Automated oxidation-state assignment for metal sites in coordination complexes in the Cambridge Structural Database
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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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".