Computer Simulation Techniques for Modelling Statics and Dynamics of Nanoscale Structures
Bibliographic record
Abstract
This chapter describes computer simulation techniques that are used to model the statics and dynamics of nanoscale structures and their self-organized assemblies via their physical interactions. We describe some models which cannot be enabled without employing computer simulation but do not explicitly address models such as self-consistent field approaches or DLVO theory. The chapter is divided into four sections: introduction and background, atomic scale molecular dynamics, coarse-grained modelling and stochastic processes, and fluid flow. It is introduced via brief descriptions of protein folding and crystalline microscale structures in edible oils. A brief background to important aspects of statistical mechanics is followed by a description of atomic scale molecular dynamics. The spatial scale is then expanded and coarse-graining of atomic interactions is described. This leads into nanoscale systems and stochastic processes, and we describe the various applications of Monte Carlo techniques. The fourth section deals with fluid flow and we describe dissipative particle dynamics and, to a lesser extent, lattice-Boltzmann theory. In all sections we give steps to follow (recipes) in using these techniques. In addition, we give one or two examples of modelling and how computer simulation was used. Although our choices of methods and examples reflect our principal interests, we are not pushing for the use of one technique rather than another. We describe techniques which either continue to play fundamental roles in computer simulation of soft matter and fluids or are newer developments which have shown increased use in the last decade.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".