Driving Thermoelectric Optimization in AgSbTe<sub>2</sub> via Design of Experiments and Machine Learning
Bibliographic record
Abstract
Systemic optimization of thermoelectric materials is arduous due to their conflicting electrical and thermal properties. A strategy based on Design of Experiments and machine learning is developed to optimize the thermoelectric efficiency of AgSb 1+ x Te 2+ y, an established thermoelectric. From eight experiments, high thermoelectric performance in AgSb 1.021 Te 2.04 is revealed with a peak and average thermoelectric figure of merit of 1.61 ± 0.24 at 600 K and 1.18 ± 0.18 (300–623 K), respectively, which is >30% higher than the best literature values for AgSb 1+ x Te 2+ y . Ag deficiency and suppression of secondary phases in AgSb 1.021 Te 2.04 improve the electrical properties and reduce the thermal conductivity (∼0.4 W m –1 K –1 ). Our strategy is implemented into an open-source graphical user interface, and it can be used to optimize the methodologies, properties, and processes across different scientific fields.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".