ChemOS 2.0: an orchestration architecture for chemical self-driving laboratories
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Self-driving laboratories (SDLs), which combine automated experimental hardware with computational experiment planning, have emerged as powerful tools for accelerating materials discovery. The intrinsic complexity created by their multitude of components requires an effective orchestration platform to ensure the correct operation of diverse experimental setups. Existing orchestration frameworks, however, are either tailored to specific setups or have not been implemented for real-world synthesis. To address these issues, we introduce ChemOS 2.0, an orchestration architecture that efficiently coordinates communication, data exchange, and instruction management among modular laboratory components. By treating the laboratory as an "operating system" ChemOS 2.0 combines ab-initio calculations, experimental orchestration and statistical algorithms to guide closed-loop operations. To demonstrate its capabilities, we showcase ChemOS 2.0 in a case study focused on discovering organic laser molecules. The results confirm the ChemOS 2.0's prowess in accelerating materials research and demonstrate its potential as a valuable design for future SDL platforms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it