Potential of Mean Force Calculation Using Non-Uniform Sampling Windows for Optimal Computational Efficiency
Bibliographic record
Abstract
Free energy calculation in molecular simulation is an computationally expensive process. Umbrella sampling (US) is a go-to method for obtaining the potential of mean force (PMF) along a reaction coordinate. Its computational cost increases drastically as the molecular system gets more complex. For many polymeric and biomolecular systems, adequately sampling all configurational degrees of freedom is computationally prohibitive. Using the adsorption of a short-chain methylcellulose on a cellulose crystalline surface as the test case, we show that the sampling time required for reliable results is much higher than typical choices made in the literature. The accuracy of the PMF profile is strongly affected by sampling inadequacy in a few regions along the reaction coordinate. We propose to use non-uniform windows and sampling parameters to enhance the sampling in difficult regions. Sampling windows that vary with the local PMF steepness are allocated with a new algorithm. Parameters in this algorithm are optimized for best sampling efficiency. We demonstrate that significantly less computer time would be required to achieve the same sampling accuracy if computational resources are optimally distributed along the reaction coordinate.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".