Machine learning-enabled band gap prediction of monolayer transition metal chalcogenide alloys
Bibliographic record
Abstract
Monolayer transition metal dichalcogenide (TMD) alloys with tunable direct band gaps have promising applications in nanoelectronics and optoelectronics. The composition-dependent band gaps of ternary, quaternary and quinary monolayer TMD alloys have been systematically studied combining density functional theory and machine learning models in the present study. The excellent agreement between the DFT-calculated band gaps and the ML-predicted values for the training, validation and test datasets demonstrates the accuracy of our machine learning based on a neural network model. It is found that the band gap bowing parameter is closely related to the difference between the band gaps of the endpoint material compositions of the monolayer TMD alloy and increases with increasing band gap difference. The band gap bowing effects of monolayer TMD alloys obtained by mixing different transition metals are attributed to the conduction band minimum positions, while those of monolayer TMD alloys obtained by mixing different chalcogen atoms are dominated by the valence band maximum positions. This study shows that monolayer TMD alloys with tunable direct band gaps can provide new opportunities for band gap engineering, as well as electronic and optoelectronic applications.
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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) | 0.001 | 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 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".