Machine Learning-Driven SCAPS Modeling for Optimizing CH<sub>3</sub>NH<sub>3</sub>SnBr<sub>3</sub> Perovskite Solar Cells: Analytical Insights into Materials for Hole Transport and the Active Layer
Bibliographic record
Abstract
This work explores the potential for integrating organic compounds, which serve as absorbers, with HTL to achieve steady, efficient PSCs. This study’s proposed architecture is made up of ETL, HTL, and a CH 3 NH 3 SnBr 3 absorber. The effect of thickness, doping, and defect densities of absorber, HTL, and ETL layers and interface defect densities on a solar device’s output is investigated utilizing the SCAPS-1D model. The FTO/SnS 2 /CH 3 NH 3 SnBr 3 /Ni structure has a V OC of 0.991 V, a J SC of 28.796 mA cm –2, a PCE of 23.88%, and an FF of 83.69%. Concerns about stability, rapid oxidation of Sn 2+ to Sn 4+, and high defect density limit the efficiency of CH 3 NH 3 SnBr 3 -based solar cells. The FTO/SnS 2 /CH 3 NH 3 SnBr 3 /HTL/Ni structure is investigated to prevent Sn oxidation, increase stability, and improve charge transport for improved performance. The analyzed structure is integrated with BiI 3 /SnS/WSe 2 /PTAA/CuS/CuI/C 6 TBTAPH 2 /CBTS layers as an HTL, resulting in a maximum V OC of 1.128 V, a J SC of 34.014 mA cm –2, a PCE of 33.70%, and an FF of 87.83% with the FTO/SnS 2 /CH 3 NH 3 SnBr 3 /CBTS/Ni structure. The performance matrix of the investigated best optimum solar cell was predicted by ML with an accuracy rate of roughly 83.75%. This study’s useful design and important discoveries could result in the creation of an inexpensive CH 3 NH 3 SnBr 3 thin-film solar cell.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".