Data-Driven Design of Halide Perovskites for Efficient Green Light-Emitting Diodes via Machine Learning and DFT
Bibliographic record
Abstract
To accelerate the application of inorganic halide perovskite materials in green-light-emitting diodes (LEDs), it is crucial to develop novel perovskite materials with suitable band gaps. However, traditional experimental screening methods and density functional theory (DFT) calculations are time-consuming and costly. Therefore, we propose an innovative screening strategy that combines machine learning with DFT calculations to predict the band gaps of 30 inorganic halide perovskite materials. This study establishes a database based on 1193 inorganic perovskite materials and utilizes five machine learning models: random forest regression (RFR), gradient boosting regression (GBR), support vector regression (SVR), extreme gradient boosting regression (XGBR), and decision tree regression (DT). Using this approach, four promising inorganic perovskite candidates, Cs 2 KTlCl 6, Cs 2 RbTlCl 6, Cs 2 TlBiCl 6, and Cs 2 KInBr 6, were successfully identified. Moreover, detailed DFT calculations were conducted to study the band gaps, density of states, effective mass, and exciton binding energy, thereby identifying the most promising green LED perovskite candidates. Among these, Cs 2 TlBiCl 6 not only possesses an ideal band gap of 2.23 eV for green LEDs but also demonstrates excellent performance in terms of effective mass, exciton binding energy, and stability. By combining machine learning with DFT calculations, we significantly enhanced the efficiency and accuracy of material screening, providing an efficient and innovative solution for the development of green LEDs.
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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 | 0.001 | 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".