Automated oxidation-state assignment for metal sites in coordination complexes in the Cambridge Structural Database
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
The Cambridge Structural Database (CSD) currently contains over 400 000 transition-metal-containing entries, however many entries still lack curated oxidation-state assignments. Surveying and editing the remaining entries would be far too resource- and time-intensive to be carried out manually. Here, a highly reliable automated workflow for oxidation-state assignment in transition-metal coordination complexes via CSD Python API (application programming interface) scripts is presented. The strengths and limitations of the bond-valence sum (BVS) method are discussed and the use of complementary methods for improved assignment confidence is explored. In total, four complementary techniques have been implemented in this study. The resulting workflow overcomes the limitations of the BVS approach, widening the applicability of an automated procedure to more CSD entries. Assignments are successful for 99% of the cases where a high consensus between different methodologies is observed. Out of a total number of 54 999 unique metal atoms in a test dataset, the procedure yielded the correct oxidation state in 47 072 (86%) of cases.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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