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 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 | 0.000 |
| 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 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".