Molecular Modeling of Biomolecules and Solutions in Nanoporous Materials
Bibliographic record
Abstract
As one goes down the length scale to nanoworld, the properties of objects and phenomena swerve from those described by the conventional, macroscopic laws governing the behavior of continuous media and materials. The functional features of nanostructures manifest on length scale from one to hundreds nanometers and time scale up to microseconds and more, but all stem from microscopic properties of the atoms and chemical groups they are built of. Explicit molecular modeling of such nanosystems involving millions of molecules is by far not feasible with ab initio methods and molecular simulations, and requires multiple-scale approaches. Statistical-mechanical theory of molecular liquids and other disordered systems successfully describes the molecular structure and thermodynamics of nanosystems, with proper account of their chemical functionalities.¹-⁴ It operates with spatial/temporal distributions of species averaged over the statistical ensemble rather than with trajectories of individual molecules. This coarse-graining, however, keeps the short-range detail of the solvation structure of chemical specificities, such as the hydrophobic effects, hydrogen bonding, and other association effects. Below discussed are two illustrative examples, self-assembly of organic nanotubes in electrolyte solution and electrochemical devices with nanoporous electrodes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".