Generalised canonical–isokinetic ensemble: speeding up multiscale molecular dynamics and coupling with 3D molecular theory of solvation
Bibliographic record
Abstract
We have proposed a new canonical–isokinetic ensemble for efficient sampling of conformational space in molecular dynamics (MD) simulations which leads to the optimised isokinetic Nosé–Hoover (OIN) chain algorithm for atomic and molecular systems. We applied OIN to multiple time step (MTS) MD simulations of the rigid and flexible models of water to demonstrate its advantage over the standard canonical, isokinetic and canonical–isokinetic ensembles. With the stabilising effect of OIN thermostatting in MTS-MD, gigantic outer time steps up to picoseconds can be employed to accurately calculate equilibrium and conformational properties. Furthermore, we developed the atomic version of OIN for MTS-MD of a biomolecule in a solvent potential of mean force obtained at sequential MD steps by using the molecular theory of solvation, aka three-dimensional reference interaction site model with the Kovalenko–Hirata closure (3D-RISM-KH). The solvation forces are obtained analytically by converging the 3D-RISM-KH integral equations once per several OIN outer time steps, and are calculated in between by using solvation force-coordinate extrapolation (SFCE) in the subspace of previous successive solutions to 3D-RISM-KH. For illustration, we applied the multiscale OIN/SFCE/3D-RISM-KH algorithm to a fully flexible model of alanine dipeptide in aqueous solution. Although the computational rate of solvent sampling in OIN/SFCE/3D-RISM-KH is already 20 times faster than standard MD with explicit solvent, further substantial acceleration of sampling stems from making solute evolution steps in a statistically averaged potential of mean force obtained from 3D-RISM-KH. The latter efficiently samples the phase space for essential events with rare statistics such as exchange and localisation of solvent and ligand molecules in confined spaces, pockets and at binding sites of the solute macromolecule, as distinct from MD with explicit solvent which requires enormous computational time and number of steps in such cases.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".