Dissipative Particle Dynamics with an Effective Pair Potential from Integral Equation Theory of Molecular Liquids
Bibliographic record
Abstract
We present a method of DPD simulation based on a coarse-grained effective pair potential obtained from the DRISM-KH molecular theory of solvation. The theory is first used to calculate the radial distribution functions of all-atom solute monomers in all-atom solvent and then to invert them into an effective pair potential between coarse-grained beads such that their fluid without solvent accounts for molecular specificities and solvation effects in the all-atom system. Bonded interactions are sampled in relatively short MD of the all-atom system and modeled with best multi-Gaussian fit. Replacing the heuristically defined conservative force potential in DPD, the coarse-grained effective pair potential is free from the artificial restrictions on potential range and shape and on equal volume of solute and solvent blobs inherent in standard DPD. The procedure is flexible in specifying coarse-grained mapping and enormously increases computational efficiency by eliminating solvent. The method is validated on polystyrene chains of various length in toluene at finite concentrations for room and polystyrene glass transition temperature. It yields the chain elastic properties and diffusion coefficient in good agreement with experiment and all-atom MD simulations. DPD with coarse-grained effective pair potential is capable of predicting both structural and dynamic properties of polymer solutions and soft matter with high accuracy and computational efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
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".