHapley Additive exPlanations (SHAP) analysis identified electronegativity, oxygen fraction, valence electrons in the p -orbital, and lattice constants as the most influential features. The resulting database is expected to provide valuable guidance for experimentalists in designing nanodevices and photocatalysts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 teacher head, 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".