Universal Chemical Programming Language for Robotic Synthesis Reproducibility
Bibliographic record
Abstract
Abstract The amount of chemical synthesis literature is growing quickly, but it still takes a long time to share and evaluate new processes because of cultural and practical barriers. Herein, we present an approach that uses a universal chemical programming language (χDL) to encode and execute synthesis procedures for a variety of chemical reactions including reductive amination, ring formation, esterification, carbon-carbon bond formation, and amide coupling on different hardware and in different laboratories. With around fifty lines of code per reaction, our approach uses abstraction to efficiently compress chemical protocols. Our different robotic platforms consistently produce the expected synthesis with yields up to 90% per step, matching those achieved by an expert chemist. This allows for faster and more secure research workflows and can be used to increase the throughput of a process by number-up instead of scale-up. To achieve that we use Chemputer-type platforms at the University of Glasgow and the University of British Columbia, Vancouver as well as Opentrons- and multi-axis cobotic robots to distribute and reproduce experimental results. In total, protocols for 7 complex molecules were validated and disseminated to be reproduced in two international laboratories and on three independent robots.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".