Incorporation of Ligand Charge and Metal Oxidation State Considerations into the Computational Solvent Removal and Activation of Experimental Crystal Structures Preceding Molecular Simulation
Bibliographic record
Abstract
capture. Preprocessing techniques have been developed to render experimental crystal structures suitable for molecular simulations by mimicking experimental activation protocols, particularly residual solvent removal. Current accounts examining these preprocessed materials databases indicate the presence of assorted structural errors introduced by solvent removal and preprocessing, including improper elimination of charge-balancing ions and ligands. Here, we address the need for a reliable experimental crystal structure preprocessing protocol by introducing a novel solvent removal method, which we call SAMOSA, that is informed by systematic ligand charge and metal oxidation state calculations. A robust set of solvent removal criteria is outlined, which identifies solvent molecules and counterions without predefined molecule lists or significant reliance on experimental chemical information. Validation results against popular metal-organic framework (MOF) databases suggest that this method observes significant performance improvements regarding the retention of charged ligands and recognition of charged frameworks. SAMOSA enhances structure fidelity with respect to the original material as-synthesized, thereby representing a powerful tool in computational materials database curation and preprocessing for molecular simulation. The source code is accessible at https://github.com/uowoolab/SAMOSA.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".