Quantitative matching of crystal structures to experimental powder diffractograms
Bibliographic record
Abstract
Identifying whether two experimental crystal structures determined under different experimental conditions correspond to the same polymorph is a challenging problem in crystallography, with practical (and even legal) implications. We recently developed a new quantitative metric for comparison of powder X-ray diffractograms (PXRD), termed the variable-cell powder difference (VC-PWDF) method. VC-PWDF substantially improves the agreement with COMPACK compared to other PXRD-based comparison tools and is recommended to be used in conjunction with COMPACK to improve reliability of structure comparison. We further extended VC-PWDF to allow direct comparison of both experimental and in-silico-generated crystal structures to collected powder diffractograms. The resulting VC-xPWDF method correctly identifies the most similar crystal structure to both moderate and “low” quality experimental powder diffractograms for a set of 7 representative organic compounds, as well as the prolific polymorph former, ROY. This approach should allow for rapid identification of new polymorphs from solid-form screening studies by matching to a set of candidates resulting from crystal structure prediction, without requiring single-crystal analysis.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".