Tradeoffs and Compatibilities of Chemical Properties in CpHqFrOs System
Bibliographic record
Abstract
<p>To comply with the Kigali amendment to the Montreal Protocol in 2016, it becomes an urgent matter to develop new refrigerants with low global warming potential with simultaneously meeting conventional requirements of cooling performance, safety, and non-destructive to the ozone layer. Because each requirement links to different chemical property, proper control of various chemical properties is necessary to achieve the requirements. However, it seems to be extremely difficult to satisfy all the requirements simultaneously due to the tradeoffs among the properties. For this reason, we need to correctly recognize how these chemical properties behave when the composition of molecule is changed. Here we have done in silico screening that combines quantum chemical calculation, machine learning, and database search, where 10,163 molecules were exhaustively investigated within the properly imposed constraints, then we have found several candidates for new refrigerants. It should be noted that the synthesis of refrigerants is more difficult than that of ordinary organic molecules because glassware cannot be used for the synthesis of fluorine-containing molecules that most refrigerants apply. This makes in silico screening a more useful approach in the design of refrigerants. </p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 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".