Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization
Bibliographic record
Abstract
Machine learning algorithms can enhance the design and experimental processing of solar cells, resulting in increased conversion efficiency. In this study, we introduce a novel machine learning-based methodology for optimizing the Pareto front of four-terminal (4T) perovskite-copper indium selenide (CIS) tandem solar cells (TSCs). By training a neural network using the Bayesian regularization-backpropagation algorithm via Hammersley sampling, we achieve high prediction accuracy when testing with unseen data through random sampling. This surrogate model not only reduces computational costs but also potentially enhances device performance, increasing from 29.4% to 30.4% while simultaneously reducing material costs for fabrication by 50%. Comparing experimentally fabricated cells with the predicted optimal cells, the latter show a thinner front contact electrode, charge-carrier transport layer, and back contact electrode. Highly efficient perovskite cells identified from the Pareto front have a perovskite layer thickness ranging from 420 to 580 nm. Further analysis reveals the front contact electrode needs to be thin, while the back contact electrode can have a thickness ranging from 100 to 145 nm and still achieve high efficiency. The charge-carrier transport layers play a crucial role in minimizing interface recombination and ensuring unidirectional current flow. The optimal design space suggests thinner electron and hole transport layer thicknesses of 7 nm, down from 23 to 10 nm, respectively. It indicates a balanced charge-carrier extraction is crucial for an optimized perovskite cell. Overall, the presented methodology and optimized design parameters have the potential to enhance the performance of 4T perovskite/CIS TSC while reducing material fabrication costs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".