Molecular simulations of self-assembly processes in metal-organic frameworks: Model dependence
Bibliographic record
Abstract
Molecular simulation is a powerful tool for investigating microscopic behavior in various chemical systems, where the use of suitable models is critical to successfully reproduce the structural and dynamic properties of the real systems of interest. In this context, molecular dynamics simulation studies of self-assembly processes in metal-organic frameworks (MOFs), a well-known class of porous materials with interesting chemical and physical properties, are relatively challenging, where a reasonably accurate representation of metal-ligand interactions is anticipated to play an important role. In the current study, we both investigate the performance of some existing models and introduce and test new models to help explore the self-assembly in an archetypal Zn-carboxylate MOF system. To this end, the behavior of six different Zn-ion models, three solvent models, and two ligand models was examined and validated against key experimental structural parameters. To explore longer time scale ordering events during MOF self-assembly via explicit solvent simulations, it is necessary to identify a suitable combination of simplified model components representing metal ions, organic ligands, and solvent molecules. It was observed that an extended cationic dummy atom (ECDA) Zn-ion model combined with an all-atom carboxylate ligand model and a simple dipolar solvent model can reproduce characteristic experimental structures for the archetypal MOF system. The successful use of these models in extensive sets of molecular simulations, which provide key insights into the self-assembly mechanism of this archetypal MOF system occurring during the early stages of this process, has been very recently reported.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".