Comparison of molecular dynamics and moment based methods as tools in the computation of time dependent correlation functions
Bibliographic record
Abstract
Methods, such as the continued fractions that are based on the exact moments (Taylor coefficients) provide powerful analytic techniques for calculating approximate dynamic correlation functions of physical properties. Owing to the practical difficulties associated with obtaining higher order exact moments these methods have been largely eclipsed by molecular dynamics. In this paper we develop the formalism for extracting trajectory moments from a molecular dynamics trajectory and compare the performance of the two methods. We begin by using the classical phase space analogs of quantum wave functions to obtain matrix representations (transition matrices) of the Lie group of time displacement operators. The group elements for small time displacements, which correspond to a step of the trajectory, are approximated by using Trotter's theorem. The method is applied to the linear harmonic oscillator, the Morse oscillator and the Lennard-Jones potential and the resulting moments compared to those obtained by the Taylor expansion of a truncated continued fraction. We find that in the case of the harmonic oscillator the results from the continued fraction are much better than those from molecular dynamics. In the latter two cases, while the truncated fraction performs better than molecular dynamics both methods produce poor quality higher order moments. However, we are able to show the criterion whereby the overall error introduced by the truncated continued fraction is smaller than that introduced by molecular dynamics. From this work we conclude that the moment based methods produce good results, they are analytic in nature rather than numerical and should not be rejected but can be used to complement molecular dynamics.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".