Hybrid particle–field molecular dynamics simulation for polyelectrolyte systems
Bibliographic record
Abstract
To achieve simulations on large spatial and temporal scales with high molecular chemical specificity, a hybrid particle-field method was proposed recently. This method is developed by combining molecular dynamics and self-consistent field theory (MD-SCF). The MD-SCF method has been validated by successfully predicting the experimentally observable properties of several systems. Here we propose an efficient scheme for the inclusion of electrostatic interactions in the MD-SCF framework. In this scheme, charged molecules are interacting with the external fields that are self-consistently determined from the charge densities. This method is validated by comparing the structural properties of polyelectrolytes in solution obtained from the MD-SCF and particle-based simulations. Moreover, taking PMMA-b-PEO and LiCF3SO3 as examples, the enhancement of immiscibility between the ion-dissolving block and the inert block by doping lithium salts into the copolymer is examined by using the MD-SCF method. By employing GPU-acceleration, the high performance of the MD-SCF method with explicit treatment of electrostatics facilitates the simulation study of many problems involving polyelectrolytes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".