Coarse-grained simulation of water: A comparative study and overview
Bibliographic record
Abstract
In spite of the tremendous increase in computational power over the last few decades, the problem of simulating atomistic systems containing large amounts of water molecules over longer lengths and time scales still remains. In this respect, the coarse-grained (CG) force field reduces the computational cost and, therefore, allows simulations of larger systems for longer times. However, the specific scope of the different CG water models is more limited compared to their atomistic counterparts. In this context, we conducted a comparative study on the molecular physical structure, thermodynamic, and dynamic properties of bulk water systems using six distinct CG water models and all-atom (AA) simulations. The six CG simulation procedures involved modeling with three variants of the water model coming from the MARTINI force field, one from the SPICA force field, and the two Iterative Boltzmann Inversion (IBI) derived potentials from the AA simulations. The AA simulations have been performed using the SPC/E and TIP4P force fields. The IBI models, namely SPC/E-IBI and TIP4P-IBI, depict the structural features in close agreement with the atomistic samples. The explicit number of water molecules in the first coordination shell for the three MARTINI models and the SPICA force field is in excellent agreement with the SPC/E and TIP4P values. The ensuing simulated densities for the various water models align significantly with the literature data, indicating the reliability of our approach. The SPC/E and SPICA models stand out in predicting the enthalpy of vaporization among the all-atom and CG force fields, respectively. The two all-atom models and their IBI equivalents are better at representing the isobaric specific heat capacity compared to the other models. The isothermal compressibility is reproduced comprehensively by the SPC/E force field followed by TIP4P, while SPICA is the better choice within the CG models. With respect to the dynamics of the system, the diffusion coefficient of the SPICA force field is in perfect agreement with the experimental data, even better than the atomistic samples. The overall scores of the different models, indicative of their relative performances compared to the other models, have also been computed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".