Optimizing the Synthesis Parameters of Double Perovskites with Machine Learning Using a Multioutput Regression Model
Bibliographic record
Abstract
Double perovskites are attractive candidates for addressing the main challenges associated with efficient single perovskites, such as low stability and the presence of heavy metals in their composition. However, the double perovskite structure is complex and presents unique synthesis challenges. Furthermore, fine-tuning the synthesis parameters to obtain precise control of the nanoparticle size is necessary. To tackle these issues, we combined machine learning with a multioutput model, allowing us to simultaneously generate multiple outcomes within a single regression while considering interactions among all targets in a complex reaction. As a result, we built a specific data set with relevant parameters for the synthesis of double perovskite, using the hot injection method considering lead-free double and single perovskites. We then developed three different multioutput machine learning models based on decision trees, random forests, and neural networks. These models were trained to predict optimal synthesis conditions for double perovskites, including reagent amounts, time of reaction, and bandgap. The selection of the best model was based on MAE, RMSE, and R 2 metrics. We utilize this model to predict the synthesis condition of a double perovskite, which we subsequently synthesized, specifically Cs 2 AgInCl 6, hence validating our model with experimental results. Our approach enables us to achieve accurate predictions and gain a deeper understanding of the intricate relationships between synthesis parameters and material properties.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".