A self-driving laboratory optimizes a scalable process for making functional coatings
Bibliographic record
Abstract
Functional coatings are used in a wide range of high surface-area technologies, such as low-E windows and photovoltaics. Solution-based coatings are typically less expensive to produce than vacuum-based coatings; however, it is generally more difficult to produce high-quality coatings using solution-based methods due to lower control over the physical and chemical processes involved. Here, we show how a self-driving laboratory can be used to optimize spray coating parameters. For this demonstration, we optimized the combustion synthesis of spray-cast conductive palladium (Pd) films. The closed-loop optimization yielded films with conductivities of >4 MS/m, which compares favorably with the conductivities of 2–6 MS/m reported for thin Pd films obtained by vacuum-based sputtering processes. The champion coating conditions were scaled up to an 8× larger area using the same spray-coating apparatus while preserving coating quality and conductivity. This work shows how self-driving laboratories can optimize solution-based coatings at scale.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".